Trends and Seasonality of Emergency Department Visits and Hospitalizations For Suicidality among Children and adolescents in the Us From 2016 to 2021

IMPORTANCE: The detection of seasonal patterns in suicidality should be of interest to clinicians and US public health officials, as intervention efforts can benefit by targeting periods of heightened risk. OBJECTIVES: to examine recent trends in suicidality rates, quantify the seasonality in suicidality, and demonstrate the disrupted seasonality patterns during the spring 2020 COVID-19-related school closures among US children and adolescents. DESIGN, SETTING, AND PARTICIPANTS: This population-based, descriptive cross-sectional study used administrative claims data from Optum\u27s deidentifed Clinformatics Data Mart Database. Participants included children aged 10 to 12 years and adolescents aged 13 to 18 years who were commercially insured from January 1, 2016, to December 31, 2021. Statistical analysis was conducted between April and November 2022. EXPOSURES: Month of the year and COVID-19 pandemic. MAIN OUTCOMES AND MEASURES: Rates and seasonal patterns of emergency department (ED) visits and hospitalizations for suicidality. RESULTS: The analysis included 73 123 ED visits and hospitalizations for suicidality reported between 2016 and 2021. Among these events, 66.1% were reported for females, and the mean (SD) age at the time of the event was 15.4 (2.0) years. The mean annual incidence of ED visits and hospitalizations for suicidality was 964 per 100 000 children and adolescents (95% CI, 956-972 per 100 000), which increased from 760 per 100 000 (95% CI, 745-775 per 100 000) in 2016 to 1006 per 100 000 (95% CI, 988-10 024 per 100 000) in 2019, with a temporary decrease to 942 per 100 000 (95% CI, 924-960 per 100 000) in 2020 and a subsequent increase to 1160 per 100 000 (95% CI, 1140-1181 per 100 000) in 2021. Compared with January, seasonal patterns showed peaks in April (incidence rate ratio [IRR], 1.15 [95% CI, 1.11-1.19]) and October (IRR, 1.24 [95% CI, 1.19-1.29]) and a nadir in July (IRR, 0.63 [95% CI, 0.61-0.66]) during pre-COVID-19 years and 2021. However, during the spring of 2020, which coincided with school closures, seasonal patterns were disrupted and April and May exhibited the lowest rates. CONCLUSIONS AND RELEVANCE: The findings of this study indicated the presence of seasonal patterns and an observed unexpected decrease in suicidality among children and adolescents after COVID-19-related school closures in March 2020, which suggest a potential association between suicidality and the school calendar

Does Hospital Location Matter? association of Neighborhood Socioeconomic Disadvantage With Hospital Quality in Us Metropolitan Settings

An aspect of a hospital\u27s location, such as its degree of socioeconomic disadvantage, could potentially affect quality ratings of the hospital; yet, few studies have granularly explored this relationship in United States (US) metropolitan areas characterized by a wide breadth of socioeconomic disparities across neighborhoods. An understanding of the effect of neighborhood socioeconomic disadvantage on hospital quality of care is informative for targeting resources in poor neighborhoods. We assessed the association of neighborhood socioeconomic disadvantage with hospital quality of care across several areas of quality (including mortality, readmission, safety, patient experience, effectiveness of care, summary and overall star rating) in US metropolitan areas. Hospitals in the most disadvantaged neighborhoods, compared to hospitals in the least disadvantaged neighborhoods, had worse mortality scores, readmission scores, safety of care scores, patient experience of care scores, effectiveness of care scores, summary scores and overall star rating. Timeliness of care and efficient use of imaging scores were not strongly associated with neighborhood socioeconomic disadvantage; although, future studies are needed to validate this finding. Policymakers could target innovative strategies for improving neighborhood socioeconomic conditions in more disadvantaged areas, as this may improve hospital quality

Contemporary analysis of Reexcision and Conversion to Mastectomy Rates and associated Healthcare Costs For Women Undergoing Breast-Conserving Surgery

