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Background: Prior research demonstrates that SARS-COV-2 infection can be associated with a broad range of mental health outcomes including depression symptoms. Veterans, in particular, may be at elevated risk of increased depression following SARS-COV-2 infection given their high rates of pre-existing mental and physical health comorbidities. However, few studies have tried to isolate SARS-COV-2 infection associations with long term, patient-reported depression symptoms from other factors (e.g., physical health comorbidities, pandemic-related stress). Objective: To evaluate the association between SARS-COV-2 infection and subsequent depression symptoms among United States Military Veterans. Design: Survey-based non-randomized cohort study with matched comparators. Participants: A matched-dyadic sample from a larger, stratified random sample of participants with and without known to SARS-COV-2 infection were invited to participate in a survey evaluating mental health and wellness 18-months after their index infection date. Sampled participants were stratified by infection severity of the participant infected with SARS-COV-2 (hospitalized or not) and by month of index date. A total of 186 participants in each group agreed to participate in the survey and had sufficient data for inclusion in analyses. Those in the uninfected group who were later infected were excluded from analyses. Main Measures: Participants were administered the Patient Health Questionnaire-9 as part of a phone interview survey. Demographics, physical and mental health comorbidities were extracted from VHA administrative data. Key Results: Veterans infected with SARS-COV-2 had significantly higher depression symptoms scores compared with those uninfected. In particular, psychological symptoms (e.g., low mood, suicidal ideation) scores were elevated relative to the comparator group (MInfected = 3.16, 95%CI: 2.5, 3.8; MUninfected = 1.96, 95%CI: 1.4, 2.5). Findings were similar regardless of history of depression. Conclusion: SARS-COV-2 infection was associated with more depression symptoms among Veterans at 18-months post-infection. Routine evaluation of depression symptoms over time following SARS-COV-2 infection is important to facilitate adequate assessment and treatment. [ABSTRACT FROM AUTHOR]

Purpose: Some hospitals in the United States (US) use intensive care 20 times more than others. Since intensive care is lifesaving for some but potentially harmful for others, there is a need to understand factors that influence how intensive care unit (ICU) admission decisions are made. Methods: A qualitative analysis of eight US hospitals was conducted with semi-structured, one-on-one interviews supplemented by site visits and clinical observations. Results: A total of 87 participants (24 nurses, 52 physicians, and 11 other staff) were interviewed, and 40 h were spent observing ICU operations across the eight hospitals. Four hospital-level factors were identified that influenced ICU admission decision-making. First, availability of intermediate care led to reallocation of patients who might otherwise be sent to an ICU. Second, participants stressed the importance of ICU nurse availability as a key modifier of ICU capacity. Patients cared for by experienced general care physicians and nurses were less likely to receive ICU care. Third, smaller or rural hospitals opted for longer emergency department patient-stays over ICU admission to expedite interhospital transfer of critically ill patients. Fourth, lack of clarity in ICU admission policies led clinicians to feel pressured to use ICU care for patients who might otherwise not have received it. Conclusion: Health care systems should evaluate their use of ICU care and establish institutional patterns that ensure ICU admission decisions are patient-centered but also account for resources and constraints particular to each hospital. [ABSTRACT FROM AUTHOR]

Background: Survivors of severe COVID-19 are at risk of impaired health-related quality of life (HRQoL) and persistent physical and psychological disability after ICU and hospital discharge. The subsequent social burden is a major concern. We aimed to assess the short-term HRQoL, physical function and prevalence of post-traumatic stress symptoms of invasively mechanically ventilated COVID-19 patients treated in our ICU. Methods: Prospective, observational cohort study in a follow-up clinic. Patients completed a 6-min walking test (6MWT) to assess their cardio-pulmonary function around 2 months (early follow-up) from hospital discharge, the EQ-5D-5L questionnaire for quality of life assessment around 2 months and at 6 months from hospital discharge and an anonymous web-based Impact of Event Scale-Revised (IES-R) questionnaire for Post-Traumatic Stress symptoms at 2 months. Results: 47 patients attended our follow-up program, mean age 59 ± 10 years, median pre-morbid Clinical Frailty Scale (CFS) 2 [2–3]. The median distance walked in 6 min was 470 [406–516] m, 83 [67–99]% of the predicted value. Overall 1 out 3 patients and 4/18 (22%) among those with a good functional baseline prior to COVID-19 (CFS of 1 or 2) had lower (84%) than predicted 6MWT. EQ-5D-5L quality of life VAS was 80 [70–90] out of 100 at early follow-up with a slight improvement to 85 [77.5–90] at 6 months. Mobility, self-care and usual activities improved between the two timepoints, while pain/discomfort and depression/anxiety did not improve or got worse. The IES-R total score was greater than the threshold for concern of 1.6 in 27/41(66%) respondents. Conclusions: Patients recovering from severe COVID-19 requiring invasive mechanical ventilation surviving hospital discharge present with early mild to moderate functional impairment, mildly reduced quality of life from hospital discharge with an overall improvement of mobility, self-care and the ability of performing usual activities, while a worsening of pain and depression/anxiety symptoms at 6 months and a large proportion of symptoms of post-traumatic distress soon after hospital discharge. [ABSTRACT FROM AUTHOR]

