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This article continues evaluation of the construct validity of the Australian Early Development Census (AEDC) through comparison with linked data from a sample of 2216 4-5 year old children collected as part of the Longitudinal Study of Australian Children (LSAC). This builds on the construct validity assessment of Brinkman et al. (Early Educ Dev 18(3):427-451, 2007) based on a smaller sample of linked Australian Early Development Instrument (AvEDI) and LSAC children, in which moderate to large correlations were apparent between teacher-rated AvEDI domains and subconstructs and LSAC measures, with lower levels apparent for parent reported LSAC measures. In the current study, the data showed moderate to low correlations between the domains and subdomains from the AEDC and teacher reported LSAC data. Differences in testing times, data sources (e.g. teachers versus carers) and levels of exposure to formal schooling at the time of testing are all discussed to account for the observed outcomes., (© 2023. The Author(s).)

This study aimed to gain insight into the success rate of linking the NICE registry with SES data from CBS and to examine whether the characteristics of linked and non-linked patients differ. Although clinically relevant differences were found, in total 93,4% of the admissions were successfully linked.

Introduction: Benchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative. However, adequate data on comparative performance between these approaches is currently lacking., Methods: The AmsterdamUMCdb intensive care database was used to construct a baseline APACHE IV in-hospital mortality model based on data typically available through manual data curation. Subsequently, new in-hospital mortality models were systematically developed and evaluated. New models differed with respect to the extent of automatic variable extraction, classification method, recalibration usage and the size of collection window., Results: A total of 13 models were developed based on data from 5,077 admissions divided into a train (80%) and test (20%) cohort. Adding variables or extending collection windows only marginally improved discrimination and calibration. An XGBoost model using only automatically extracted variables, and therefore no acute or chronic diagnoses, was the best performing automated model with an AUC of 0.89 and a Brier score of 0.10., Discussion: Performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data is similar. Importantly, our results suggest that variables typically requiring manual curation, such as diagnosis at admission and comorbidities, may not be necessary for accurate mortality prediction. These proof-of-concept results require replication using multi-centre data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Objectives: Strain on ICUs during the COVID-19 pandemic required stringent triage at the ICU to distribute resources appropriately. This could have resulted in reduced patient volumes, patient selection, and worse outcome of non-COVID-19 patients, especially during the pandemic peaks when the strain on ICUs was extreme. We analyzed this potential impact on the non-COVID-19 patients., Design: A national cohort study., Setting: Data of 71 Dutch ICUs., Participants: A total of 120,393 patients in the pandemic non-COVID-19 cohort (from March 1, 2020 to February 28, 2022) and 164,737 patients in the prepandemic cohort (from January 1, 2018 to December 31, 2019)., Interventions: None., Measurements and Main Results: Volume, patient characteristics, and mortality were compared between the pandemic non-COVID-19 cohort and the prepandemic cohort, focusing on the pandemic period and its peaks, with attention to strata of specific admission types, diagnoses, and severity. The number of admitted non-COVID-19 patients during the pandemic period and its peaks were, respectively, 26.9% and 34.2% lower compared with the prepandemic cohort. The pandemic non-COVID-19 cohort consisted of fewer medical patients (48.1% vs. 50.7%), fewer patients with comorbidities (36.5% vs. 40.6%), and more patients on mechanical ventilation (45.3% vs. 42.4%) and vasoactive medication (44.7% vs. 38.4%) compared with the prepandemic cohort. Case-mix adjusted mortality during the pandemic period and its peaks was higher compared with the prepandemic period, odds ratios were, respectively, 1.08 (95% CI, 1.05-1.11) and 1.10 (95% CI, 1.07-1.13)., Conclusions: In non-COVID-19 patients the strain on healthcare has driven lower patient volume, selection of fewer comorbid patients who required more intensive support, and a modest increase in the case-mix adjusted mortality., Competing Interests: Dr. Brinkman received funding from the National Intensive Care Evaluation (NICE) Foundation. Drs. de Keizer's and Termorshuizen’s institutions received funding from the NICE Foundation. Dr. Termorshuizen received funding from the Mental Health Care Institute Rivierduinen, Leiden. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.)