PURPOSE: This study was designed to provide a comprehensive and up-to-date understanding of population-level reoperation rates and incremental healthcare costs associated with reoperation for patients who underwent breast-conserving surgery (BCS). METHODS: This is a retrospective cohort study using Merative™ MarketScan RESULTS: The commercial cohort included 17,129 women with a median age of 55 (interquartile range [IQR] 49-59) years, and the Medicare cohort included 6977 women with a median age of 73 (IQR 69-78) years. Overall reoperation rates were 21.1% (95% confidence interval [CI] 20.5-21.8%) for the commercial cohort and 14.9% (95% CI 14.1-15.7%) for the Medicare cohort. In both cohorts, reoperation rates decreased as age increased, and conversion to mastectomy was more prevalent among younger women in the commercial cohort. The mean healthcare costs during 1 year of follow-up from the initial BCS were $95,165 for the commercial cohort and$36,313 for the Medicare cohort. Reoperations were associated with 24% higher costs in both the commercial and Medicare cohorts, which translated into $21,607 and$8559 incremental costs, respectively. CONCLUSIONS: The rates of reoperation after BCS have remained high and have contributed to increased healthcare costs. Continuing efforts to reduce reoperation need more attention

Covid-19 Severity Scale For Claims Data Research

OBJECTIVE: to create and validate a methodology to assign a severity level to an episode of COVID-19 for retrospective analysis in claims data. DATA SOURCE: Secondary data obtained by license agreement from Optum provided claims records nationally for 19,761,754 persons, of which, 692,094 persons had COVID-19 in 2020. STUDY DESIGN: The World Health Organization (WHO) COVID-19 Progression Scale was used as a model to identify endpoints as measures of episode severity within claims data. Endpoints used included symptoms, respiratory status, progression to levels of treatment and mortality. DATA COLLECTION/EXTRACTION METHODS: The strategy for identification of cases relied upon the February 2020 guidance from the Centers for Disease Control and Prevention (CDC). PRINCIPAL FINDINGS: A total of 709,846 persons (3.6%) met the criteria for one of the nine severity levels based on diagnosis codes with 692,094 having confirmatory diagnoses. The rates for each level varied considerably by age groups, with the older age groups reaching higher severity levels at a higher rate. Mean and median costs increased as severity level increased. Statistical validation of the severity scales revealed that the rates for each level varied considerably by age group, with the older ages reaching higher severity levels (p \u3c 0.001). Other demographic factors such as race and ethnicity, geographic region, and comorbidity count had statistically significant associations with severity level of COVID-19. CONCLUSION: A standardized severity scale for use with claims data will allow researchers to evaluate episodes so that analyses can be conducted on the processes of intervention, effectiveness, efficiencies, costs and outcomes related to COVID-19

Future expenditure risk of silent members: a statistical analysis

Silent-members are members of a medical health plan who submit no claims for healthcare services in a benefit year despite 12 months of continuous-enrollment. This study was conducted to evaluate the future expenditure risk of commercial-insured members who avoid all medical care despite coverage. In order to determine if the silent-members were at greater risk, we compared them to members who received care in the anchor year (2009) but had low-expenditures. The low-expenditure members were assumed to represent persons without significant medical conditions and without care-avoidance behaviors. We examined the claims experience of a cohort of silent members in the 2 years after the silent year (2009) and compared it with the corresponding claims experience for a cohort of low-expenditure members from the same anchor year (2009). Members of commercial health plans (BCBS of Texas) were selected based on continuous-enrollment in 2009. Two sub-groups were identified based on annual claims expenditure: Care avoiders were members with 12 months continuous-enrollment and no medical claims, and are thus referred to as “silent members” in the insurance industry. Low-Expenditure members were those with 12 months continuous-enrollment and total PMPY (per member per year) annual medical claims expenditure in the lowest 10th percentile of members with claims experience. “Low-expenditure” members served as a comparison group to the “silent members”, under the assumption that such claimants were using benefits for minor healthcare issues as needed. Key variables were enrollment and expenditures. Enrollment data identified demographics and continuous-enrollment. Medical claims data were used to calculate utilization and expenditures. All claims data were de-identified and no consent was required, as approved by the Institutional Review Board. No research involved human subjects. Multivariate logistic regression models were applied. Silent members who seek care in subsequent years have a greater probability of becoming high-expenditure claimants than those with low-expenditure experience. For silent members who subsequently seek treatment, the probability of becoming high-expenditure is significantly greater than low-expenditure members from the anchor year. The implications of future high costs for silent members who become claimants may support the need for additional research to address the risks of care avoidance behaviors.https://doi.org/10.1186/s12913-016-1552-

