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PURPOSE This study evaluated risk factors predicting unplanned 30-day acute service utilization among adults subsequent to hospitalization for a new diagnosis of leukemia, lymphoma, or myeloma. This study explored the prevalence of medical complications (aligned with OP-35 measure specifications from the Centers for Medicare & Medicaid Services [CMS] Hospital Outpatient Quality Reporting Program) and the potential impact of psychosocial factors on unplanned acute care utilization. METHODS This study included 933 unique patients admitted to three acute care inpatient facilities within a nonprofit community-based health care system in southern California from 2012 to 2017. Integrated comprehensive data elements from electronic medical records and facility oncology registries were leveraged for univariate statistics, predictive models constructed using multivariable logistic regression, and further exploratory data mining, with predictive accuracy of the models measured with c-statistics. RESULTS The mean age of study participants was 65 years, and 55.1% were male. Specific diagnoses were lymphoma (48.7%), leukemia (35.2%), myeloma (14.0%), and mixed types (2.1%). Approximately one fifth of patients received unplanned acute care services within 30 days postdischarge, and over half of these patients presented with one or more symptoms associated with the CMS medical complication measure. The predictive models, with c-statistics ranging from 0.7 and above for each type of hematologic malignancy, indicated good predictive qualities with the impact of psychosocial functioning on the use of acute care services ( P values < .05), including lack of consult for social work during initial admission (lymphoma or myeloma), history of counseling or use of psychotropic medications (lymphoma), and past substance use (myeloma). CONCLUSION This study provides insights into patient-related factors that may inform a proactive approach to improve health outcomes, such as enhanced care transition, monitoring, and support interventions.

Patient age, comorbidity burden, and disease severity at presentation are the major factors associated with surviving COVID-19. Hospital-level factors including ICU occupancy may confer additional risk to individual patients, particularly at times of maximal stress on healthcare systems. The interaction of patient- and hospital-level factors over time during pandemic disease remains an area of active exploration.To determine the impact of patient and hospital risk factors during episodic surges, characterize severity distribution between waves, and evaluate patient-level impact of ICU capacity on COVID-19 survivorship.Retrospective cohort study.Four acute care hospitals within an integrated healthcare network in San Diego, California.All patients (18+ y.o.) admitted with a positive PCR test for SARS-CoV-2 or ICD-10 code for COVID-19 from March 1, 2020 through June 30, 2021.Patient survivorship and length of stay.Six thousand eight hundred fifty-one patients were evaluated in this large cohort series. Patient level factors associated with mortality included: severity at admission (WHO Clinical Progression Score [WCPS]), age, gender, BMI, marital status, language preference, Elixhauser score, elevated laboratory (d-dimer, ferritin, LDH) or lower absolute lymphocyte count. When adjusting for patient age alone, survivorship during surges was also inversely associated with ICU occupancy, though this correlation was not present when adjusted for patient-level factors.Patient age, comorbidity burden, and severity at the time of presentation are the major factors associated with surviving COVID-19. Hospital-level factors including ICU occupancy may confer additional risk to individual patients, particularly at times of maximal stress on healthcare systems.

Background Post-traumatic stress disorder (PTSD) is a significant public health problem, affecting approximately 7% of the general population and 13–18% of the combat Veteran population. The first study using acupuncture for PTSD in a civilian population showed large pre- to post-treatment effects for an empirically developed verum protocol, which was equivalent to group cognitive behavior therapy and superior to a wait-list control. The primary objective of this study is to determine both clinical and biological effects of verum acupuncture for combat-related PTSD in treatment-seeking US Veterans. Methods This is a two-arm, parallel-group, prospective randomized placebo-controlled clinical trial. The experimental condition is verum acupuncture and the placebo control is sham (minimal) acupuncture in 1-h sessions, twice a week for 12 weeks. Ninety subjects will provide adequate power and will be allocated to group by an adaptive randomization procedure. The primary outcome is change in PTSD symptom severity from pre- to post-treatment. The secondary biological outcome is change from pre- to post-treatment in psychophysiological response, startle by electromyographic (EMG) eyeblink. Assessments will be conducted at pre-, mid-, post-, and 1-month post-treatment, blind to group allocation. Intent-to-treat analyses will be conducted. Discussion The study results will be definitive because both clinical and biological outcomes will be assessed and correlated. Issues such as the number needed for recruitment and improvement, use of sham acupuncture, choice of biological measure, and future research need will be discussed. Trial registration ClinicalTrials.gov NCT02869646. Registered on 17 August 2016.

