Heat Waves and Emergency Department Visits Among the Homeless, San Diego, 2012–2019

Objectives. To determine the effect of heat waves on emergency department (ED) visits for individuals experiencing homelessness and explore vulnerability factors. Methods. We used a unique highly detailed data set on sociodemographics of ED visits in San Diego, California, 2012 to 2019. We applied a time-stratified case–crossover design to study the association between various heat wave definitions and ED visits. We compared associations with a similar population not experiencing homelessness using coarsened exact matching. Results. Of the 24 688 individuals identified as experiencing homelessness who visited an ED, most were younger than 65 years (94%) and of non-Hispanic ethnicity (84%), and 14% indicated the need for a psychiatric consultation. Results indicated a positive association, with the strongest risk of ED visits during daytime (e.g., 99th percentile, 2 days) heat waves (odds ratio = 1.29; 95% confidence interval = 1.02, 1.64). Patients experiencing homelessness who were younger or elderly and who required a psychiatric consultation were particularly vulnerable to heat waves. Odds of ED visits were higher for individuals experiencing homelessness after matching to nonhomeless individuals based on age, gender, and race/ethnicity. Conclusions. It is important to prioritize individuals experiencing homelessness in heat action plans and consider vulnerability factors to reduce their burden. (Am J Public Health. 2022;112(1):98–106. https://doi.org/10.2105/AJPH.2021.306557 )

Attrition of Police Officers With Immigrant Background

Recruiting police officers with immigrant backgrounds has a limited effect if many of these recruits leave the police service. The dropout and attrition rates among officers with immigrant backgrounds are also an important indicator of the challenges this group faces when joining the police profession. We investigated police education dropout patterns and attrition rates among police students and officers with immigrant backgrounds. Our study is based on detailed longitudinal data with total coverage of the population, which were previously unavailable for police career studies. Using logistic regression and coarsened exact matching, we analysed administrative registry data covering all individuals admitted to the Norwegian police university college (1995–2010, N = 6570) and all police-educated staff employed in the Norwegian police (1995–2014, N = 7001). Students and police officers with non-Western immigrant backgrounds have a greater tendency to both dropout of education and leave the police service. Prior academic performance can only partly explain these higher educational dropout rates. Female and males with non-Western immigrant backgrounds have similar dropout patterns. Our results provide a rationale for developing policy aimed at not only recruiting, but also retaining police officers with immigrant backgrounds.

In Search of Inspiration: External Mobility and the Emergence of Technology Intrapreneurs

Recent scholarship has established several ways in which external hiring—versus filling a role with a comparable internal candidate—is detrimental to firms. Yet, organizational learning theory suggests that external hires benefit firms: by importing knowledge that is unavailable or obscured to insiders and applying it toward experimentation and risky recombination. Accordingly and consistent with studies of learning by hiring and innovation, we predict that external hires are at greater risk of intrapreneurship than internal hires. We test this prediction via a study of product managers in large technology companies. We use machine learning to operationalize intrapreneurship by comparing product manager job descriptions with the founding statements of venture-backed technology entrepreneurs. Our research design employs coarsened exact matching to balance pretreatment covariates between product managers who arrived at their roles internally versus externally. The results of our analysis indicate that externally hired product managers are substantially more intrapreneurial than observably equivalent internal hires. However, we also find that intrapreneurial product managers have a higher turnover rate, an effect that is primarily driven by external hires. This suggests that hiring for intrapreneurship may be a difficult strategy to sustain.

Return to work: does cardiac rehabilitation make a difference? Danish nationwide register-based study

Aim: to assess whether participation in cardiac rehabilitation affects the probability of returning to work after ischaemic heart disease. Methods: the study population consisted of 24,509 patients (18–70 years of age) discharged from an inpatient admission at a Danish hospital during 2014–2018 and who were working before their admission. Only patients with a percutaneous coronary intervention or coronary artery bypass grafting surgery procedure and ICD-10 codes I20–I25 as their main diagnosis or ICD-10 codes I21, I240, I248 or I249 as secondary diagnosis during an emergency admission were included. Exposure was defined as participation in cardiac rehabilitation ( N = 15,742), and binary indicator of being at work in the last week of a given month were used as primary outcomes. Coarsened exact matching (CEM) of exposed and unexposed patients was used to reduce selection bias. Logistic regression models were applied on the matched population ( N = 15,762). Results: Less deprived and less comorbid patients were more likely to receive cardiac rehabilitation. CEM succeeded in arriving at a population where this selection was reduced and in this population we found that patients who received cardiac rehabilitation had a lower probability of returning to work after 3 months (OR 0.81, 95%CI: 0.77–0.84), a higher but insignificant probability after 6 (OR 1.02, 95%CI: 0.97–1.08), and a higher probability after 9 (OR 1.08, 95%CI: 1.02–1.15) and 12 months (OR 1.20, 95%CI: 1.13–1.28). Conclusions: Deprived and comorbid patients have lower use of cardiac rehabilitation. In a matched population where this bias is reduced, cardiac rehabilitation will increase the probability of returning to work.

Survival after COVID-19-associated organ failure among inpatients with systemic lupus erythematosus in France: a nationwide study

ObjectiveWe analysed the incidence of, the specific outcomes and factors associated with COVID-19-associated organ failure (AOF) in patients with systemic lupus erythematosus (SLE) in France.MethodsWe performed a cohort study using the French national medical/administrative hospital database for the January 2011–November 2020 period. Each patient with SLE diagnosed in a French hospital with a COVID-19-AOF until November 2020 was randomly matched with five non-SLE patients with COVID-19-AOF. We performed an exact matching procedure taking age ±2 years, gender and comorbidities as matching variables. COVID-19-AOF was defined as the combination of at least one code of COVID-19 diagnosis with one code referring to an organ failure diagnosis.ResultsFrom March to November 2020, 127 380 hospital stays in France matched the definition of COVID-19-AOF, out of which 196 corresponded with patients diagnosed with SLE. Based on the presence of comorbidities, we matched 908 non-SLE patients with COVID-19-AOF with 190 SLE patients with COVID-19-AOF. On day 30, 43 in-hospital deaths (22.6%) occurred in SLE patients with COVID-19-AOF vs 198 (21.8%) in matched non-SLE patients with COVID-19-AOF: HR 0.98 (0.71–1.34). Seventy-five patients in the SLE COVID-19-AOF group and 299 in the matched control group were followed up from day 30 to day 90. During this period, 19 in-hospital deaths occurred in the SLE group (25.3%) vs 46 (15.4%) in the matched control group; the HR associated with death occurring after COVID-19-AOF among patients with SLE was 1.83 (1.05–3.20).ConclusionsCOVID-19-AOF is associated with a poor late-onset prognosis among patients with SLE.

Impact of Pre-eclampsia/eclampsia on Hemorrhagic and Ischemic Stroke Risk: A 17 years Follow-up Nationwide Cohort Study

Objective No study ever investigated the long-term risk of stroke in women with pre-eclampsia/eclampsia. The purpose of this study is to explore long-term stroke risks, differentiating subtypes and their time trends. Design Nationwide population-based cohort study Methods Between 2000 and 2017, 1,384,427 pregnant women were registered in the National Health Insurance Research Database in Taiwan. After excluding women with previous stroke history and exact matching with all confounders, 6,053 women with pre-eclampsia/eclampsia and 24,212 controls were recruited. Main Outcome Measures Hemorrhagic and ischemic strokes after child-birth Results Over the 17-year follow-up, the adjusted hazard ratio (aHR) for stroke in women with a history of pre-eclampsia/eclampsia was 2.05 (95% confidence interval, CI = 1.67-2.52, p<0.001). The 17 years overall risks of both ischemic and hemorrhagic stroke were 1.98 and 3.45, respectively (p<0.001). The stroke subtypes, hemorrhagic and ischemic, had different time trend risks, and hemorrhagic stroke risks kept higher than that of ischemic stroke. The ischemic stroke risk peaked during 1-3 years after childbirth (aHR=3.09). The hemorrhagic stroke risk peaked during 3-5 years (aHR=7.49). Conclusions Stroke risk persisted even after decades, for both ischemic and hemorrhagic subtypes. Women with pre-eclampsia/eclampsia history should be aware of the long-term risk of stroke. Tweetable abstract Both ischemic and hemorrhagic stroke risks persisted high even after decades, while their time trend risks were different. Keywords: pre-eclampsia/eclampsia; ischemic stroke; hemorrhagic stroke

