Understanding racial differences in financial hardship among older adults surviving cancer.

BACKGROUND: Despite Medicare coverage, financial hardship is a prevalent issue among those diagnosed with cancer at age 65 years and older, particularly among those belonging to a racial or ethnic minority group. Sociodemographic, clinical, and area-level factors may mediate this relationship; however, no studies have assessed the extent to which these factors contribute to the racial/ethnic disparities in financial hardship. METHODS: Surveys assessing financial hardship were completed by 721 White (84%) or Black (16%) patients (aged 65 years and older) who were diagnosed with breast (34%), prostate (27%), lung (17%), or colorectal (14%) cancer or lymphoma (9%) at the University of Alabama at Birmingham between 2000 and 2019. Financial hardship included material, psychological, and behavioral domains. Nonlinear Blinder-Oaxaca effect decomposition methods were used to evaluate the extent to which individual and area-level factors contribute to racial disparities in financial hardship. RESULTS: Black patients reported lower income (65% vs. 34% earning <$50,000) and greater scores on the Area Deprivation Index (median, 93.0 vs. 55.0). Black patients reported significantly higher rates of overall (39% vs. 18%), material (29% vs. 11%), and psychological (27% vs. 11%) hardship compared with White patients. Overall, the observed characteristics explained 51% of racial differences in financial hardship among cancer survivors, primarily because of differences in income (23%) and area deprivation (11%). CONCLUSIONS: The current results identify primary contributors to racial disparities in financial hardship among older cancer survivors, which can be used to develop targeted interventions and allocate resources to those at greatest risk for financial hardship.

Nearly 20 Years Since the Federal Ban: Can State-Level Assault Weapon Prohibitions Fill the Void? Comparative Analysis of Case Fatality and Assault Weapon Recovery in States With and Without an Assault Weapon Ban.

INTRODUCTION: Few states established assault weapon bans (AWBs) after the federal AWB expired. The effectiveness of state AWBs as well as neighboring state legislation, in reducing the local prevalence of assault weapons (AWs) or in reducing overall shooting lethality is unknown. METHODS: We queried the Gun Violence Archive (2014-2021) to identify US firearm injuries and fatalities. Shooting case fatality rates were compared among states with and without AWBs, as reported in the State Firearm Laws Database. Data on recovered firearms was obtained from the ATF Firearms Trace Database and used to estimate weapon prevalence. Recovered firearms were classified as AWs based on caliber (7.62 mm, 5.56 mm, 0.223 cal). We performed spatially weighted linear regression models, with fixed effects for state and year to assess the association between geographically clustered state legislation and firearm outcomes. RESULTS: From 2014 to 2021, the US shooting victim case fatality rate was 8.06% and did not differ among states with and without AWBs. The proportion of AWs to total firearms was 5.0% in states without an AWB and 6.0% in states with an AWB (mean difference [95% CI] = -0.8% [-1.6% to -0.2%], P = 0.03). Most recovered firearms in AWB states originated from non-AWB states. On adjusted models, there was no association between state-level AWB and firearm case fatality; however, adjacency to states with an AWB was associated with lower case fatality (P < 0.001). Clustered AWB states with shared borders had lower AW prevalence and fatality rates than the rest of the US. CONCLUSIONS: Isolated state AWBs are not inversely associated with shooting case fatality rates nor the prevalence of AWs, but AWBs among multiple neighboring states may be associated with both outcomes.

Psychosocial burden after the death of a child from cancer: Results of a bereaved parent survey.