Background Clinical guidelines emphasize identifying atrial fibrillation (AF) as a strategy to reduce stroke risk. Cardiac implantable electronic device (CIED) interrogation at the point of care may facilitate AF detection, increasing opportunities to identify patients at high risk for stroke. Objectives This study sought to quantify AF prevalence and assess stroke risk in patients with a CIED who presented to the emergency department (ED). Methods This noninterventional, retrospective observational study included adult patients who presented at a single facility ED that incorporated device interrogation as a routine standard practice for all patients with a CIED. Interrogations were conducted in 494 unique patients, and relevant demographic/clinical information was captured from electronic medical records. Results AF was detected via CIED interrogation in 54.8% (271/494) of the unique patient population that presented to the ED. Device interrogation detected the presence of AF in 110 patients without a documented past history or current diagnosis of AF, representing 22.3% (110/494) of total unique patients. Based on CHA2DS2-VASc (Congestive heart failure, Hypertension, Age > 75 years, Diabetes mellitus, prior Stroke or transient ischemic attack or thromboembolism, Vascular disease, Age 65-74 years, Sex category [female]) risk scoring methodology, over three-quarters of these newly detected AF patients (78.2%, 86/110) were classified in a high stroke risk category that reflected a > 2.2% annualized risk, and over half (57.3%, 63/110) presented to the ED for reasons unrelated to cardiac/dysrhythmia problems. Conclusions The use of technology-assisted device interrogation of CIEDs at the point of care has promise in identifying patients with asymptomatic AF. Results suggest consideration of routine device interrogation of CIEDs in the ED, regardless of reason for admission or history of AF.

Among 1,770 healthcare workers serving in high-risk care areas for coronavirus disease 2019 (COVID-19), 39 (2.2%) were seropositive. Exposure to severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the community was associated with being seropositive. Job or unit type and percentage of time working with COVID-19 patients were not associated with positive antibody tests.

Introduction: Student success in institutions of higher education is of mounting importance as the link between education, health, and well-being grows. This study investigates adverse childhood experiences (ACE) that may confer resilience or may negatively impact a student’s educational success independent of other known factors for educational achievement. Methods: A cross-sectional survey-based study was conducted using the 2012 Behavioral Risk Factor Surveillance System (BRFSS) data to investigate a large representative US Population of 25-35-year-olds (n=2,543) that were surveyed on ACE measures. Univariate and weighted multivariable logistic regression models focused on educational success in those with and without ACE factors are presented. All data management and analyses were conducted using SAS® 9.4. Results: Each single level increase of ACE reporting indicated an unadjusted 5% decrease in odds for obtaining some college or a college degree. Adjusting for other ACEs, demographic, socioeconomic, behavioral, and health factors, parental depression and mental illness in childhood was associated with college success, while those who experienced parental drug use, prison time, divorce, and being physically hurt at home as a child had statistically significantly lower odds of college success. Other significant predictors of academic success included being female, normal weight, never smoking, never being married, reporting good/excellent health, and exercising with a c-statistic of .88 indicating a strong prediction model. Conclusions: Higher education success and the myriad of associated public health benefit requires significant student and institutional interaction to be flexible enough to engage traditional as well as adult, non-traditional, and underserved student populations. Understanding the modifiable and non-modifiable constellation of health and well-being factors will better allow a more population-based student-centered approach to higher education. Keywords: education; public health; learning; Resilience

Study Design. A prospective cohort study. Objective. Activities performed during military operations vary in complexity and physical demand. The risk for mental illness following military combat deployment has been well documented. However, information regarding the possible contribution of back pain to decreased mental and functional health is scarce. To our knowledge, this is the first study to prospectively assess deployment and self-reported recent back pain in a population-based U.S. military cohort. Summary of Background Data. The study consisted of Millennium Cohort participants who were followed for the development of back pain for an average of 3.9 years. Methods. Descriptive statistics and longitudinal analyses were used to assess the temporal relationship of deployment with self-reported recent back pain at follow-up (N = 53,933). Results. Recent back pain was self-reported by 8379 (15.5%) participants at follow-up. After adjusting for covariates, deployers with combat experiences had higher odds [odds ratio (OR) = 1.38, 95% confidence interval (95% CI): 1.28–1.50] of recent back pain than noncombat deployers. There was no association between recent back pain and nondeployers compared with noncombat deployers. Service support/supply handlers were at an increased odds of reporting recent back pain (OR = 1.11, 95% CI: 1.02–1.21) than functional support/administration occupations. Occupations associated with a physically demanding work environment had a higher risk of back pain. Conclusion. Deployers with combat experiences were more likely to report back pain postdeployment. This well-defined group of military personnel may potentially benefit from integrated prevention efforts. Level of Evidence: 3