Purpose: To investigate the development in quality of ICU care over time using the Dutch National Intensive Care Evaluation (NICE) registry., Materials and Methods: We included data from all ICU admissions in the Netherlands from those ICUs that submitted complete data between 2009 and 2021 to the NICE registry. We determined median and interquartile range for eight quality indicators. To evaluate changes over time on the indicators, we performed multilevel regression analyses, once without and once with the COVID-19 years 2020 and 2021 included. Additionally we explored between-ICU heterogeneity by calculating intraclass correlation coefficients (ICC)., Results: 705,822 ICU admissions from 55 (65%) ICUs were included in the analyses. ICU length of stay (LOS), duration of mechanical ventilation (MV), readmissions, in-hospital mortality, hypoglycemia, and pressure ulcers decreased significantly between 2009 and 2019 (OR <1). After including the COVID-19 pandemic years, the significant change in MV duration, ICU LOS, and pressure ulcers disappeared. We found an ICC ≤0.07 on the quality indicators for all years, except for pressure ulcers with an ICC of 0.27 for 2009 to 2021., Conclusions: Quality of Dutch ICU care based on seven indicators significantly improved from 2009 to 2019 and between-ICU heterogeneity is medium to small, except for pressure ulcers. The COVID-19 pandemic disturbed the trend in quality improvement, but unaltered the between-ICU heterogeneity., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Stroke patients admitted to an intensive care unit (ICU) follow a particular survival pattern with a high short-term mortality, but if they survive the first 30 days, a relatively favourable subsequent survival is observed., Objectives: The development and validation of two prognostic models predicting 30-day mortality for ICU patients with ischaemic stroke and for ICU patients with intracerebral haemorrhage (ICH), analysed separately, based on parameters readily available within 24 h after ICU admission, and with comparison with the existing Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) model., Design: Observational cohort study., Setting: All 85 ICUs participating in the Dutch National Intensive Care Evaluation database., Patients: All adult patients with ischaemic stroke or ICH admitted to these ICUs between 2010 and 2019., Main Outcome Measures: Models were developed using logistic regressions and compared with the existing APACHE-IV model. Predictive performance was assessed using ROC curves, calibration plots and Brier scores., Results: We enrolled 14 303 patients with stroke admitted to ICU: 8422 with ischaemic stroke and 5881 with ICH. Thirty-day mortality was 27% in patients with ischaemic stroke and 41% in patients with ICH. Important factors predicting 30-day mortality in both ischaemic stroke and ICH were age, lowest Glasgow Coma Scale (GCS) score in the first 24 h, acute physiological disturbance (measured using the Acute Physiology Score) and the application of mechanical ventilation. Both prognostic models showed high discrimination with an AUC 0.85 [95% confidence interval (CI), 0.84 to 0.87] for patients with ischaemic stroke and 0.85 (0.83 to 0.86) in ICH. Calibration plots and Brier scores indicated an overall good fit and good predictive performance. The APACHE-IV model predicting 30-day mortality showed similar performance with an AUC of 0.86 (95% CI, 0.85 to 0.87) in ischaemic stroke and 0.87 (0.86 to 0.89) in ICH., Conclusion: We developed and validated two prognostic models for patients with ischaemic stroke and ICH separately with a high discrimination and good calibration to predict 30-day mortality within 24 h after ICU admission., Trial Registration: Trial registration: Dutch Trial Registry ( https://www.trialregister.nl/ ); identifier: NTR7438., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Society of Anaesthesiology and Intensive Care.)