Parents experience lasting psychological distress after a child's death from cancer. Limited evidence exists regarding difficult life events, duration of psychosocial impacts, and associated risk factors among bereaved parents. Alex's Lemonade Stand Foundation surveyed self-selected, bereaved parents regarding difficult life events and psychosocial wellbeing (life satisfaction, unanswered questions, and missing the care team) through a public, cross-sectional survey. 176 bereaved parents (89% mothers) participated a median of 7 y after their child's death. The most difficult events were family vacations (80%), their child's birthday (80%), and anniversary of their child's death (76%). Only the latter did not improve with time. Greater life satisfaction was associated with male sex (ARR = 1.2, 95% CI:1.1-1.4) and being married/partnered (ARR = 1.2, 95% CI = 1.0-1.3). Having unanswered questions and missing the child's team were associated with annual income <$50,000 (ARR = 1.2, 95% CI:1.1-1.2; ARR = 1.2, 95% CI:1.0-1.3, respectively). Pediatric oncology programs need robust bereavement programs that include prolonged contact with families.

Financial hardships and psychosocial outcomes among parents of children who die of cancer.

BACKGROUND: Caregivers experience financial hardship during a child's cancer treatment and after their child's death. These bereaved caregivers also experience negative psychosocial outcomes following the death of a child, but the relationship between financial hardship and negative psychosocial outcomes is poorly understood in this population. METHODS: We surveyed self-selected bereaved caregivers as part of a publicly posted survey through Alex's Lemonade Stand Foundation in order to explore family experiences after losing a child to cancer. The survey contained questions regarding parent psychosocial and financial outcomes following their child's death. RESULTS: One-hundred seventy-six caregivers completed the survey a median of 7 years after their child's death. The majority were female (91%), non-Hispanic White (97%), and married or living with a domestic partner (76%). Overall, 31% of caregivers reported that their child's death significantly impacted the financial well-being of their family, 23% experienced a decrease in income following their child's death, and 14% were still paying medical expenses. Financial hardship that the caregiver attributed to the child's death was associated with feeling lonely and isolated (adjusted relative risk [ARR] = 1.7, 95% CI: 1.1-2.7) and living day to day (ARR = 1.8, 95% CI: 1.3-2.5), even after adjustment for household income and time since child's death. CONCLUSIONS: Caregivers experience multiple financial hardships following the death of a child to cancer, which endure for years after the child's death. These hardships are associated with negative psychosocial outcomes, demonstrating the need for both financial and psychosocial interventions for caregivers following the death of a child to cancer.

Transmission Dynamics of Large Coronavirus Disease Outbreak in Homeless Shelter, Chicago, Illinois, USA, 2020.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has the potential for rapid transmission in congregate settings. We describe the multidisciplinary response to an outbreak of coronavirus disease (COVID-19) in a large homeless shelter in Chicago, Illinois, USA. The response to the outbreak included 4 rounds of mass PCR testing of all staff and residents and subsequent isolation of persons who tested positive for SARS-CoV-2. We further describe the dynamics of the shelter outbreak by fitting a modified susceptible-exposed-infectious-recovered compartmental model incorporating the widespread SARS-CoV-2 testing and isolation measures implemented in this shelter. Our model demonstrates that rapid transmission of COVID-19 in the shelter occurred before the outbreak was detected; rates of transmission declined after widespread testing and isolation measures were put in place. Overall, we demonstrate the feasibility of mass PCR testing and isolation in congregate settings and suggest the necessity of prompt response to suspected COVID-19 outbreaks in homeless shelters.

Venous-thromboembolism and associated health care utilization in elderly patients with diffuse large B cell lymphoma.