Marital status is recognized as an important social determinant of health, income, and social support, but is rarely available in administrative data. We assessed the feasibility of using exact address data and zip code history to identify cohabiting couples using the 2018 Medicare Vital Status file and ZIP codes in the 2011–2014 Master Beneficiary Summary Files. Medicare beneficiaries meeting our algorithm displayed characteristics consistent with assortative mating and resembled known married couples in the Health and Retirement Study linked to Medicare claims. Address information represents a promising strategy for identifying cohabiting couples in administrative data including healthcare claims and other data types. [ABSTRACT FROM AUTHOR]

Purpose: Patients with persistent critical illness may account for up to half of all intensive care unit (ICU) bed-days. It is unknown if there is hospital variation in the development of persistent critical illness and if hospital performance affects the incidence of persistent critical illness. Methods: This is a retrospective analysis of Veterans admitted to the Veterans Administration (VA) ICUs from 2015 to 2017. Hospital performance was defined by the risk- and reliability-adjusted 30-day mortality. Persistent critical illness was defined as an ICU length of stay of at least 11 days. We used 2-level multilevel logistic regression models to assess variation in risk- and reliability-adjusted probabilities in the development of persistent critical illness. Results: In the analysis of 100 hospitals which encompassed 153,512 hospitalizations, 4.9% (N = 7640/153,512) developed persistent critical illness. There was variation in the development of persistent critical illness despite controlling for patient characteristics (intraclass correlation: 0.067, 95% CI 0.049–0.091). Hospitals with higher risk- and reliability-adjusted 30-day mortality had higher probabilities of developing persistent critical illness (predicted probability: 0.057, 95% CI 0.051–0.063, p < 0.01) compared to those with lower risk- and reliability-adjusted 30-day mortality (predicted probability: 0.046, 95% CI 0.041–0.051, p < 0.01). The median odds ratio was 1.4 (95% CI 1.33–1.49) implying that, for two patients with the same physiology on admission at two different VA hospitals, the patient admitted to the hospital with higher adjusted mortality would have 40% greater odds of developing persistent critical illness. Conclusion: Hospitals with higher risk- and reliability-adjusted 30-day mortality have a higher probability of developing persistent critical illness. Understanding the drivers of this variation may identify modifiable factors contributing to the development of persistent critical illness. [ABSTRACT FROM AUTHOR]

Purpose: Persistent critical illness has been described as a subtype of chronic critical illness, characterized as a transition after ICU admission where primary diagnosis and illness acuity are no better at predicting outcome than pre-hospital characteristics. Herein we describe the occurrence and outcomes associated with persistent critical illness in a large Canadian health region.Methods: In this multi-center observational cohort study, all patients aged older than 14 years admitted to 12 ICUs in Alberta, Canada, between June 2012 and December 2014 were included. Primary outcome was in-hospital mortality. Predictors at ICU admission were separated into: (1) antecedent characteristics component (e.g., demographics, chronic health component of the APACHE II score, comorbid conditions); and (2) acute illness component (e.g., APACHE II score at admission, SOFA score, primary diagnostic category, surgical status, acute organ support). Using multiple statistical methods and randomly splitting the cohort into development and validation samples for risk scoring using logistic regression, we examined mortality prediction of each of these components to characterize the timing of transition to persistent critical illness.Results: We included 17,783 patients with a median (IQR) age 61 years (49-71), 62% were male, and mean APACHE II score was 19.0 (7.9). In-hospital mortality was 16.8%. Among patients alive and in ICU, the acute illness component, which accurately predicted outcome at the time of admission [area under the receiver operating characteristics curve (AUC) 0.861; 95% CI 0.860-0.862], progressively lost predictive ability and was no longer more predictive than antecedent characteristics after 9 days. This transition defined the onset of persistent critical illness and comprised 16.1% (n = 2856) of the cohort. Transition ranged between 5 and 21 days across subgroups. In-hospital mortality was greater for those with persistent critical illness [23.9% vs. 15.5%, odds ratio (OR) 1.54; 95% CI 1.43-1.67, p < 0.001]. Persistently critically ill patients accounted for 54.5% of 97844 ICU bed-days and 36.3% of 420119 hospital bed-days, respectively.Conclusions: Persistent critical illness occurred in one in six patients admitted to Alberta ICUs and portended greater risk of death, prolonged ICU and hospital stay, and disproportionate use of health resources compared to patients without persistent critical illness. [ABSTRACT FROM AUTHOR]

The frequency and intensity of weather and climate-related disasters that have devastating effects on millions of Americans is expected to increase due to climate change.[1], [2] While the direct death toll of disasters is well-recognized,[3] little is known regarding how the impact of disasters and their resultant disruption on healthcare affect health outcomes among individuals with serious health conditions and complex healthcare needs. We identified 1,738 cancer cases diagnosed (794 breast, 532 colorectal, and 412 lung) in parishes affected by Hurricane Katrina and a matched sample of 6557 cancer cases. Graph: Figure 1 Long-term cancer survival among individuals exposed to Hurricane Katrina. [Extracted from the article]

Purpose: Frail patients are known to experience poor outcomes. Nevertheless, we know less about how frailty manifests itself in patients' physiology during critical illness and how it affects resource use in intensive care units (ICU). We aimed to assess the association of frailty with short-term outcomes and organ support used by critically ill patients.Methods: Retrospective analysis of prospective collected data from 93 ICUs in Brazil from 2014 to 2015. We assessed frailty using the modified frailty index (MFI). The primary outcome was in-hospital mortality. Secondary outcomes were discharge home without need for nursing care, ICU and hospital length of stay (LOS), and utilization of ICU organ support and transfusion. We used mixed logistic regression and competing risk models accounting for relevant confounders in outcome analyses.Results: The analysis consisted of 129,680 eligible patients. There were 40,779 (31.4%) non-frail (MFI = 0), 64,407 (49.7%) pre-frail (MFI = 1-2) and 24,494 (18.9%) frail (MFI ≥ 3) patients. After adjusted analysis, frailty was associated with higher in-hospital mortality (OR 2.42, 95% CI 1.89-3.08), particularly in patients admitted with lower SOFA scores. Frail patients were less likely to be discharged home (OR 0.36, 95% CI 0.54-0.79) and had higher hospital and ICU LOS than non-frail patients. Use of all forms of organ support (mechanical ventilation, non-invasive ventilation, vasopressors, dialysis and transfusions) were more common in frail patients and increased as MFI increased.Conclusions: Frailty, as assessed by MFI, was associated with several patient-centered endpoints including not only survival, but also ICU LOS and organ support. [ABSTRACT FROM AUTHOR]

Hospital readmission is a key metric of hospital quality, such as for comparing Veterans Affairs (VA) hospitals to private sector hospitals. To calculate readmission rates, one must first identify individual hospitalizations. However, in the VA Corporate Data Warehouse (CDW), data are organized by “bedded stays,” that is, any stay in a healthcare facility where a patient is provided a bed, not hospitalizations. Thus, CDW data poses several challenges to identifying hospitalizations including: (1) bedded stays include both non-acute inpatient stays (i.e. nursing home, mental health) and acute inpatient hospital care; (2) transfers between VA facilities appear as separate bedded stays; and (3) VA care may also be fragmented by non-VA care. Thus, we sought to develop a rigorous method to identify acute hospitalizations using the VA CDW. We examined all VA bedded stays with an admission date in 2009. Non-acute portions of a stay were dropped. VA to VA transfers were merged when consecutive discharge and admission dates were within one calendar day. Finally, hospitalizations that occurred in a non-VA facility were merged. The 30-day readmission rate was calculated at each step of the algorithm to demonstrate the impact. The total number of VA medical hospitalizations in 2009 with live discharges was 505,861. The 30-day readmission rate after adjusting for VA to VA transfers and incorporating non-VA care was 18.3% (95% confidence interval (CI): 18.2, 18.4%). [ABSTRACT FROM AUTHOR]

Background: Readmission rates after pneumonia, heart failure, and acute myocardial infarction hospitalizations are risk-adjusted for age, gender, and medical comorbidities and used to penalize hospitals.Objective: To assess the impact of disability and social determinants of health on condition-specific readmissions beyond current risk adjustment.Design, Setting, and Participants: Retrospective cohort study of Medicare patients using 1) linked Health and Retirement Study-Medicare claims data (HRS-CMS) and 2) Healthcare Cost and Utilization Project State Inpatient Databases (Florida, Washington) linked with ZIP Code-level measures from the Census American Community Survey (ACS-HCUP). Multilevel logistic regression models assessed the impact of disability and selected social determinants of health on readmission beyond current risk adjustment.Main Measures: Outcomes measured were readmissions ≤30 days after hospitalizations for pneumonia, heart failure, or acute myocardial infarction. HRS-CMS models included disability measures (activities of daily living [ADL] limitations, cognitive impairment, nursing home residence, home healthcare use) and social determinants of health (spouse, children, wealth, Medicaid, race). ACS-HCUP model measures were ZIP Code-percentage of residents ≥65 years of age with ADL difficulty, spouse, income, Medicaid, and patient-level and hospital-level race.Key Results: For pneumonia, ≥3 ADL difficulties (OR 1.61, CI 1.079-2.391) and prior home healthcare needs (OR 1.68, CI 1.204-2.355) increased readmission in HRS-CMS models (N = 1631); ADL difficulties (OR 1.20, CI 1.063-1.352) and 'other' race (OR 1.14, CI 1.001-1.301) increased readmission in ACS-HCUP models (N = 27,297). For heart failure, children (OR 0.66, CI 0.437-0.984) and wealth (OR 0.53, CI 0.349-0.787) lowered readmission in HRS-CMS models (N = 2068), while black (OR 1.17, CI 1.056-1.292) and 'other' race (OR 1.14, CI 1.036-1.260) increased readmission in ACS-HCUP models (N = 37,612). For acute myocardial infarction, nursing home status (OR 4.04, CI 1.212-13.440) increased readmission in HRS-CMS models (N = 833); 'other' patient-level race (OR 1.18, CI 1.012-1.385) and hospital-level race (OR 1.06, CI 1.001-1.125) increased readmission in ACS-HCUP models (N = 17,496).Conclusions: Disability and social determinants of health influence readmission risk when added to the current Medicare risk adjustment models, but the effect varies by condition. [ABSTRACT FROM AUTHOR]

Purpose: Hospital-acquired infections with highly resistant organisms are an important problem among critically ill patients. Control of these organisms has largely focused within individual hospitals. We examine the extent to which transfers of critically ill patients could be a vector for the wide spread of highly resistant organisms, and compare the efficiency of different approaches to targeting infection control resources. Methods: We analyzed the network of interhospital transfers of intensive care unit patients in 2005 US Medicare data and 2004-2006 Pennsylvania all-payer data. We simulated the spread of highly resistant hospital-acquired infections by randomly choosing a single hospital to develop a highly resistant organism and following the spread of infection or colonization throughout the network under varying strategies of infection control and varying levels of infectivity. Results: Critical care transfers could spread a highly resistant organism between any two US hospitals in a median of 3 years. Hospitals varied substantially in their importance to limiting potential spread. Targeting resources to a small subset of hospitals on the basis of their position in the transfer network was 16 times more efficient than distributing infection control resources uniformly. Within any set of targeted hospitals, the best strategy for infection control heavily concentrated resources at a few particularly important hospitals, regardless of level of infectivity. Conclusions: Critical care transfers provide a plausible vector for widespread dissemination of highly resistant hospital-acquired microorganisms. Infection control efforts can be made more efficient by selectively targeting hospitals most important for transmission. [ABSTRACT FROM AUTHOR]

Patients requiring prolonged mechanical ventilation (PMV) are a subset of critically ill patients with high resource utilization and poor long-term outcomes. We sought to develop an algorithm for identifying patients receiving PMV, defined as either 14 or 21 days of mechanical ventilation, in administrative and claims data. The algorithm was derived in mechanically ventilated patients at an academic medical center ( n = 1,500) and validated in patients with community-acquired pneumonia in a multi-center clinical registry ( n = 20,370), with further evaluation in the Pennsylvania discharge database ( n = 62,383). The final algorithm combined the International Classification of Diseases codes for mechanical ventilation, diagnosis related groups for ventilation and tracheostomy, and intensive care unit length of stay. In the derivation dataset the algorithm was highly sensitive (14 days = 92.4%; 21 days = 97.6%) and specific (14 days = 91.6%, 21 days = 92.1%). The definition continued to perform well in the validation dataset (14 days: sensitivity = 87.6%, specificity = 88.5%). In both the derivation and validation datasets the negative predictive value was over 95% and positive predictive values ranged from 60% to 70%. In state discharge data the algorithm identified a cohort of patients with high costs and frequent discharge to skilled care facilities. Administrative data can be used to accurately identify populations of patients receiving PMV. [ABSTRACT FROM AUTHOR]

Objective: In several populations, maternal depression has been associated with reduced child safety. In an urban pediatric Emergency Department, we examined the relationship between parental depression, social support, and domestic conflict and child safety behaviors. Methods: We studied consecutive patients in an Emergency Department. Trained interviewers used a structured instrument to assess patient, primary caregiver, and household demographics, socio-economic status, psychosocial factors, child safety behaviors (whether a gun was in the home, poisons were locked, a functioning smoke detector was present, and use of carseats or seatbelts), and whether the home was smoke-free. 1,116 patients provided adequate data. Results: Depression was associated with a modest and not statistically significant reduction in child safety behaviors in this population. Lack of social support and the presence of domestic conflict were robustly, independently, and statistically significantly associated with less safe homes. Domestic conflict was associated with more smoking in the home. Conclusion: In our population, child safety was associated less with depression and more with parental lack of social support and domestic conflict. These can be assessed in a Emergency Department and may be amenable to intervention. [ABSTRACT FROM AUTHOR]

Objective: To understand the role of race, ethnicity, and affluence in elderly patients' use of teaching hospitals when they have that option.Methods: Using a novel data set of 787,587 Medicare patients newly diagnosed with serious illness in 1993, we look at how sociodemographic factors influence whether patients use a teaching hospital for their initial hospitalization for their disease. We use hierarchical linear models to take into account differences in the availability of teaching hospitals to different groups. These models look within groups of people who live in the same county and ask what demographic factors make an individual within that county more or less likely to use a teaching hospital.Results: We find that blacks are much more likely than whites to use teaching hospitals (odds ratio [OR], 1.75; 95% confidence interval [95% CI], 1.73 to 1.77). However, Hispanics and Asian-Americans are less likely to use teaching hospitals than are whites (Hispanic OR, 0.92; 95% CI, 0.88 to 0.97; Asian-American OR, 0.89; 95% CI, 0.82 to 0.97). Medicaid patients are less likely to use teaching hospitals (given their opportunities) than are non-Medicaid recipients (OR, 0.91; 95% CI, 0.90 to 0.92). And we find a curvilinear relationship with affluence, with those in the poorest and those in the wealthiest neighborhoods most likely to use a teaching hospital.Conclusion: The use of teaching hospitals is more complex that heretofore appreciated. Understanding why some groups do not go to teaching hospitals could be important for the health of those groups and of teaching hospitals. [ABSTRACT FROM AUTHOR]

We describe a method for the development of cohorts of up to three quarters of the 14 million married couples aged 65 and over in the United States. The health care experiences, illness histories, and mortality of these identified couples can be assessed longitudinally using Medicare data. We summarize strengths and limitations of using data from Medicare administrative records for the study of marriage, health, and aging. We illustrate the method by demonstrating substantial differences in survival in a cohort of hospice patients as a function of not only the patient's own diagnosis and illness burden but also the patient's spouse's illness burden. [ABSTRACT FROM AUTHOR]

This article presents information on the article "Methodology for Detecting Married Couples in the Medicare Data." It also includes the authors reply to the comments by sociologist Bert Kestenbaum on their article. Kestenbaum emphasizes the heterogeneity of couples detected in the cross-reference file, the authors believe that his projections of total efficacy in detection also bear emphasis. Kestenbaum points out that the rate of increase of detectable couples in the cross-reference file (as continually updated) which lies at the heart of Method 2 is slower than they suggested. In the SSA files, this appears to be true. Nonetheless, at every point within at least the four-year follow-up, the SSA analyses suggest that our methods are more powerful than they originally estimated, at four years" follow-up, they predicted that 55% of couples could be linked, whereas Kestenbaum finds that 73% can be linked. Assuming linear growth of detection rates for the sake of simple discussion, our original estimates of overall detection efficacy for both methods combined are lower than Kestenbaum's estimates through nine years of follow-up. Thus the SSA data suggest that our original estimates of the combined methods performance were conservative.