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Suicide is a severe public health issue globally. Accurately identifying high-risk lung cancer patients for suicidal behavior and taking timely intervention measures has become a focus of current research. This study intended to construct dynamic prediction models for identifying suicide risk among lung cancer patients. Patients were sourced from the Surveillance, Epidemiology, and End Results database, while meteorological data was acquired from the Centers for Disease Control and Prevention. This cohort comprised 455, 708 eligible lung cancer patients from January 1979 to December 2011. A Cox proportional hazard regression model based on landmarking approach was employed to explore the impact of meteorological factors and clinical characteristics on suicide among lung cancer patients, and to build dynamic prediction models for the suicide risk of these patients. Additionally, subgroup analyses were conducted by age and sex. The model's performance was evaluated using the C-index, Brier score, area under curve (AUC) and calibration plot. During the study period, there were 666 deaths by suicide among lung cancer patients. Multivariable Cox results from the dynamic prediction model indicated that age, marital status, race, sex, primary site, stage, monthly average daily sunlight, and monthly average temperature were significant predictors of suicide. The dynamic prediction model demonstrated well consistency and discrimination capabilities. Subgroup analyses revealed that the association of monthly average daily sunlight and monthly average temperature with suicide remained significant among female and younger lung cancer patients. The dynamic prediction model can effectively incorporate covariates with time-varying to predict lung cancer patients' suicide death. The results of this study have significant implications for assessing lung cancer individuals' suicide risk., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: To identify the symptom cluster among cancer survivors and examine their subgroup differences via network analysis based on nationally representative data., Methods: This cross-sectional study included 2966 survivors participating in the 2020 National Health Interview Survey (NHIS). Participants self-reported the presence of 14 symptoms capturing four clusters (physical, somatic, sleep, and psychologic problems). Network analysis models were used to reveal the relationships between symptoms and those interactions. Network comparison tests were applied to compare subgroups., Results: The core symptoms of the symptom cluster were fatigue (Bet = 33, Clo = 0.0067, Str = 0.9397), pain (Bet = 11, Clo = 0.0060, Str = 0.9226), wake up well rested (Bet = 25, Clo = 0.0057, Str = 0.8491), and anxiety (Bet = 5, Clo = 0.0043, Str = 0.9697) among cancer survivors. The core symptoms, network structure, and global strength were invariant between time since diagnoses (< 2 years vs. ≥ 2 years) or between numbers of cancers (1 vs. ≥ 2), yet varied between the comorbidity group and non-comorbidity group (≥ 1 vs. 0)., Conclusions: Fatigue would be a potential target for alleviating other symptoms through a negative feedback loop of other related symptoms of cancer survivors. In particular, cancer survivors with other chronic diseases should be the focus of attention and strengthen targeted intervention., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The United States has one of the largest incarcerated populations per capita. Prisons are dangerous environments, with high in-prison and postrelease mortality. The Death in Custody Reporting Acts (DCRAs) of 2000 and 2013 require deaths of people in correctional custody or caused by law enforcement to be reported to the Bureau of Justice Assistance. These deaths must be reported within 3 months of the death and include 10 required fields (eg, age, cause of death). There is no public reporting requirement. Our Third City Mortality project tracks near-real-time data about individual deaths released publicly and prison system metadata, including data completeness and release speed, across (N = 54) US state, federal (N = 2; Bureau of Prisons, Immigration and Customs Enforcement), Washington, District of Columbia, and Puerto Rico prison systems. Twenty-one (38%) systems release no individual death data; 13 systems release incomplete data slower than 1 year; 19 release timely, but incomplete, death data; and only one system (Iowa) releases complete and timely data. Incomplete, untimely, public prison mortality data limit protective community responses and epidemiology., Competing Interests: No conflicts of interest are declared., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc.)

Objectives: Our objectives were to document data availability and reporting on suicide mortality in state prison systems. The United States leads the world in mass incarceration, a structural determinant of health, but lacks real-time reporting of prison health statistics. This absence is particularly notable in suicides, a leading cause of death that carceral policies play a key role in mitigating., Methods: Suicide data for each state prison system from 2017-2021 were gathered through statistical reports, press releases, and Freedom of Information Act requests. We graded states based on data availability., Results: Only sixteen states provide updated, frequent, granular, freely provided suicide data. An additional thirteen states provided frequently updated data but that had little granularity, was incomplete, or was not freely provided. Eight states provided sparse, infrequent, or outdated data, and thirteen provided no data at all., Conclusions: The 2000 Death in Custody Reporting Act requires that states provide these data freely, yet the majority of states do not. There is a need for reliable, real-time data on suicides, suicide attempts, and conditions of confinement to better understand the harms of the carceral system and to advocate for change., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 LeMasters et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Carceral settings in the United States have been the source of many single site COVID-19 outbreaks. Quarantine is a strategy used to mitigate the spread of COVID-19 in correctional settings, and specific quarantine practices differ state to state. To better understand how states are using quarantine in prisons, we reviewed each state's definition of quarantine and compared each state's definition to the Centers for Disease Control's (CDC) definition and recommendations for quarantine in jails and prisons. Most prison systems, 45 of 53, define quarantine, but definitions vary widely. No state published definitions of quarantine that align with all CDC recommendations, and only 9 states provide quarantine data. In these states, the highest recorded quarantine rate occurred in Ohio in May 2020 at 843 per 1,000. It is necessary for prison systems to standardize their definitions of quarantine and to utilize quarantine practices in accordance with CDC recommendations. In addition, data transparency is needed to better understand the use of quarantine and its effectiveness at mitigating COVID-19 outbreaks in carceral settings., Competing Interests: The authors have declared that no competing interests exist.