BACKGROUND: Patients with lymphoma have an increased risk of venous thromboembolism (VTE). The authors examined the risk of VTE and subsequent health care utilization in elderly patients with diffuse large B cell lymphoma (DLBCL). METHODS: A total of 5537 DLBCL patients ≥66 years old enrolled in Medicare from the Surveillance, Epidemiology, and End Results registry and a noncancer control group of Medicare beneficiaries (n = 5537) were identified. Cumulative incidence function to examine the risk of VTE 12 months after DLBCL diagnosis was used. Fine and Gray method was used to examine the risk factors associated with VTE risk in multivariable models. Total number of hospitalizations, outpatient visits, and Medicare spending were compared in DLBCL patients with and without VTE. RESULTS: VTE was diagnosed in 8.3% DLBCL patients and 1.5% controls, yielding an 8.6-fold higher risk of VTE in DLBCL in adjusted analysis (95% confidence interval [CI], 6.62-11.20; P < .001). Multivariable regression analysis showed that precancer VTE history was associated with an increased risk of developing VTE after a DLBCL diagnosis (hazard ratio [HR], 5.39; 95% CI, 4.39-6.63), and Asian individuals were associated with a lower risk (HR, 0.54; 95% CI, 0.29-1.00). Patients newly diagnosed with VTE after lymphoma had a 1.7-fold higher rate of hospitalization and a 1.2-fold higher rate of outpatient visits compared to those without, resulting in excess Medicare spending of $22,208 in the first year after DLBCL diagnosis. CONCLUSIONS: Elderly patients with DLBCL have an elevated risk of VTE resulting in excess health care utilization. VTE history before DLBCL was associated with increased risk of post-DLBCL VTE, and Asian individuals were associated with a lower risk of VTE.

Early palliative care is associated with less intense care in children dying with cancer in Alabama: A retrospective, single-site study.

BACKGROUND: Regional studies show that children with cancer receive medically intense end-of-life (EOL) care, but EOL care patterns, including palliative care utilization in Alabama, remain unknown. METHODS: This was a retrospective study of 233 children (0-19 years) who received cancer-directed therapy at Children's of Alabama and died from 2010 through 2019. Rates and disparities in palliative care utilization and the association between palliative care and intense EOL care, death location, and hospice were examined. RESULTS: The median death age was 11 years; 62% were non-Hispanic White. Forty-one percent had a non-central nervous system (CNS) solid tumor. Fifty-eight percent received palliative care, and 36% received early palliative care (≥30 days before death). Children without relapsed/refractory disease were less likely to receive palliative care than those who had relapsed/refractory disease (adjusted odds ratio [aOR], 0.2; 95% confidence interval [CI], 0.1-0.7). Children with CNS tumors and hematologic malignancies were less likely to have early palliative care (aOR for CNS tumors, 0.4; 95% CI, 0.2-0.7; aOR for hematologic malignancies, 0.3; 95% CI, 0.2-0.7) than children with non-CNS solid tumors. Late palliative care (vs none) was associated with more medically intense care (aOR, 3.3; 95% CI, 1.4-7.8) and hospital death (aOR, 4.8; 95% CI, 1.9-11.6). Early palliative care (vs none) was associated with more hospice enrollment (aOR, 3.4; 95% CI, 1.5-7.6) but not medically intense care (aOR, 1.3; 95% CI, 0.6-2.9) or hospital death (aOR, 1.8; 95% CI, 0.8-3.7). CONCLUSIONS: Fifty-eight percent of children dying of cancer in Alabama receive palliative care, but EOL care varies with the receipt and timing (early vs late) of palliative care. Whether this variation reflects differences in child and family preferences or systemic factors (eg, hospice access) remains unknown.

Location of death and hospice use in children with cancer varies by type of health insurance.

BACKGROUND: Disparities in end-of-life (EOL) care for children with cancer remain understudied. We addressed this gap by examining patterns of EOL care, with a focus on location of death and hospice utilization. METHODS: We used MarketScan - a nationally representative dataset with Medicaid and commercial claims to conduct a retrospective observational study of children with cancer who died between 2011 and 2017 at age ≤21 years. We examined rates of (a) home death, (b) hospice utilization, (c) and medically intense interventions in the last 30 days (e.g., intubation). RESULTS: Of the 1492 children in the cohort, 44% had Medicaid and 56% commercial insurance; 71% carried a solid tumor diagnosis, and 37% were between the ages of 15 and 21 years at the time of death. Forty percent died at home; children with Medicaid were less likely to die at home (relative risk [RR] = 0.82, 95% confidence interval [CI]: 0.73-0.92; reference: commercial). Forty-five percent enrolled in hospice, for a median of 2 days. Hospice enrollment rates did not vary with insurance. However, children with Medicaid spent less time enrolled (incidence rate ratio [IRR] = 0.22, 95% CI: 0.17-0.27). Among children with Medicaid, Black children were less likely to die at home (RR = 0.69, 95% CI: 0.52-0.92) and enroll on hospice (RR = 0.71, 95% CI: 0.55-0.91) than non-Hispanic White children. Medically intense interventions did not vary with insurance or race. CONCLUSION: Only 40% of children with cancer die at home, and the duration of hospice enrollment is short. EOL care varies significantly with insurance. It is imperative that we determine if these patterns and disparities represent EOL preferences, provider biases, or differences in quality or availability of hospice.

