Youth Mental Health Outcomes up to Two Years After SARS-CoV-2 Infection Long-COVID or Long-Pandemic Syndrome: A Retrospective Cohort Study.

PURPOSE: Youth mental distress has substantially increased during the COVID-19 pandemic. However, it is unclear if mental symptoms are directly related to SARS-CoV-2 infection or to social restrictions. We aimed to investigate mental health outcomes in infected versus uninfected adolescents, for up to two years after an index polymerase chain reaction (PCR) test. METHODS: A retrospective cohort study, based on electronic health records from a large nationally representative Israeli health fund, among adolescents aged 12-17 years with a PCR test for SARS-CoV-2 between March 1, 2020 and March 1, 2021. Infected and uninfected individuals were matched by age, sex, test date, sector, and socioeconomic status. Cox regression was used to derive hazard ratios (HRs) for mental health outcomes within two years from PCR test for infected versus uninfected individuals, while accounting for pre-existing psychiatric history. External validation was performed on UK primary care data. RESULTS: Among 146,067 PCR-tested adolescents, 24,009 were positive and 22,354 were matched with negative adolescents. SARS-CoV-2 infection was significantly associated with reduced risks for dispensation of antidepressants (HR 0.74, 95% confidence interval [CI] 0.66-0.83), diagnoses of anxiety (HR 0.82, 95% CI 0.71-0.95), depression (HR 0.65, 95% CI 0.53-0.80), and stress (HR 0.80, 95% CI 0.69-0.92). Similar results were obtained in the validation dataset. DISCUSSION: This large, population-based study suggests that SARS-CoV-2 infection is not associated with elevated risk for mental distress in adolescents. Our findings highlight the importance of taking a holistic view on adolescents' mental health during the pandemic, with consideration of both SARS-CoV-2 infection and response measures.

Data-Driven Assessment of Adolescents' Mental Health During the COVID-19 Pandemic.

OBJECTIVE: Adolescents' mental health was severely compromised during the COVID-19 pandemic. Longitudinal real-world studies on changes in the mental health of adolescents during the later phase of the pandemic are limited. We aimed to quantify the effect of COVID-19 pandemic on adolescents' mental health outcomes based on electronic health records. METHOD: This was a retrospective cohort study using the computerized database of a 2.5 million members, state-mandated health organization in Israel. Rates of mental health diagnoses and psychiatric drug dispensations were measured among adolescents 12 to 17 years of age with and without pre-existing mental history, for the years 2017 to 2021. Relative risks were computed between the years, and interrupted time series (ITS) analyses evaluated changes in monthly incidence rates of psychiatric outcomes. RESULTS: The average population size was 218,146 in 2021. During the COVID-19 period, a 36% increase was observed in the incidence of depression (95% CI = 25-47), 31% in anxiety (95% CI = 23-39), 20% in stress (95% CI = 13-27), 50% in eating disorders (95% CI = 35-67), 25% in antidepressant use (95% CI = 25-33), and 28% in antipsychotic use (95% CI = 18-40). A decreased rate of 26% (95% CI = 0.80-0.88) was observed in ADHD diagnoses. The increase of the examined outcomes was most prominent among youth without psychiatric history, female youth, general secular Jewish population, youth with medium-high socioeconomic status, and those 14 to 15 years of age. ITS analysis confirmed a significantly higher growth in the incidence of psychiatric outcomes during the COVID-19 period, compared to those in previous years. CONCLUSION: This real-world study highlights the deterioration of adolescents' mental health during the COVID-19 pandemic and suggests that youth mental health should be considered during health policy decision making. DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We actively worked to promote sex and gender balance in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Long covid outcomes at one year after mild SARS-CoV-2 infection: nationwide cohort study.