Improving the ability to predict which patients are at increased risk for readmission can lead to more effective interventions and greater compliance with CMS Hospital Readmissions Reduction Program (HRRP) requirements. This study evaluated the performance of a risk model that used data from a health system's electronic medical record (EMR) to predict all-cause readmission among adult inpatients with acute medical conditions, with a specific focus on the impact of including behavioral health screening data. The study included 39,155 unique adult patients admitted during 2015 to 4 acute care inpatient facilities within a nonprofit community-based health care system. The risk model integrated a comprehensive set of data elements including demographics, psychosocial characteristics, medical history, assessment results, and clinical events. Predictive models were constructed using a multivariable logistic regression with a stepwise selection approach. Among study participants, the mean age was 62.9 years, 48.0% were male, 31.2% had comorbid psychiatric conditions, and 6986 had medical conditions/procedures subject to HRRP penalties. Results from exploratory predictive analyses demonstrated that any patients with a Serious Mental Illness (SMI) diagnosis were 28% more likely to be readmitted within 30 days, and the likelihood of readmission associated with SMI increased to 56% for patients with medical conditions subject to HRRP penalties. As health care systems face increasing pressures to reduce readmissions and avoid CMS HRRP financial penalties, study results indicate the importance of including behavioral health data from EMRs and screening assessments for all inpatients to improve discharge planning and patient outcomes.

Objective: To prospectively examine the association between post-traumatic stress disorder (PTSD) and weight change. Methods: Longitudinal analysis techniques were used to examine data (2001-2008) from Millennium Cohort Study participants, consisting of U.S. service members and veterans. Using the PTSD Checklist-Civilian Version, PTSD was assessed as none, resolved, new onset, or persistent. Subsequent weight change was assessed as stable (_3% loss or gain), 3% weight loss, 3% but 10% weight gain, and _10% weight gain. Results: Of the 38,352 participants, 2391 (6.2%) had PTSD (838 resolved, 1024 new onset, and 529 persistent), and 11% of participants subsequently had _10% weight gain. In multivariable models, PTSD was associated with higher odds of _10% weight gain (new onset OR: 1.44 [95% CI: 1.20-1.73]; persistent OR: 1.51 [CI: 1.17-1.96]; resolved OR: 1.30 [CI: 1.05-1.60]) compared with those without PTSD. New-onset and persistent PTSD were also associated with higher odds of 3% weight loss (OR: 1.41 [CI: 1.17-1.71]; OR: 1.42 [CI: 1.09-1.86], respectively). Conclusions: PTSD is independently associated with a higher risk of weight gain and loss, the former of which leads to a higher prevalence of overweight and obesity and a higher risk of comorbidities associated with excessive body adiposity.

Public health researchers increasingly recognize that to advance their field they must grapple with the availability of increasingly large (i.e., thousands of variables) traditional population-level datasets (e.g., electronic medical records), while at the same time integrating additional large datasets (e.g., data on genomics, the microbiome, environmental exposures, socioeconomic factors, and health behaviors). Leveraging these multiple forms of data might well provide unique and unexpected discoveries about the determinants of health and wellbeing. However, we are in the very early stages of advancing the techniques required to understand and analyze big population-level data for public health research. To address this problem, this paper describes how we propose that big data can be efficiently used for public health discoveries. We show that data analytics techniques traditionally employed in public health studies are not up to the task of the data we now have in hand. Instead we present techniques adapted from big data visualization and analytics approaches used in other domains that can be used to answer important public health questions utilizing these existing and new datasets. Our findings are based on an exploratory big data case study carried out in San Diego County, California where we analyzed thousands of variables related to health to gain interesting insights on the determinants of several health outcomes, including life expectancy and anxiety disorders. These findings provide a promising early indication that public health research will benefit from the larger set of activities in contemporary big data research.

312 Background: Improving the ability to predict which patients are at increased risk for acute care services subsequent to hospitalization can lead to more effective interventions and quality care. This study evaluated the performance of risk models to predict 30-day acute service utilization among adult inpatients with newly diagnosed hematologic malignancies. The study explored the impact of medical complications on potentially preventable service utilization, aligned with specifications for proposed measure OP-35 from the CMS Hospital Outpatient Quality Reporting Program. Methods: The study included 933 unique adult patients admitted to four acute care inpatient facilities within a non-profit community based healthcare system during calendar years 2012 to 2017. Risk models integrated a comprehensive set of data elements using clinical information from electronic medical records and facility oncology registries. Predictive models were constructed using a multivariable logistic regression, with calculation of standardized coefficients to rank order variables with the greatest impact on unplanned readmissions. Exploratory data mining techniques were conducted to augment classification of at-risk patients. Results: The mean age of study participants was 65 years and 55.1% were male. In order of prevalence, the types of hematologic malignancy were lymphoma (48.7%), leukemia (35.2%), myeloma (14.0%), and mixed types (2.1%). Approximately one fifth of patients received unplanned acute care services within 30 days after the index hospitalization (lymphoma = 23.1%, myeloma = 22.1%, leukemia = 18.6%). Among the risk models, the most influential predictors included initial length of stay, severity of illness, and consult for social work. Overall, 56.6% of patients who were readmitted within 30 days of hospitalization presented with one or more symptoms associated with the CMS medical complication measure, including cytopenia, infection, negative hydration, and/or pain. Conclusions: Optimizing quality care in a value-based health care environment requires proactive approaches such as risk prediction models for identifying patients who can benefit most from care transition interventions.