Don't Ditch the Laptop Just Yet: A Direct Replication of Mueller and Oppenheimer's (2014) Study 1 Plus Mini Meta-Analyses Across Similar Studies.

In this direct replication of Mueller and Oppenheimer's (2014) Study 1, participants watched a lecture while taking notes with a laptop (n = 74) or longhand (n = 68). After a brief distraction and without the opportunity to study, they took a quiz. As in the original study, laptop participants took notes containing more words spoken verbatim by the lecturer and more words overall than did longhand participants. However, laptop participants did not perform better than longhand participants on the quiz. Exploratory meta-analyses of eight similar studies echoed this pattern. In addition, in both the original study and our replication, higher word count was associated with better quiz performance, and higher verbatim overlap was associated with worse quiz performance, but the latter finding was not robust in our replication. Overall, results do not support the idea that longhand note taking improves immediate learning via better encoding of information.

Urban-rural differences in cancer mortality: Operationalizing rurality.

OBJECTIVE: To assess urban-rural differences in cancer mortality across definitions of rurality as (1) established binary cut-points, (2) data-driven binary cut-points, and (3) continuous. METHODS: We used Surveillance, Epidemiology, and End Results (SEER) data between 2000 and 2016 to identify incident adult screening-related cancers. Analyses were based on one testing and four validation cohorts (all n = 26,587). Urban-rural status was defined by Rural-Urban Continuum Codes, National Center for Health Statistics codes, and the Index of Relative Rurality. Each was modeled using established binary cut-points, data-driven cut-points, and as continuous. The primary outcome was 5-year cancer-specific mortality. RESULTS: Compared to established cut-points, data-driven cut-points classified more patients as rural, resulted in larger White populations in rural areas, and yielded 7%-14% lower estimates of urban-rural differences in cancer mortality. Further, hazard of cancer mortality increased 4%-67% with continuous rurality measures, revealing important between-unit differences. CONCLUSIONS: Different cut-points introduce variation in urban-rural differences in mortality across definitions, whereas using urban-rural measures as continuous allows rurality to be conceptualized as a continuum, rather than a simple aggregation. POLICY IMPLICATIONS: Findings provide alternative cut-points for multiple measures of rurality and support the consideration of utilizing continuous measures of rurality in order to guide future research and policymakers.

Estimating the Impact of Rurality in Disparities in Cancer Mortality.

PURPOSE: Estimation of the independent effect of rurality on cancer mortality requires causal inference methodology and consideration of area-level socioeconomic status and rural designations. METHODS: Using SEER data, we identified key incident cancers diagnosed between 2000 and 2016 at age ≥20 years (N = 3,788,273), examining a 20% random sample (n = 757,655). Standardized competing risk and survival models estimated the association between rural residence, defined by Rural-Urban Continuum Codes, and cancer-specific and all-cause mortality, controlling for age at cancer diagnosis, sex, race/ethnicity, year of diagnosis, and Area Deprivation Index (ADI). We estimated the attributable fraction (AF) of rurality and high ADI (ADI > median) to the probability of mortality. Finally, we examined county measurement issues contributing to mortality rates discordant from hypothesized rates. RESULTS: The 5-year standardized failure probability for cancer mortality for rural patients was 33.9% versus 31.56% for urban. The AF for rural residence was 1.04% at year 1 (0.89% by year 5), the highest among local stage disease (Y1 2.1% to Y5 1.9%). The AF for high ADI was 3.33% in Y1 (2.87% in Y5), while the joint effect of rural residence and high ADI was 4.28% in Y1 (3.71% in Y5). Twenty-two percent of urban counties and 30% of rural were discordant. Among discordant urban counties, 30% were only considered urban because of adjacency to metro area. High ADI was associated with urban discordance and low ADI with rural discordance. CONCLUSION: Rural residence independently contributes to cancer mortality. The rural impact is the greatest among those with localized disease and in high deprivation areas. Rural-urban county designations may mask high-need urban counties, limiting eligibility to state and federal resources dedicated to rural areas.

Contribution of County Characteristics to Disparities in Rural Mortality After Cancer Diagnosis.

INTRODUCTION: Rural disparities in cancer outcomes have been widely evaluated, but limited evidence is available to describe what characteristics of rural environments contribute to the increased risk of poor outcomes. Therefore, this manuscript sought to assess the mediating effects of county characteristics on the relationship between urban/rural status and mortality among patients with cancer, characterize county profiles, and determine at-risk county profiles alongside rural settings. METHODS: Patients diagnosed with cancer between 2000 and 2016 were assessed using Surveillance, Epidemiology and End Results data linked to the 2010 Rural-Urban Commuting Codes and 2010 County Health Rankings. There were 757,655 patients representing 596 counties (of 3,143 in the U.S.) and 12 states. Mediation analyses, conducted in 2023, estimated the direct contribution of rurality to 5-year all-cause survival and the contribution of the rural effect indirectly through County Health Ranking domains. Latent class analysis and survival models identified county groupings and estimated the hazard of mortality associated with class membership. RESULTS: Rankings for premature death, clinical care, and physical environment resulted in rural patients having 17.9%-20.2% less survival time than urban patients. Of this, 4.1%-12.6% of the total excess risk was mediated by these characteristics. Patients living in rural and high-risk county classes saw higher all-cause mortality than those in urban lower-risk counties (hazard ratio=1.04, 95% CI=1.01, 1.08 and 1.07, 95% CI=1.03, 1.11). CONCLUSIONS: Counties with poorer health rankings had increased mortality risks regardless of rurality; however, the poor rankings, notably health behaviors and social and economic factors, elevated the risk for rural counties.

Bullet holes: A novel model to identify the most impactful gaps in the firearm homicide prevention laws of each state.

BACKGROUND: Firearm homicides (FH) are a major cause of mortality in the United States. Firearm law implementation is variable across states, and legislative gaps may represent opportunities for FH prevention. For each state, we sought to identify which firearm law category would have been most effective if implemented and how effective it would have been. METHODS: We conducted a retrospective cohort study examining the effects of firearm laws on FH rates in the 48 contiguous US states 2010 to 2019. Data were obtained from the CDC WONDER and FBI UCR databases, State Firearm Law Database, and US Census. Firearm laws were grouped into 14 categories. We assessed the association between the presence of each law category and FH rate as an incidence rate ratio (IRR) using a Poisson regression accounting for state population characteristics and laws of surrounding states. We estimated the IRR for each state that did not have a given law category present and determined which of these missing law categories would have been associated with the greatest reduction in FH rate. RESULTS: FH rates varied widely across states and increased from a mean of 3.2 (SD = 1.7) to 4.2 (SD = 2.9) FH per 100,000. All law categories were significantly associated with decreased FH rate ( p < 0.05), with IRR ranging from 0.25 to 0.85. The most effective missing law category differed between states but was most commonly child access prevention (34.09% of states), assault weapons and large-capacity magazines (15.91%), preemption (15.91%), and concealed carry permitting (13.64%). In total across 2010 to 2019, we estimated that 129,599 fewer FH would have occurred with enactment of the most effective missing law category in each state. CONCLUSION: Modeling firearm law prevention of FH with regard to state legislative and population characteristics can identify the highest impact missing law categories in each state. These results can be used to inform efforts to reduce FH. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.

Adolescents and young adults (AYA) with cancer: the clinical course of COVID-19 infections.

Adolescents and Young Adults (AYAs: 15-39 y) with cancer face unique vulnerabilities, yet remain under-represented on clinical trials, including adult registries of COVID-19 in cancer (AYAs: 8-12%). Thus, we leveraged the Pediatric Oncology COVID-19 Case Report (POCC) to examine the clinical course of COVID-19 among AYAs with cancer. POCC collects de-identified clinical and sociodemographic data regarding 0-39yo with cancer (AYAs = 37%) and COVID-19 from >100 institutions. Between 04/01/2020-11/28/2023, 191 older AYAs [22-39y] and 640 younger AYAs [15-21y] were captured. Older AYAs were less often hospitalized (p < .001), admitted to the intensive care unit (ICU, p = .02), and/or required respiratory support (p = .057). In multivariable analyses, older AYAs faced 80% lower odds of ICU admission but 2.3-times greater odds of changes to cancer-directed therapy. Unvaccinated patients had 5.4-times higher odds of ICU admission. Among AYAs with cancer, the COVID-19 course varies by age. These findings can inform pediatric/adult oncology teams surrounding COVID-19 management and prevention.

Defining rurality: an evaluation of rural definitions and the impact on survival estimates.

BACKGROUND: Patients with cancer living in rural areas have inferior cancer outcomes; however, studies examining this association use varying definitions of "rural," complicating comparisons and limiting the utility of the results for policy makers and future researchers. METHODS: Surveillance, Epidemiology, and End Results data (2000-2016) were used to assess risk of cancer mortality and mortality from any cause across 4 definitions of rurality: Urban Influence codes (UIC), National Center for Health Statistics (NCHS), Rural-Urban continuum codes (RUCC), and Index of Relative Rurality. Binary (urban vs rural) and ternary (urban, micropolitan, rural) definitions were evaluated. Multivariable parametric survival models estimated hazards of mortality overall and among 3 cancer groupings: screening related, obesity related, and tobacco related. Definition agreement was also assessed. RESULTS: Overall, 3 788 273 patients with an incident cancer representing 605 counties were identified. There was little discordance between binary definitions of rural vs urban and moderate agreement at the 3 levels. Adjusted models using binary definitions revealed 15% to 17% greater hazard of cancer mortality in rural compared with urban. At the 3 levels when comparing rural with metropolitan, RUCC and NCHS saw similarly increased hazard ratios; however, Index of Relative Rurality did not. Screening-related cancers saw the highest hazards of mortality and the largest divergence between definitions. Obesity-related and tobacco-related cancers saw similarly increased hazards of mortality at the binary and ternary levels. CONCLUSIONS: Hazard of death is similar across binary definitions; however, this differed when categorized as ternary or continuous, especially among screening-related cancers. Results suggest that study purpose should direct choice of definitions and categorization.

Clinical Features and Risk Factors Associated With Multisystem Inflammatory Syndrome in Children With Cancer and COVID-19.