OBJECTIVES: To determine the clinical sequelae of long covid for a year after infection in patients with mild disease and to evaluate its association with age, sex, SARS-CoV-2 variants, and vaccination status. DESIGN: Retrospective nationwide cohort study. SETTING: Electronic medical records from an Israeli nationwide healthcare organisation. POPULATION: 1 913 234 Maccabi Healthcare Services members of all ages who did a polymerase chain reaction test for SARS-CoV-2 between 1 March 2020 and 1 October 2021. MAIN OUTCOME MEASURES: Risk of an evidence based list of 70 reported long covid outcomes in unvaccinated patients infected with SARS-CoV-2 matched to uninfected people, adjusted for age and sex and stratified by SARS-CoV-2 variants, and risk in patients with a breakthrough SARS-CoV-2 infection compared with unvaccinated infected controls. Risks were compared using hazard ratios and risk differences per 10 000 patients measured during the early (30-180 days) and late (180-360 days) time periods after infection. RESULTS: Covid-19 infection was significantly associated with increased risks in early and late periods for anosmia and dysgeusia (hazard ratio 4.59 (95% confidence interval 3.63 to 5.80), risk difference 19.6 (95% confidence interval 16.9 to 22.4) in early period; 2.96 (2.29 to 3.82), 11.0 (8.5 to 13.6) in late period), cognitive impairment (1.85 (1.58 to 2.17), 12.8, (9.6 to 16.1); 1.69 (1.45 to 1.96), 13.3 (9.4 to 17.3)), dyspnoea (1.79 (1.68 to 1.90), 85.7 (76.9 to 94.5); 1.30 (1.22 to 1.38), 35.4 (26.3 to 44.6)), weakness (1.78 (1.69 to 1.88), 108.5, 98.4 to 118.6; 1.30 (1.22 to 1.37), 50.2 (39.4 to 61.1)), and palpitations (1.49 (1.35 to 1.64), 22.1 (16.8 to 27.4); 1.16 (1.05 to 1.27), 8.3 (2.4 to 14.1)) and with significant but lower excess risk for streptococcal tonsillitis and dizziness. Hair loss, chest pain, cough, myalgia, and respiratory disorders were significantly increased only during the early phase. Male and female patients showed minor differences, and children had fewer outcomes than adults during the early phase of covid-19, which mostly resolved in the late period. Findings remained consistent across SARS-CoV-2 variants. Vaccinated patients with a breakthrough SARS-CoV-2 infection had a lower risk for dyspnoea and similar risk for other outcomes compared with unvaccinated infected patients. CONCLUSIONS: This nationwide study suggests that patients with mild covid-19 are at risk for a small number of health outcomes, most of which are resolved within a year from diagnosis.

Risk for Poor Post-Operative Quality of Life Among Wearable Use Subgroups in an All of Us Research Cohort.

The objective of this research was to build and assess the performance of a prediction model for post-operative recovery status measured by quality of life among individuals experiencing a variety of surgery types. In addition, we assessed the performance of the model for two subgroups (high and moderately consistent wearable device users). Study variables were derived from the electronic health records, questionnaires, and wearable devices of a cohort of individuals with one of 8 surgery types and that were part of the NIH All of Us research program. Through multivariable analysis, high frailty index (OR 1.69, 95% 1.05-7.22, p<0.006), and older age (OR 1.76, 95% 1.55-4.08, p<0.024) were found to be the driving risk factors of poor recovery post-surgery. Our logistic regression model included 15 variables, 5 of which included wearable device data. In wearable use subgroups, the model had better accuracy for high wearable users (81%). Findings demonstrate the potential for models that use wearable measures to assess frailty to inform clinicians of patients at risk for poor surgical outcomes. Our model performed with high accuracy across multiple surgery types and were robust to variable consistency in wearable use.

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study.