Background: Previously, we reported a decreased mortality rate among patients with COVID-19 who were admitted at the ICU during the final upsurge of the second wave (February-June 2021) in the Netherlands. We examined whether this decrease persisted during the third wave and the phases with decreasing incidence of COVID-19 thereafter and brought up to date the information on patient characteristics., Methods: Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and rates of in-hospital mortality (the primary outcome) during the consecutive periods after the first wave (periods 2-9, May 25, 2020-January 31, 2023) were compared with those during the first wave (period 1, February-May 24, 2020)., Results: After adjustment for patient characteristics and ICU occupancy rate, the mortality risk during the initial upsurge of the third wave (period 6, October 5, 2021-January, 31, 2022) was similar to that of the first wave (OR adj  = 1.01, 95%-CI [0.88-1.16]). The mortality rates thereafter decreased again (e.g., period 9, October 5, 2022-January, 31, 2023: OR adj  = 0.52, 95%-CI [0.41-0.66]). Among the SARS-CoV-2 positive patients, there was a huge drop in the proportion of patients with COVID-19 as main reason for ICU admission: from 88.2% during the initial upsurge of the third wave to 51.7%, 37.3%, and 41.9% for the periods thereafter. Restricting the analysis to these patients did not modify the results on mortality., Conclusions: The results show variation in mortality rates among critically ill COVID-19 patients across the calendar time periods that is not explained by differences in case-mix and ICU occupancy rates or by varying proportions of patients with COVID-19 as main reason for ICU admission. The consistent increase in mortality during the initial, rising phase of each separate wave might be caused by the increased virulence of the contemporary virus strain and lacking immunity to the new strain, besides unmeasured patient-, treatment- and healthcare system characteristics., (© 2024. The Author(s).)

Background: Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data., Methods: Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status., Results: A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables., Conclusions: We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making., (© 2023. The Author(s).)

Importance: Little is known about temporal trends in children's well-being and how the COVID-19 pandemic may have influenced the well-being of young Australians. Certain demographic groups may be more vulnerable to experiencing declines in well-being., Objective: To examine well-being trends over 6 consecutive years among South Australian students and explore the influence of sociodemographic characteristics., Design, Setting, and Participants: Longitudinal analyses of annual (2017 to 2022) cross-sectional data of students in grades 4 through 9 (n = 40 392 to 56 897 per year) attending South Australian government schools from the Well-being and Engagement Collection (WEC) census., Exposures: Calendar year (2017-2022) and sociodemographic characteristics (sex, school grade, parental education, language spoken at home, residential region) from school enrollment records., Main Outcomes and Measures: Students self-reported life satisfaction, optimism, happiness, cognitive engagement, emotional regulation, perseverance, worry, and sadness., Results: Over 6 years (2017 to 2022), a total of 119 033 students (mean [SD] age, 12.1 y; 51.4% male) participated in this study. Most well-being measures declined over time, with consistent worsening of well-being from 2020 onward. For example, compared with 2017, sadness was 0.26 (95% CI, 0.25-0.27) points higher in 2020 (standardized mean difference [SMD], 0.27) and remained elevated by more than 0.26 points (SMD, 0.27) in 2021 and 2022. At almost every time point, greatest well-being was reported by students of male sex (except cognitive engagement and perseverance), in earlier school grades, with highest parental education, speaking a language other than English at home, and residing in outer regional and remote settings (for satisfaction, optimism, and emotional regulation). Sociodemographic differences in well-being were generally consistent over time; however, sex differences widened from 2020 for all indicators except cognitive engagement and perseverance. For example, between 2017 and 2022, sadness increased by 0.27 (95% CI, 0.25-0.29) more points among females than males (SMD, 0.28)., Conclusions and Relevance: In this longitudinal analysis of annual census data, there were downward trends in students' well-being, especially since 2020. The largest sociodemographic disparities were observed for students of female sex, those in later school grades, and those with lowest parental education. Urgent and equitable support for the well-being of all young people, particularly those facing disparity, is imperative.