Importance: Little is known about the risk of post-COVID-19 multisystem inflammatory syndrome in children (MIS-C) in the setting of childhood cancer. Objective: To evaluate factors associated with MIS-C and describe the clinical course of COVID-19 in the setting of MIS-C. Design, Setting, and Participants: Multisite observational cohort study of a registry representing more than 100 US pediatric oncology sites. All included patients were registered between April 1, 2020, and May 18, 2022. Sites submitted deidentified data surrounding sociodemographics, cancer diagnosis and treatment, and COVID-19 course (symptoms, maximum support required, outcome). Patients with MIS-C (n = 24) were compared with matched controls (n = 96). Children (<21 years) with cancer who developed COVID-19 while receiving cancer treatment or within 1 year of completing treatment were characterized based on their development of MIS-C. Exposures: (1) Clinical and sociodemographic characteristics of children with cancer and COVID-19; and (2) MIS-C. Main Outcomes and Measures: (1) Development of MIS-C among children with cancer and COVID-19; and (2) symptoms and disease severity associated with MIS-C. Results: Among 2035 children with cancer and COVID-19, 24 (1.2%) developed MIS-C. COVID-19 occurred at a median (IQR) age of 12.5 (5.5-17.1) years in those with MIS-C and 11 (6-16) years among matched controls (P = .86). The majority of children with MIS-C had a hematologic cancer (83.3% [n = 20]), were publicly insured (66.7% [n = 16]), and were Hispanic (54.2% [n = 13]). Half (n = 12) had 1 or more noncancer comorbidity. Those with comorbidities were more likely to develop MIS-C than those without (odds ratio [OR], 2.5 [95% CI, 1.1-5.7]). Among children with MIS-C, 100% (n = 24) were admitted to the hospital and 54.2% (n = 13) to the intensive care unit (ICU), while COVID-19 contributed to the death of 20.1% (n = 5); cancer therapy was changed in 62.5% (n = 15). Compared with matched controls, those with MIS-C had higher odds of symptoms classified as systemic (OR, 4.7 [95% CI, 1.4-15.8]) or gastrointestinal (OR, 5.0 [95% CI, 1.7-14.6]) along with higher odds of hospitalization (OR, 42.9 [95% CI, 7.1-258]), ICU admission (OR, 11.4 [95% CI, 3.6-36.4]), and changes to cancer therapy (OR, 24.9 [95% CI, 6.5-94.8]). Conclusions and Relevance: In this cohort study among children with cancer and COVID-19, those with MIS-C had a more severe clinical course than those without MIS-C. The risk of MIS-C and its severity are important to consider as clinicians monitor patients with COVID-19. These findings can inform their conversations with families regarding COVID-19 risks and the benefits of prevention strategies that are pharmacologic (vaccination) and nonpharmacologic (masking), as well as treatment (antivirals, monoclonal antibodies).

Stress-Induced Changes in Effective Connectivity During Regulation of the Emotional Response to Threat.

Background: Stress-related disruption of emotion regulation appears to involve the prefrontal cortex (PFC) and amygdala. However, the interactions between brain regions that mediate stress-induced changes in emotion regulation remain unclear. The present study builds upon prior work that assessed stress-induced changes in the neurobehavioral response to threat by investigating effective connectivity between these brain regions. Methods: Participants completed the Montreal Imaging Stress Task followed by a Pavlovian fear conditioning procedure during functional magnetic resonance imaging. Stress ratings and psychophysiological responses were used to assess stress reactivity. Effective connectivity during fear conditioning was identified using multivariate autoregressive modeling. Effective connectivity values were calculated during threat presentations that were either predictable (preceded by a warning cue) or unpredictable (no warning cue). Results: A neural hub within the dorsomedial PFC (dmPFC) showed greater effective connectivity to other PFC regions, inferior parietal lobule, insula, and amygdala during predictable than unpredictable threat. The dmPFC also showed greater connectivity to different dorsolateral PFC and amygdala regions during unpredictable than predictable threat. Stress ratings varied with connectivity differences from the dmPFC to the amygdala. Connectivity from dmPFC to amygdala was greater in general during unpredictable than predictable threat, however, this connectivity increased during predictable compared with unpredictable threat as stress reactivity increased. Conclusions: Our findings suggest that acute stress disrupts connectivity underlying top-down emotion regulation of the threat response. Furthermore, increased connectivity between the dmPFC and amygdala may play a critical role in stress-induced changes in the emotional response to threat. Impact statement The present study builds upon prior work that assessed stress-induced changes in the human neurobehavioral response to threat by demonstrating that increased top-down connectivity from the dorsomedial prefrontal cortex to the amygdala varies with individual differences in stress reactivity. These findings provide novel evidence in humans of stress-induced disruption of a specific top-down corticolimbic circuit during active emotion regulation processes, which may play a causal role in the long-term effects of chronic or excessive stress exposure.

Meaningful Change Thresholds for Patient-Reported Outcomes Measurement Information System (PROMIS) Fatigue and Pain Interference Scores in Patients With Rheumatoid Arthritis.

OBJECTIVE: We estimated meaningful change thresholds (MCTs) for Patient-Reported Outcomes Measurement Information System (PROMIS) Fatigue and Pain Interference in rheumatoid arthritis (RA). METHODS: The responsiveness of several patient-reported outcomes (PROs) was assessed among 521 patients with RA in the Arthritis, Rheumatism, and Aging Medical Information Systems (ARAMIS) cohort. PROMIS Fatigue (7-item) and Pain Interference (6-item) short form instruments were administered at baseline, 6 months, and 12 months. Self-reported retrospective changes over the previous 6 months (a lot better/worse, a little better/worse, stayed the same) were obtained at 6 and 12 months' follow-up. We estimated MCTs using the mean change in PROMIS scores for patients who rated their change "a little better" or "a little worse." RESULTS: Baseline fatigue and pain interference scores were near normal (median 54 and 56, respectively). At 6 months, 7.9% of patients reported their fatigue was a little better compared to baseline (mean change [SD]: -2.6 [4.8]) and 22.8% a little worse (1.7 [5.6]). Pain was a little better for 11.5% of patients (-1.9 [6.1]) and a little worse for 24.2% of patients (0.6 [5.7]). At 12 months, results were similar. Thus, the MCT range was 1-2 points for both fatigue and pain interference. Correlations between change scores and retrospective ratings were low (0.13-0.29), indicating possible underestimation of MCT. CONCLUSION: The group-level MCT for PROMIS Fatigue and Pain Interference is roughly 2-3 points and corresponds to a small effect size, which is consistent with earlier work demonstrating an MCT of 2 points for PROMIS Physical Functioning.

SARS-CoV-2 in Childhood Cancer in 2020: A Disease of Disparities.

PURPOSE: The Pediatric Oncology COVID-19 Case Report registry supplies pediatric oncologists with data surrounding the clinical course and outcomes in children with cancer and SARS-CoV-2. METHODS: This observational study captured clinical and sociodemographic characteristics for children (≤ 21 years) receiving cancer therapy and infected with SARS-CoV-2 from the pandemic onset through February 19, 2021. The demographic and clinical characteristics of the cohort were compared with population-level pediatric oncology data (SEER). Multivariable binomial regression models evaluated patient characteristics associated with hospitalization, intensive care unit (ICU) admission, and changes in cancer therapy. RESULTS: Ninety-four institutions contributed details on 917 children with cancer and SARS-CoV-2. Median age at SARS-CoV-2 infection was 11 years (range, 0-21 years). Compared with SEER, there was an over-representation of Hispanics (43.6% v 29.7%, P < .01), publicly insured (59.3% v 33.5%, P < .01), and patients with hematologic malignancies (65.8% v 38.3%, P < .01) in our cohort. The majority (64.1%) were symptomatic; 31.2% were hospitalized, 10.9% required respiratory support, 9.2% were admitted to the ICU, and 1.6% died because of SARS-CoV-2. Cancer therapy was modified in 44.9%. Hispanic ethnicity was associated with changes in cancer-directed therapy (adjusted risk ratio [aRR] = 1.3; 95% CI, 1.1 to 1.6]). Presence of comorbidities was associated with hospitalization (aRR = 1.3; 95% CI, 1.1 to 1.6) and ICU admission (aRR = 2.3; 95% CI, 1.5 to 3.6). Hematologic malignancies were associated with hospitalization (aRR = 1.6; 95% CI, 1.3 to 2.1). CONCLUSION: These findings provide critical information for decision making among pediatric oncologists, including inpatient versus outpatient management, cancer therapy modifications, consideration of monoclonal antibody therapy, and counseling families on infection risks in the setting of the SARS-CoV-2 pandemic. The over-representation of Hispanic and publicly insured patients in this national cohort suggests disparities that require attention.