The prediction of hospital length of stay using unstructured data

Objective This study aimed to assess the performance improvement for machine learning-based hospital length of stay (LOS) predictions when clinical signs written in text are accounted for and compared to the traditional approach of solely considering structured information such as age, gender and major ICD diagnosis. Methods This study was an observational retrospective cohort study and analyzed patient stays admitted between 1 January to 24 September 2019. For each stay, a patient was admitted through the Emergency Department (ED) and stayed for more than two days in the subsequent service. LOS was predicted using two random forest models. The first included unstructured text extracted from electronic health records (EHRs). A word-embedding algorithm based on UMLS terminology with exact matching restricted to patient-centric affirmation sentences was used to assess the EHR data. The second model was primarily based on structured data in the form of diagnoses coded from the International Classification of Disease 10th Edition (ICD-10) and triage codes (CCMU/GEMSA classifications). Variables common to both models were: age, gender, zip/postal code, LOS in the ED, recent visit flag, assigned patient ward after the ED stay and short-term ED activity. Models were trained on 80% of data and performance was evaluated by accuracy on the remaining 20% test data. Results The model using unstructured data had a 75.0% accuracy compared to 74.1% for the model containing structured data. The two models produced a similar prediction in 86.6% of cases. In a secondary analysis restricted to intensive care patients, the accuracy of both models was also similar (76.3% vs 75.0%). Conclusions LOS prediction using unstructured data had similar accuracy to using structured data and can be considered of use to accurately model LOS.

Phase transition in the bipartite z-matching

We study numerically the maximum z-matching problems on ensembles of bipartite random graphs. The z-matching problems describes the matching between two types of nodes, users and servers, where each server may serve up to z users at the same time. Using a mapping to standard maximum-cardinality matching, and because for the latter there exists a polynomial-time exact algorithm, we can study large system sizes of up to $$10^6$$ 10 6 nodes. We measure the capacity and the energy of the resulting optimum matchings. First, we confirm previous analytical results for bipartite regular graphs. Next, we study the finite-size behaviour of the matching capacity and find the same scaling behaviour as before for standard matching, which indicates the universality of the problem. Finally, we investigate for bipartite Erdős–Rényi random graphs the saturability as a function of the average degree, i.e. whether the network allows as many customers as possible to be served, i.e. exploiting the servers in an optimal way. We find phase transitions between unsaturable and saturable phases. These coincide with a strong change of the running time of the exact matching algorithm, as well with the point where a minimum-degree heuristic algorithm starts to fail. Graphical Abstract

A Method to Improve Exact Matching Results in Compressed Text using Parallel Wavelet Tree

The process of searching on the World Wide Web (WWW) is increasing regularly, and users around the world also use it regularly. In WWW the size of the text corpus is constantly increasing at an exponential rate, so we need an efficient indexing algorithm that reduces both space and time during the search process. This paper proposes a new technique that utilizes Word-Based Tagging Coding compression which is implemented using Parallel Wavelet Tree, called WBTC_PWT. WBTC_PWT uses the word-based tagging coding encoding technique to reduce the space complexity of the index and uses a parallel wavelet tree which reduces the time it takes to construct indexes. This technique utilizes the features of compressed pattern matching to minimize search time complexity. In this technique, all the unique words present in the text corpus are divided into different levels according to the word frequency table and a different wavelet tree is made for each level in parallel. Compared to other existing search algorithms based on compressed text, the proposed WBTC_PWT search method is significantly faster and it reduces the chances of getting the false matching result.

ST-Elevation Myocardial Infarction Among Septic Shock and Coronary Interventions: A National Emergency Database Study

Objective To study coronary interventions and mortality among patients with ST-elevated myocardial infarction (STEMI) who were admitted with septic shock. Methods Data from the national emergency department sample (NEDS) that constitutes 20% sample of hospital-owned emergency departments in the United States was analyzed for the septic shock related visits from 2016 to 2018. Septic shock was defined by the ICD codes. Results Out of 1 375 507 adult septic shock patients, 521 300 had a primary diagnosis of septic shock (mean age 67.41±15.67 years, 51.1% females) in the national emergency database for the years 2016 to 2018. Of these patients, 2768 (0.53%) had STEMI recorded during the hospitalization. Mortality rates for STEMI patients were higher than patients without STEMI (52.3% vs 23.5%). Mortality rates improved with PCI among STEMI patients (43.8% vs 56.2%). Coronary angiography was performed among 16% of patients of which percutaneous coronary intervention (PCI) rates were 7.7% among patients with STEMI septic shock. PCI numerically improved mortality, however, had no significant difference than patients without PCI on multivariate logistic regression and univariate logistic regression post coarsened exact matching of baseline characteristics among STEMI patients. Among the predictors, STEMI was a significant predictor of mortality in septic shock patients (OR 2.87, 95% CI 2.37-3.49; P<.001). Age, peripheral vascular disease, were predominant predictors of mortality in STEMI with septic shock subgroup ( P <.001). Pneumonia was the predominant underlying infection among STEMI (36.4%) and without STEMI group (29.5%). Conclusion STEMI complicating septic shock worsens mortality. PCI and coronary angiography numerically improved mortality, however, had no significant difference from patients without PCI. More research will be needed to improve mortality in such a critically ill subgroup of patients.