Competing Interests: MM-A reports grants from UNICEF Colombia. MF reports voluntary leadership roles at HundrED, as an advisory and academy board member. MH-A reports receiving royalties on books on developmental neurology from Mac Keith Press, Kohlhammer, and Taylor and Francis; and payments, honoraria, and travel support for giving lectures on neurodisability at several universities. SSB reports consultancy fees from Special Olympics International, ending in July, 2022; support from the World Council of Optometry for travel to represent the organisation; and voluntary leadership roles at the World Council of Optometry and VISION2020 USA. All other authors declare no competing interests.

Australian families increasingly rely on eating foods from outside the home, which increases intake of energy-dense nutrient-poor foods. 'Kids' Menus' are designed to appeal to families and typically lack healthy options. However, the nutritional quality of Kids' Menus from cafes and full-service restaurants (as opposed to fast-food outlets) has not been investigated in Australia. The aim of this study was to evaluate the nutritional quality of Kids' Menus in restaurants and cafés in metropolitan Perth, Western Australia. All 787 cafes and restaurants located within the East Metropolitan Health Service area were contacted and 33% had a separate Kids' Menu. The validated Kids' Menu Healthy Score (KIMEHS) was used to assess the nutritional quality of the Kids' Menus. Almost all Kids' Menus (99%) were rated 'unhealthy' using KIMEHS. The mean KIMEHS score for all restaurants and cafés was -8.5 (range -14.5 to +3.5) which was lower (i.e., more unhealthy) than the mean KIMEHS score for the top 10 most frequented chain fast-food outlets (mean -3.5, range -6.5 to +3). The findings highlight the need for additional supports to make improvements in the nutritional quality of Kids' Menus. Local Government Public Health Plans provide an opportunity for policy interventions, using locally relevant tools to guide decision making.

Background: Building Machine Learning (ML) models in healthcare may suffer from time-consuming and potentially biased pre-selection of predictors by hand that can result in limited or trivial selection of suitable models. We aimed to assess the predictive performance of automating the process of building ML models (AutoML) in-hospital mortality prediction modelling of triage COVID-19 patients at ICU admission versus expert-based predictor pre-selection followed by logistic regression., Methods: We conducted an observational study of all COVID-19 patients admitted to Dutch ICUs between February and July 2020. We included 2,690 COVID-19 patients from 70 ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry. The main outcome measure was in-hospital mortality. We asessed model performance (at admission and after 24h, respectively) of AutoML compared to the more traditional approach of predictor pre-selection and logistic regression., Findings: Predictive performance of the autoML models with variables available at admission shows fair discrimination (average AUROC = 0·75-0·76 (sdev = 0·03), PPV = 0·70-0·76 (sdev = 0·1) at cut-off = 0·3 (the observed mortality rate), and good calibration. This performance is on par with a logistic regression model with selection of patient variables by three experts (average AUROC = 0·78 (sdev = 0·03) and PPV = 0·79 (sdev = 0·2)). Extending the models with variables that are available at 24h after admission resulted in models with higher predictive performance (average AUROC = 0·77-0·79 (sdev = 0·03) and PPV = 0·79-0·80 (sdev = 0·10-0·17))., Conclusions: AutoML delivers prediction models with fair discriminatory performance, and good calibration and accuracy, which is as good as regression models with expert-based predictor pre-selection. In the context of the restricted availability of data in an ICU quality registry, extending the models with variables that are available at 24h after admission showed small (but significantly) performance increase., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs., Materials and Methods: Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared., Results: 6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58., Conclusion: Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff., Competing Interests: Declaration of Competing Interest All authors state that there were no conflict of interest. The NICE foundation pays the department of Medical Informatics to process and analyze data for the registry. This institution is not paid specifically for this study but it is part of the task agreement to perform analyses like this on data from the NICE registry., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: To assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands., Methods: Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and indicators of quality of care during the first two upsurges (N = 4215: October 5, 2020-January 31, 2021) and the final upsurge of the second wave, called the 'third wave' (N = 4602: February 1, 2021-June 30, 2021) were compared with those during the first wave (N = 2733, February-May 24, 2020)., Results: During the second and third wave, there were less patients treated with mechanical ventilation (58.1 and 58.2%) and vasoactive drugs (48.0 and 44.7%) compared to the first wave (79.1% and 67.2%, respectively). The occupancy rates as fraction of occupancy in 2019 (1.68 and 1.55 vs. 1.83), the numbers of ICU relocations (23.8 and 27.6 vs. 32.3%) and the mean length of stay at the ICU (HRs of ICU discharge = 1.26 and 1.42) were lower during the second and third wave. No difference in adjusted hospital mortality between the second wave and the first wave was found, whereas the mortality during the third wave was considerably lower (OR = 0.80, 95% CI [0.71-0.90])., Conclusions: These data show favorable shifts in the treatment of COVID-19 patients at the ICU over time. The adjusted mortality decreased in the third wave. The high ICU occupancy rate early in the pandemic does probably not explain the high mortality associated with COVID-19., (© 2022. The Author(s).)