BACKGROUND: Crowdsourcing is a useful way to rapidly collect information on COVID-19 symptoms. However, there are potential biases and data quality issues given the population that chooses to participate in crowdsourcing activities and the common strategies used to screen participants based on their previous experience. OBJECTIVE: The study aimed to (1) build a pipeline to enable data quality and population representation checks in a pilot setting prior to deploying a final survey to a crowdsourcing platform, (2) assess COVID-19 symptomology among survey respondents who report a previous positive COVID-19 result, and (3) assess associations of symptomology groups and underlying chronic conditions with adverse outcomes due to COVID-19. METHODS: We developed a web-based survey and hosted it on the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We conducted a pilot study from August 5, 2020, to August 14, 2020, to refine the filtering criteria according to our needs before finalizing the pipeline. The final survey was posted from late August to December 31, 2020. Hierarchical cluster analyses were performed to identify COVID-19 symptomology groups, and logistic regression analyses were performed for hospitalization and mechanical ventilation outcomes. Finally, we performed a validation of study outcomes by comparing our findings to those reported in previous systematic reviews. RESULTS: The crowdsourcing pipeline facilitated piloting our survey study and revising the filtering criteria to target specific MTurk experience levels and to include a second attention check. We collected data from 1254 COVID-19-positive survey participants and identified the following 6 symptomology groups: abdominal and bladder pain (Group 1); flu-like symptoms (loss of smell/taste/appetite; Group 2); hoarseness and sputum production (Group 3); joint aches and stomach cramps (Group 4); eye or skin dryness and vomiting (Group 5); and no symptoms (Group 6). The risk factors for adverse COVID-19 outcomes differed for different symptomology groups. The only risk factor that remained significant across 4 symptomology groups was influenza vaccine in the previous year (Group 1: odds ratio [OR] 6.22, 95% CI 2.32-17.92; Group 2: OR 2.35, 95% CI 1.74-3.18; Group 3: OR 3.7, 95% CI 1.32-10.98; Group 4: OR 4.44, 95% CI 1.53-14.49). Our findings regarding the symptoms of abdominal pain, cough, fever, fatigue, shortness of breath, and vomiting as risk factors for COVID-19 adverse outcomes were concordant with the findings of other researchers. Some high-risk symptoms found in our study, including bladder pain, dry eyes or skin, and loss of appetite, were reported less frequently by other researchers and were not considered previously in relation to COVID-19 adverse outcomes. CONCLUSIONS: We demonstrated that a crowdsourced approach was effective for collecting data to assess symptomology associated with COVID-19. Such a strategy may facilitate efficient assessments in a dynamic intersection between emerging infectious diseases, and societal and environmental changes.

Third-generation cephalosporin resistant Enterobacteriaceae in neonates and young infants: impact and outcome.

OBJECTIVE: Third-generation cephalosporins resistant Enterobacteriaceae (3GCR-EB) are a major threat in severely ill neonates hospitalized in Neonatal Intensive Care Units. Still, the particular impact of 3GCR-EB on outcomes in the wide neonatal population is not well-appreciated. We aimed to study the impact of 3GCR-EB on the length of hospital stay and mortality of a general population of neonates and young infants. STUDY DESIGN: This was a retrospective cohort study of neonates and young infants born in eight Israeli hospitals between 2009 and 2013, with a culture taken within three months after birth that tested positive for Enterobacteriaceae (EB). Data for this study were taken from centralized electronic health records included inpatient, outpatient, socio-demographic, administrative and laboratory information. The main outcomes were length of stay and mortality. The main explanatory variable was an isolation of 3GCR-EB in any bacterial culture taken from a neonate or young infant. RESULTS: Cultures were taken for 31,921 neonates and young infants; 2647 (8.3%) tested positive for EB and 290 (11%) tested positive for 3GCR-EB. Length of stay for those who tested positive was 2.8 times longer (95%CI: 2.70-2.91, p ˂ .001) than patients who tested positive for 3GC-susceptible EB. 3GCR-EB were also associated with increased mortality (OR: 12.06, 95%CI: 4.92-32.29). CONCLUSIONS: Neonates with third-generation cephalosporins resistant Enterobacteriaceae had extended hospitalization and increased mortality, which was mostly significant in normal gestational weight newborns.

Identification of elderly patients at risk for 30-day readmission: Clinical insight beyond big data prediction.

AIM: This study explores the potential benefit of combining clinicians' risk assessments and the automated 30-day readmission prediction model. BACKGROUND: Automated readmission prediction models based on electronic health records are increasingly applied as part of prevention efforts, but their accuracy is moderate. METHODS: This prospective multisource study was based on self-reported surveys of clinicians and data from electronic health records. The survey was performed at 15 internal medicine wards of three general Clalit hospitals between May 2016 and June 2017. We examined the degree of concordance between the Preadmission Readmission Detection Model, clinicians' readmission risk classification and the likelihood of actual readmission. Decision trees were developed to classify patients by readmission risk. RESULTS: A total of 694 surveys were collected for 371 patients. The disagreement between clinicians' risk assessment and the model was 34.5% for nurses and 33.5% for physicians. The decision tree algorithms identified 22% and 9% (based on nurses and physicians, respectively) of the model's low-medium-risk patients as high risk (accuracy 0.8 and 0.76, respectively). CONCLUSIONS: Combining the Readmission Model with clinical insight improves the ability to identify high-risk elderly patients. IMPLICATIONS FOR NURSING MANAGEMENT: This study provides algorithms for the decision-making process for selecting high-risk readmission patients based on nurses' evaluations.

Recurrent preterm birth risk assessment for two delivery subtypes: A multivariable analysis.

OBJECTIVE: The study sought to develop and apply a framework that uses a clinical phenotyping tool to assess risk for recurrent preterm birth. MATERIALS AND METHODS: We extended an existing clinical phenotyping tool and applied a 4-step framework for our retrospective cohort study. The study was based on data collected in the Genomic and Proteomic Network for Preterm Birth Research Longitudinal Cohort Study (GPN-PBR LS). A total of 52 sociodemographic, clinical and obstetric history-related risk factors were selected for the analysis. Spontaneous and indicated delivery subtypes were analyzed both individually and in combination. Chi-square analysis and Kaplan-Meier estimate were used for univariate analysis. A Cox proportional hazards model was used for multivariable analysis. RESULTS: : A total of 428 women with a history of spontaneous preterm birth qualified for our analysis. The predictors of preterm delivery used in multivariable model were maternal age, maternal race, household income, marital status, previous caesarean section, number of previous deliveries, number of previous abortions, previous birth weight, cervical insufficiency, decidual hemorrhage, and placental dysfunction. The models stratified by delivery subtype performed better than the naïve model (concordance 0.76 for the spontaneous model, 0.87 for the indicated model, and 0.72 for the naïve model). DISCUSSION: The proposed 4-step framework is effective to analyze risk factors for recurrent preterm birth in a retrospective cohort and possesses practical features for future analyses with other data sources (eg, electronic health record data). CONCLUSIONS: We developed an analytical framework that utilizes a clinical phenotyping tool and performed a survival analysis to analyze risk for recurrent preterm birth.

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study.

BACKGROUND: Wearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices. OBJECTIVE: This study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data. METHODS: This was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses. RESULTS: A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income. CONCLUSIONS: Most wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).

Preventing Hospital Readmissions: Healthcare Providers' Perspectives on "Impactibility" Beyond EHR 30-Day Readmission Risk Prediction.

BACKGROUND: Predictive models based on electronic health records (EHRs) are used to identify patients at high risk for 30-day hospital readmission. However, these models' ability to accurately detect who could benefit from inclusion in prevention interventions, also termed "perceived impactibility", has yet to be realized. OBJECTIVE: We aimed to explore healthcare providers' perspectives of patient characteristics associated with decisions about which patients should be referred to readmission prevention programs (RPPs) beyond the EHR preadmission readmission detection model (PREADM). DESIGN: This cross-sectional study employed a multi-source mixed-method design, combining EHR data with nurses' and physicians' self-reported surveys from 15 internal medicine units in three general hospitals in Israel between May 2016 and June 2017, using a mini-Delphi approach. PARTICIPANTS: Nurses and physicians were asked to provide information about patients 65 years or older who were hospitalized at least one night. MAIN MEASURES: We performed a decision-tree analysis to identify characteristics for consideration when deciding whether a patient should be included in an RPP. KEY RESULTS: We collected 817 questionnaires on 435 patients. PREADM score and RPP inclusion were congruent in 65% of patients, whereas 19% had a high PREADM score but were not referred to an RPP, and 16% had a low-medium PREADM score but were referred to an RPP. The decision-tree analysis identified five patient characteristics that were statistically associated with RPP referral: high PREADM score, eligibility for a nursing home, having a condition not under control, need for social-services support, and need for special equipment at home. CONCLUSIONS: Our study provides empirical evidence for the partial congruence between classifications of a high PREADM score and perceived impactibility. Findings emphasize the need for additional research to understand the extent to which combining EHR data with provider insights leads to better selection of patients for RPP inclusion.