Survey of shift and work tolerance of City of Houston emergency medical service personnel

This study of ambulance workers for the emergency medical services of the City of Houston studied the factors related to shiftwork tolerance and intolerance. The EMS personnel work a 24-hour shift with rotating days of the week. Workers are assigned to A, B, C, D shift, each of which rotate 24-hours on, 24-hours off, 24-hours on and 4 days off. One-hundred and seventy-six male EMTs, paramedics and chauffeurs from stations of varying levels of activity were surveyed. The sample group ranged in age from 20 to 45. The average tenure on the job was 8.2 years. Over 68% of the workers held a second job, the majority of which worked over 20 hours a week at the second position. The survey instrument was a 20-page questionnaire modeled after the Folkard Standardized Shiftwork Index. In addition to demographic data, the survey tool provided measurements of general job satisfaction, sleep quality, general health complaints, morningness/eveningness, cognitive and somatic anxiety, depression, and circadian types. The survey questionnaire included an EMS-specific scaler of stress. A conceptual model of Shiftwork Tolerance was presented to identify the key factors examined in the study. An extensive list of 265 variables was reduced to 36 key variables that related to: (1) shift schedule and demographic/lifestyle factors, (2) individual differences related to traits and characteristics, and (3) tolerance/intolerance effects. Using the general job satisfaction scaler as the key measurement of shift tolerance/intolerance, it was shown that a significant relationship existed between this dependent variable and stress, number of years working a 24-hour shift, sleep quality, languidness/vigorousness. The usual amount of sleep received during the shift, general health complaints and flexibility/rigidity (R\sp2 =.5073). The sample consisted of a majority of morningness-types or extreme-morningness types, few evening-types and no extreme-evening types, duplicating the findings of Motohashi\u27s previous study of ambulance workers. The level of activity by station was not significant on any of the dependent variables examined. However, the shift worked had a relationship with sleep quality, despite the fact that all shifts work the same hours and participate in the same rotation schedule

Factors Associated With COVID-19 Death in the United States: Cohort Study

BackgroundSince the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making. ObjectiveThis study aims to assess the factors associated with COVID-19–related deaths from a large, national, individual-level data set. MethodsA cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic. ResultsA total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19–related death included older age (≥80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI ≥40 vs <30 kg/m2: HR 1.71, 95% CI 1.50-1.96), race (Hispanic White, African American, Asian vs non-Hispanic White: HR 2.46, 95% CI 2.01-3.02; HR 2.27, 95% CI 2.06-2.50; HR 2.06, 95% CI 1.65-2.57), region (South, Northeast, Midwest vs West: HR 1.62, 95% CI 1.33-1.98; HR 2.50, 95% CI 2.06-3.03; HR 1.35, 95% CI 1.11-1.64), chronic respiratory disease (HR 1.21, 95% CI 1.12-1.32), cardiac disease (HR 1.10, 95% CI 1.01-1.19), diabetes (HR 1.92, 95% CI 1.75-2.10), recent diagnosis of lung cancer (HR 1.70, 95% CI 1.14-2.55), severely reduced kidney function (HR 1.92, 95% CI 1.69-2.19), stroke or dementia (HR 1.25, 95% CI 1.15-1.36), other neurological diseases (HR 1.77, 95% CI 1.59-1.98), organ transplant (HR 1.35, 95% CI 1.09-1.67), and other immunosuppressive conditions (HR 1.21, 95% CI 1.01-1.46). ConclusionsThis is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19–related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups