Explore the vast range of titles available.

Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date. To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters. This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles. Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile. The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management. The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Current research on hypertension utilizes more than fifty animal models that rely mainly on stable increases in systolic blood pressure. In experimental hypertension, grading or scoring of glomerulopathy in the majority of studies is based on a wide range of opinion-based histological changes that do not necessarily comply with lesional descriptors for glomerular injury that are well-established in clinical pathology. Here, we provide a critical appraisal of experimental hypertensive glomerulopathy with the same approach used to assess hypertensive glomerulopathy in humans. Four hypertensive models with varying pathogenesis were analyzed–chronic angiotensin II infused mice, mice expressing active human renin in the liver (TTRhRen), spontaneously hypertensive rats (SHR), and Goldblatt two-kidney one-clip rats (2K1C). Analysis of glomerulopathy utilized the same criteria applied in humans–hyalinosis, focal segmental glomerulosclerosis (FSGS), ischemic, hypertrophic and solidified glomeruli, or global glomerulosclerosis (GGS). Data from animal models were compared to human reference values. Kidneys in TTRhRen mice, SHR and the nonclipped kidneys in 2K1C rats had no sign of hyalinosis, FSGS or GGS. Glomerulopathy in these groups was limited to variations in mesangial and capillary compartment volumes, with mild increases in collagen deposition. Histopathology in angiotensin II infused mice corresponded to mesangioproliferative glomerulonephritis, but not hypertensive glomerulosclerosis. The number of nephrons was significantly reduced in TTRhRen mice and SHR, but did not correlate with severity of glomerulopathy. The most substantial human-like glomerulosclerotic lesions, including FSGS, ischemic obsolescent glomeruli and GGS, were found in the clipped kidneys of 2K1C rats. The comparison of affected kidneys to healthy control in animals produces lesion values that are numerically impressive but correspond to mild damage if compared to humans. Animal studies should be standardized by employing the criteria and classifications established in human pathology to make experimental and human data fully comparable for comprehensive analysis and model improvements.

It is estimated that vaccinating 50%-70% of school-aged children for influenza can produce population-wide indirect effects. We evaluated a city-wide school-located influenza vaccination (SLIV) intervention that aimed to increase influenza vaccination coverage. The intervention was implemented in ≥95 preschools and elementary schools in northern California from 2014 to 2018. Using a matched cohort design, we estimated intervention impacts on student influenza vaccination coverage, school absenteeism, and community-wide indirect effects on laboratory-confirmed influenza hospitalizations. We used a multivariate matching algorithm to identify a nearby comparison school district with pre-intervention characteristics similar to those of the intervention school district and matched schools in each district. To measure student influenza vaccination, we conducted cross-sectional surveys of student caregivers in 22 school pairs (2017 survey, N = 6,070; 2018 survey, N = 6,507). We estimated the incidence of laboratory-confirmed influenza hospitalization from 2011 to 2018 using surveillance data from school district zip codes. We analyzed student absenteeism data from 2011 to 2018 from each district (N = 42,487,816 student-days). To account for pre-intervention differences between districts, we estimated difference-in-differences (DID) in influenza hospitalization incidence and absenteeism rates using generalized linear and log-linear models with a population offset for incidence outcomes. Prior to the SLIV intervention, the median household income was $51,849 in the intervention site and $61,596 in the comparison site. The population in each site was predominately white (41% in the intervention site, 48% in the comparison site) and/or of Hispanic or Latino ethnicity (26% in the intervention site, 33% in the comparison site). The number of students vaccinated by the SLIV intervention ranged from 7,502 to 10,106 (22%-28% of eligible students) each year. During the intervention, influenza vaccination coverage among elementary students was 53%-66% in the comparison district. Coverage was similar between the intervention and comparison districts in influenza seasons 2014-2015 and 2015-2016 and was significantly higher in the intervention site in seasons 2016-2017 (7%; 95% CI 4, 11; p < 0.001) and 2017-2018 (11%; 95% CI 7, 15; p < 0.001). During seasons when vaccination coverage was higher among intervention schools and the vaccine was moderately effective, there was evidence of statistically significant indirect effects: The DID in the incidence of influenza hospitalization per 100,000 in the intervention versus comparison site was -17 (95% CI -30, -4; p = 0.008) in 2016-2017 and -37 (95% CI -54, -19; p < 0.001) in 2017-2018 among non-elementary-school-aged individuals and -73 (95% CI -147, 1; p = 0.054) in 2016-2017 and -160 (95% CI -267, -53; p = 0.004) in 2017-2018 among adults 65 years or older. The DID in illness-related school absences per 100 school days during the influenza season was -0.63 (95% CI -1.14, -0.13; p = 0.014) in 2016-2017 and -0.80 (95% CI -1.28, -0.31; p = 0.001) in 2017-2018. Limitations of this study include the use of an observational design, which may be subject to unmeasured confounding, and caregiver-reported vaccination status, which is subject to poor recall and low response rates. A city-wide SLIV intervention in a large, diverse urban population was associated with a decrease in the incidence of laboratory-confirmed influenza hospitalization in all age groups and a decrease in illness-specific school absence rate among students in 2016-2017 and 2017-2018, seasons when the vaccine was moderately effective, suggesting that the intervention produced indirect effects. Our findings suggest that in populations with moderately high background levels of influenza vaccination coverage, SLIV programs are associated with further increases in coverage and reduced influenza across the community.

Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as \"admitted with COVID-19\" (incidental) versus specifically admitted for COVID-19 (\"for COVID-19\"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Ecosystem-based management is logistically and politically challenging because ecosystems are inherently complex and management decisions affect a multitude of groups. Coastal ecosystems, which lie at the interface between marine and terrestrial ecosystems and provide an array of ecosystem services to different groups, aptly illustrate these challenges. Successful ecosystem-based management of coastal ecosystems requires incorporating scientific information and the knowledge and views of interested parties into the decision-making process. Estimating the provision of ecosystem services under alternative management schemes offers a systematic way to incorporate biogeophysical and socioeconomic information and the views of individuals and groups in the policy and management process. Employing ecosystem services as a common language to improve the process of ecosystem-based management presents both benefits and difficulties. Benefits include a transparent method for assessing trade-offs associated with management alternatives, a common set of facts and common currency on which to base negotiations, and improved communication among groups with competing interests or differing worldviews. Yet challenges to this approach remain, including predicting how human interventions will affect ecosystems, how such changes will affect the provision of ecosystem services, and how changes in service provision will affect the welfare of different groups in society. In a case study from Puget Sound, Washington, we illustrate the potential of applying ecosystem services as a common language for ecosystem-based management.

Risk of U.S. Army soldier suicide-related behaviors increases substantially after separation from service. As universal prevention programs have been unable to resolve this problem, a previously reported machine learning model was developed using pre-separation predictors to target high-risk transitioning service members (TSMs) for more intensive interventions. This model is currently being used in a demonstration project. The model is limited, though, in two ways. First, the model was developed and trained in a relatively small cross-validation sample ( n  = 4044) and would likely be improved if a larger sample was available. Second, the model provides no guidance on subtyping high-risk TSMs. This report presents results of an attempt to refine the model to address these limitations by re-estimating the model in a larger sample ( n  = 5909) and attempting to develop embedded models for differential risk of post-separation stressful life events (SLEs) known to mediate the association of model predictions with post-separation nonfatal suicide attempts (SAs; n  = 4957). Analysis used data from the Army STARRS Longitudinal Surveys. The revised model improved prediction of post-separation SAs in the first year (AUC = 0.85) and second-third years (AUC = 0.77) after separation, but embedded models could not predict post-separation SLEs with enough accuracy to support intervention targeting.

Introduction The scientific study of racism as a root cause of health inequities has been hampered by the policies and practices of medical journals. Monitoring the discourse around racism and health inequities (i.e., racism narratives) in scientific publications is a critical aspect of understanding, confronting, and ultimately dismantling racism in medicine. A conceptual framework and multi-level construct is needed to evaluate the changes in the prevalence and composition of racism over time and across journals. Objective To develop a framework for classifying racism narratives in scientific medical journals. Methods We constructed an initial set of racism narratives based on an exploratory literature search. Using a computational grounded theory approach, we analyzed a targeted sample of 31 articles in four top medical journals which mentioned the word ‘racism’. We compiled and evaluated 80 excerpts of text that illustrate racism narratives. Two coders grouped and ordered the excerpts, iteratively revising and refining racism narratives. Results We developed a qualitative framework of racism narratives, ordered on an anti-racism spectrum from impeding anti-racism to strong anti-racism, consisting of 4 broad categories and 12 granular modalities for classifying racism narratives. The broad narratives were “dismissal,” “person-level,” “societal,” and “actionable.” Granular modalities further specified how race-related health differences were related to racism (e.g., natural, aberrant, or structurally modifiable). We curated a “reference set” of example sentences to empirically ground each label. Conclusion We demonstrated racism narratives of dismissal, person-level, societal, and actionable explanations within influential medical articles. Our framework can help clinicians, researchers, and educators gain insight into which narratives have been used to describe the causes of racial and ethnic health inequities, and to evaluate medical literature more critically. This work is a first step towards monitoring racism narratives over time, which can more clearly expose the limits of how the medical community has come to understand the root causes of health inequities. This is a fundamental aspect of medicine’s long-term trajectory towards racial justice and health equity.

Remodeling of spatially heterogeneous arterial trees is routinely quantified on tissue sections by averaging linear dimensions, with lack of comparison between different organs and models. The impact of experimental models or hypertension treatment modalities on organ-specific vascular remodeling remains undefined. A wide variety of arterial remodeling types has been demonstrated for hypertensive models, which include differences across organs. The purpose of this study was to reassess methods for measurement of arterial remodeling and to establish a morphometric algorithm for standard and comparable quantification of vascular remodeling in hypertension in different vascular beds. We performed a novel and comprehensive morphometric analysis of terminal arteries in the brain, heart, lung, liver, kidney, spleen, stomach, intestine, skin, skeletal muscle, and adrenal glands of control and Goldblatt hypertensive rats on routinely processed tissue sections. Mean dimensions were highly variable but grouping them into sequential 5 μm intervals permitted creation of reliable linear regression equations and complex profiles. Averaged arterial dimensions demonstrated seven remodeling patterns that were distinct from conventional inward-outward and hypertrophic-eutrophic definitions. Numerical modeling predicted at least nineteen variants of arterial spatial conformations. Recognition of remodeling variants was not possible using averaged dimensions, their ratios, or the remodeling and growth indices. To distinguish remodeling patterns, a three-dimensional modeling was established and tested. The proposed algorithm permits quantitative analysis of arterial remodeling in different organs and may be applicable for comparative studies between animal hypertensive models and human hypertension. Arterial wall tapering is the most important factor to consider in arterial morphometry, while perfusion fixation with vessel relaxation is not necessary. Terminal arteries in organs undergo the same remodeling pattern in Goldblatt rats, except for organs with hemodynamics affected by the arterial clip. The existing remodeling nomenclature should be replaced by a numerical classification applicable to any type of arterial remodeling.

Angiotensin-converting enzyme 2 (ACE2) is expressed in the kidney and may be a renoprotective enzyme, since it converts angiotensin (Ang) II to Ang-(1-7). ACE2 has been detected in urine from patients with chronic kidney disease. We measured urinary ACE2 activity and protein levels in renal transplant patients (age 54 yrs, 65% male, 38% diabetes, n = 100) and healthy controls (age 45 yrs, 26% male, n = 50), and determined factors associated with elevated urinary ACE2 in the patients. Urine from transplant subjects was also assayed for ACE mRNA and protein. No subjects were taking inhibitors of the renin-angiotensin system. Urinary ACE2 levels were significantly higher in transplant patients compared to controls (p = 0.003 for ACE2 activity, and p≤0.001 for ACE2 protein by ELISA or western analysis). Transplant patients with diabetes mellitus had significantly increased urinary ACE2 activity and protein levels compared to non-diabetics (p<0.001), while ACE2 mRNA levels did not differ. Urinary ACE activity and protein were significantly increased in diabetic transplant subjects, while ACE mRNA levels did not differ from non-diabetic subjects. After adjusting for confounding variables, diabetes was significantly associated with urinary ACE2 activity (p = 0.003) and protein levels (p<0.001), while female gender was associated with urinary mRNA levels for both ACE2 and ACE. These data indicate that urinary ACE2 is increased in renal transplant recipients with diabetes, possibly due to increased shedding from tubular cells. Urinary ACE2 could be a marker of renal renin-angiotensin system activation in these patients.