Risk factors for third-generation cephalosporin resistant Enterobacteriaceae in gestational urine cultures: A retrospective cohort study based on centralized electronic health records.

Third-generation-cephalosporin resistant Enterobacteriaceae (3GCR-EB) carriage in pregnant women poses challenges for infection control and therapeutic decisions. The factors associated with multidrug resistant Enterobacteriaceae carriage in the gestational period are not well documented. The aim of our study was to identify risk factors associated with 3GCR-EB isolation in gestational urine cultures. The study was designed as retrospective cohort based on centralized electronic health records database. Women delivered in Clalit Health Services hospitals in Israel in 2009-2013 and provided urine culture(s) during pregnancy were included. Multivariable analysis using the Generalized Estimating Equations model was used to assess risk factors for 3GCR-EB isolation in gestational urine cultures. The study included 15,282 pregnant women with urine cultures yielding Enterobacteriaceae (EB). The proportion of 3GCR-EB in EB isolates was 3.9% (n = 603). The following risk factors were associated with 3GCR-EB isolation: multiple hospital admissions during the year before delivery (OR,1.47;95% CI,1.21-1.79), assisted fertilization procedure (OR,1.53; 95% CI,1.12-2.10), Arab ethnicity (OR,1.22; 95% CI,1.03-1.45), multiple antibiotic courses (OR,1.76; 95% CI,1.29-2.40), specifically, cephalosporins (OR,1.56; 95% CI,1.26-1.95), fluoroquinolones (OR,1.34; 95% CI,1.04-1.74), or nitrofurantoin (OR,1.29; 95% CI,1.02-1.64). The risk factors identified by this study for 3GCR-EB in gestation, can be easily generalized for pregnant women in the Israeli population. Moreover, these risk factors, other than ethnicity, are applicable to pregnant women worldwide. The information of previous antibiotic treatments, hospitalization in the last year and assisted fertilization procedure can be easily accessed and used for appropriate infection control practices and antimicrobial therapy.

Identifying patients at highest-risk: the best timing to apply a readmission predictive model.

BACKGROUND: Most of readmission prediction models are implemented at the time of patient discharge. However, interventions which include an early in-hospital component are critical in reducing readmissions and improving patient outcomes. Thus, at-discharge high-risk identification may be too late for effective intervention. Nonetheless, the tradeoff between early versus at-discharge prediction and the optimal timing of the risk prediction model application remains to be determined. We examined a high-risk patient selection process with readmission prediction models using data available at two time points: at admission and at the time of hospital discharge. METHODS: An historical prospective study of hospitalized adults (≥65 years) discharged alive from internal medicine units in Clalit's (the largest integrated payer-provider health fund in Israel) general hospitals in 2015. The outcome was all-cause 30-day emergency readmissions to any internal medicine ward at any hospital. We used the previously validated Preadmission Readmission Detection Model (PREADM) and developed a new model incorporating PREADM with hospital data (PREADM-H). We compared the percentage of overlap between the models and calculated the positive predictive value (PPV) for the subgroups identified by each model separately and by both models. RESULTS: The final cohort included 35,156 index hospital admissions. The PREADM-H model included 17 variables with a C-statistic of 0.68 (95% CI: 0.67-0.70) and PPV of 43.0% in the highest-risk categories. Of patients categorized by the PREADM-H in the highest-risk decile, 78% were classified similarly by the PREADM. The 22% (n = 229) classified by the PREADM-H at the highest decile, but not by the PREADM, had a PPV of 37%. Conversely, those classified by the PREADM into the highest decile but not by the PREADM-H (n = 218) had a PPV of 31%. CONCLUSIONS: The timing of readmission risk prediction makes a difference in terms of the population identified at each prediction time point - at-admission or at-discharge. Our findings suggest that readmission risk identification should incorporate a two time-point approach in which preadmission data is used to identify high-risk patients as early as possible during the index admission and an "all-hospital" model is applied at discharge to identify those that incur risk during the hospital stay.

Prediction Accuracy With Electronic Medical Records Versus Administrative Claims.

OBJECTIVE: The objective of this study was to evaluate the incremental predictive power of electronic medical record (EMR) data, relative to the information available in more easily accessible and standardized insurance claims data. DATA AND METHODS: Using both EMR and Claims data, we predicted outcomes for 118,510 patients with 144,966 hospitalizations in 8 hospitals, using widely used prediction models. We use cross-validation to prevent overfitting and tested predictive performance on separate data that were not used for model training. MAIN OUTCOMES: We predict 4 binary outcomes: length of stay (≥7 d), death during the index admission, 30-day readmission, and 1-year mortality. RESULTS: We achieve nearly the same prediction accuracy using both EMR and claims data relative to using claims data alone in predicting 30-day readmissions [area under the receiver operating characteristic curve (AUC): 0.698 vs. 0.711; positive predictive value (PPV) at top 10% of predicted risk: 37.2% vs. 35.7%], and 1-year mortality (AUC: 0.902 vs. 0.912; PPV: 64.6% vs. 57.6%). EMR data, especially from the first 2 days of the index admission, substantially improved prediction of length of stay (AUC: 0.786 vs. 0.837; PPV: 58.9% vs. 55.5%) and inpatient mortality (AUC: 0.897 vs. 0.950; PPV: 24.3% vs. 14.0%). Results were similar for sensitivity, specificity, and negative predictive value across alternative cutoffs and for using alternative types of predictive models. CONCLUSION: EMR data are useful in predicting short-term outcomes. However, their incremental value for predicting longer-term outcomes is smaller. Therefore, for interventions that are based on long-term predictions, using more broadly available claims data is equally effective.

Patients' ratings of the in-hospital discharge briefing and post-discharge primary care follow-up: The association with 30-day readmissions.

OBJECTIVE: We examined whether patients' ratings of their in-hospital discharge briefing and their post-discharge Primary Care Physicians' (PCP) review of the discharge summary are associated with 30-day readmissions. METHODS: A prospective study of 594 internal-medicine patients at a tertiary medical-center in Israel. The in-hospital baseline questionnaire included sociodemographic characteristics, physical, mental, and functional health status. Patients were surveyed by phone about the discharge and post-discharge processes. Clinical data and health-service use was retrieved from a central data-warehouse. Multivariate regressions modeled the relationship between in-hospital baseline characteristics, discharge briefing, PCP visit indicator, the PCP discharge summary review, and 30-day readmissions. RESULTS: The extent of the PCPs' review of the hospital discharge summary at the post-discharge visit was rated higher than the in-hospital discharge briefing (3.46 vs. 3.17, p = 0.001) and was associated with lower odds of readmission (OR=0.35, 95% CI 0.26-0.45). The model that included this assessment performed better than the in-hospital baseline, the in-hospital discharge-briefing, and the PCP visit models (C-statistic = 0.87, compared with: 0.70, 0.81, 0.81, respectively). CONCLUSIONS: Providing extensive post-discharge explanations by PCPs serves as a significant protective factor against readmissions. PRACTICE IMPLICATIONS: PCPs should be encouraged to thoroughly review the discharge summary letter with the patient.

Population-based cohort of 500 patients with Gaucher disease in Israel.

OBJECTIVE: To characterise a population-based cohort of patients with Gaucher disease (GD) in Israel relative to the general population and describe sociodemographic and clinical differences by disease severity (ie, enzyme replacement therapy [ERT] use). DESIGN: A cross-sectional study was conducted. SETTING: Data from the Clalit Health Services electronic health record (EHR) database were used. PARTICIPANTS: The study population included all patients in the Clalit EHR database identified as having GD as of 30 June 2014. RESULTS: A total of 500 patients with GD were identified and assessed. The majority were ≥18 years of age (90.6%), female (54.0%), Jewish (93.6%) and 34.8% had high socioeconomic status, compared with 19.0% in the general Clalit population. Over half of patients with GD with available data (51.0%) were overweight/obese and 63.5% had a Charlson Comorbidity Index ≥1, compared with 46.6% and 30.4%, respectively, in the general Clalit population. The majority of patients with GD had a history of anaemia (69.6%) or thrombocytopaenia (62.0%), 40.4% had a history of bone events and 22.2% had a history of cancer. Overall, 41.2% had received ERT. CONCLUSIONS: Establishing a population-based cohort of patients with GD is essential to understanding disease progression and management. In this study, we highlight the need for physicians to monitor patients with GD regardless of their ERT status.

Cancer prevalence in Israeli men and women with schizophrenia.

The aim of this cross-sectional study was to compare cancer prevalence rates among patients with schizophrenia to those of the non-schizophrenia population. The study population included members of Clalit Health Services aged 25 to 74 years and all data was taken from patients' electronic health records. Of the 2,060,314 members who were included in the study, 32,748 had a diagnosis of schizophrenia. Cancer prevalence rates in women with and without schizophrenia were 491 per 10,000 and 439 per 10,000, respectively; in men, cancer prevalence rates were 226 per 10,000 and 296 per 10,000, respectively. The age-adjusted prevalence rate of all-type cancer was significantly lower among men with schizophrenia, compared to men without schizophrenia; specifically, men with schizophrenia had a lower rate of prostate cancer, and of cancers in the "other" category, compared to men without schizophrenia. Reduced cancer rates in men with schizophrenia may reflect under-diagnosis of some cancer types, likely due to insufficient medical attention. An effort to improve screening regimes should be made.

Functional status before and during acute hospitalization and readmission risk identification.

BACKGROUND: Recent efforts to prevent readmissions are increasingly focusing on early identification of high-risk patients. OBJECTIVE: To test whether information on functioning during hospitalization contributes to the ability to accurately identify older adults at high risk of readmission beyond their baseline risk. DESIGN: Prospective cohort study. SETTING: Internal medicine wards at 2 medical centers. PATIENTS: Five hundred fifty-nine community-dwelling older adults (aged ≥70 years) discharged to their homes. MEASUREMENTS: Data on unplanned 30-day readmissions were retrieved from electronic health records. Data on at-admission activities of daily living (ADL) and in-hospital ADL decline were collected using validated questionnaires. Multivariate logistic regression was used to model the association between functioning and readmission controlling for known risk factors. RESULTS: Higher in-hospital ADL decline was significantly associated with readmission (odds ratio for each 10-point decrease in ADL = 1.32, 95% confidence interval = 1.02-1.72) but did not contribute to the overall discrimination of the model, as compared with the at-admission data (C statistic = 0.81 for each model). Identifying high-risk (10th highest percentile) patients by the at-admission model did not detect 7/55 (12.7%) of patients who would have been categorized as high risk if risk identification was postponed to the discharge date and included data on in-hospital ADL decline. CONCLUSIONS: The study highlights the ability to identify patients at high risk for readmission already early in the index hospitalization using data on functioning, nutrition, chronic morbidity, and prior hospitalizations. Nonetheless, at-discharge functional assessment can detect additional patients whose readmission risk changes during the index hospitalization. Journal of Hospital Medicine 2016;11:636-641. © 2016 Society of Hospital Medicine.

Predictors of Persistent Carbapenem-Resistant Enterobacteriaceae Carriage upon Readmission and Score Development.

BACKGROUND: Carriers of carbapenem-resistant Enterobacteriaceae (CRE) are often readmitted, exposing patients to CRE cross-transmission. OBJECTIVE To identify predictors of persistent CRE carriage upon readmission, directing a risk prediction score. DESIGN: Retrospective cohort study. SETTING: University-affiliated general hospital. PATIENTS: A cohort of 168 CRE carriers with 474 readmissions. METHODS: The primary and secondary outcomes were CRE carriage status at readmission and length of CRE carriage. Predictors of persistent CRE carriage upon readmission were analyzed using a generalized estimating equations (GEE) multivariable model. Readmissions were randomly divided into derivation and validation sets. A CRE readmission score was derived to predict persistent CRE carriage in 3 risk groups: high, intermediate, and low. The discriminatory ability of the model and the score were expressed as C statistics. RESULTS: CRE carrier status persisted for 1 year in 33% of CRE carriers. Positive CRE status was detected in 202 of 474 readmissions (42.6%). The following 4 variables were associated with persistent CRE carriage at readmission: readmission within 1 month (odds ratio [OR], 6.95; 95% confidence interval [CI], 2.79-17.30), positive CRE status on preceding admission (OR, 5.46; 95% CI, 3.06-9.75), low Norton score (OR, 3.07; 95% CI, 1.26-7.47), and diabetes mellitus (OR, 1.84; 95% CI, 0.98-3.44). The C statistics were 0.791 and 0.789 for the derivation set (n=322) model and score, respectively, and the C statistic was 0.861 for the validation set of the score (n=152). The rates of CRE carriage at readmissions (validation set) for the groups with low, intermediate, and high scores were 8.6%, 38.9%, and 77.6%, respectively. CONCLUSIONS: CRE carrier state commonly persists upon readmission, and this risk can be estimated to guide screening policy and infection control measures.

Health information exchange systems and length of stay in readmissions to a different hospital.

BACKGROUND: Readmission to a different hospital than the original discharge hospital may result in breakdowns in continuity of care. In different-hospital readmissions (DHRs), continuity can be maintained when hospitals are connected through health information exchange (HIE) systems. OBJECTIVE: To examine whether length of readmission stay (LORS) differs between same-hospital readmissions and DHRs, and whether in DHRs the LORS differs by the availability of HIE. DESIGN: A retrospective cohort study of all internal medicine 30-day readmissions in 27 Israeli hospitals between January 1, 2010 and December 31, 2010. SETTING: Clalit Health Services-Israel's largest integrated healthcare provider and payer. POPULATION: Adult Clalit members (aged 18 and older) with at least 1 readmission during the study period. METHODS: A multivariate marginal Cox model tested the likelihood for discharge during each readmission day in same-hospital readmissions (SHRs), DHRs with HIE, and DHRs without HIE. RESULTS: Of the 27,057 readmissions, 3130 (11.6%) were DHRs and 792 where DHRs with HIE in both the index and readmitting hospital. Partial continuity (DHRs with HIE) was associated with decreased likelihood of discharge on any given day compared with full continuity (SHRs) (hazard ratio [HR] = 0.85, 95% confidence interval [CI]: 0.79-0.91). Similar results were obtained for no continuity (DHRs without HIE) versus full continuity (HR = 0.90, 95% CI: 0.86-0.94). The difference between DHRs with and without HIE was not significant. CONCLUSIONS: The prolonged LORS in DHRs versus SHRs was not mitigated by the existence of HIE systems. Future research is needed to further elucidate the effects of actual use of HIE on length of DHRs. Journal of Hospital Medicine 2016;11:401-406. © 2015 Society of Hospital Medicine.

Predicting 30-day readmissions with preadmission electronic health record data.

BACKGROUND: Readmission prevention should begin as early as possible during the index admission. Early identification may help target patients for within-hospital readmission prevention interventions. OBJECTIVES: To develop and validate a 30-day readmission prediction model using data from electronic health records available before the index admission. RESEARCH DESIGN: Retrospective cohort study of admissions between January 1 and March 31, 2010. SUBJECTS: Adult enrollees of Clalit Health Services, an integrated delivery system, admitted to an internal medicine ward in any hospital in Israel. MEASURES: All-cause 30-day emergency readmissions. A prediction score based on before admission electronic health record and administrative data (the Preadmission Readmission Detection Model-PREADM) was developed using a preprocessing variable selection step with decision trees and neural network algorithms. Admissions with a recent prior hospitalization were excluded and automatically flagged as "high-risk." Selected variables were entered into multivariable logistic regression, with a derivation (two-thirds) and a validation cohort (one-third). RESULTS: The derivation dataset comprised 17,334 admissions, of which 2913 (16.8%) resulted in a 30-day readmission. The PREADM includes 11 variables: chronic conditions, prior health services use, body mass index, and geographical location. The c-statistic was 0.70 in the derivation set and of 0.69 in the validation set. Adding length of stay did not change the discriminatory power of the model. CONCLUSIONS: The PREADM is designed for use by health plans for early high-risk case identification, presenting discriminatory power better than or similar to that of previously reported models, most of which include data available only upon discharge.