Objectives: Obesity is a risk factor for severe coronavirus disease 2019 and might play a role in its pathophysiology. It is unknown whether body mass index is related to clinical outcome following ICU admission, as observed in various other categories of critically ill patients. We investigated the relationship between body mass index and inhospital mortality in critically ill coronavirus disease 2019 patients and in cohorts of ICU patients with non-severe acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma., Design: Multicenter observational cohort study., Setting: Eighty-two Dutch ICUs participating in the Dutch National Intensive Care Evaluation quality registry., Patients: Thirty-five-thousand five-hundred six critically ill patients., Interventions: None., Measurements and Main Results: Patient characteristics and clinical outcomes were compared between four cohorts (coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma patients) and between body mass index categories within cohorts. Adjusted analyses of the relationship between body mass index and inhospital mortality within each cohort were performed using multivariable logistic regression. Coronavirus disease 2019 patients were more likely male, had a higher body mass index, lower Pao2/Fio2 ratio, and were more likely mechanically ventilated during the first 24 hours in the ICU compared with the other cohorts. Coronavirus disease 2019 patients had longer ICU and hospital length of stay, and higher inhospital mortality. Odds ratios for inhospital mortality for patients with body mass index greater than or equal to 35 kg/m2 compared with normal weight in the coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and trauma cohorts were 1.15 (0.79-1.67), 0.64 (0.43-0.95), 0.73 (0.61-0.87), and 0.81 (0.57-1.15), respectively., Conclusions: The obesity paradox, which is the inverse association between body mass index and mortality in critically ill patients, is not present in ICU patients with coronavirus disease 2019-related respiratory failure, in contrast to nonsevere acute respiratory syndrome coronavirus 2 viral and bacterial respiratory infections., Competing Interests: Dr. Kooistra was funded by a Radboudumc Coronavirus Disease 2019 grant (Radboudfonds). Drs. Brinkman’s and de Keizer’s institutions received funding from the National Intensive Care Evaluation (NICE) Foundation. Dr. de Keizer disclosed that she is a member of the NICE Board. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Introduction: The impact of the care for COVID-19 patients on nursing workload and planning nursing staff on the Intensive Care Unit has been huge. Nurses were confronted with a high workload and an increase in the number of patients per nurse they had to take care of., Objective: The primary aim of this study is to describe differences in the planning of nursing staff on the Intensive Care in the COVID period versus a recent non-COVID period. The secondary aim was to describe differences in nursing workload in COVID-19 patients, pneumonia patients and other patients on the Intensive Care. We finally wanted to assess the cause of possible differences in Nursing Activities Scores between the different groups., Methods: We analyzed data on nursing staff and nursing workload as measured by the Nursing Activities Score of 3,994 patients and 36,827 different shifts in 6 different hospitals in the Netherlands. We compared data from the COVID-19 period, March 1st 2020 till July 1st 2020, with data in a non-COVID period, March 1st 2019 till July 1st 2019. We analyzed the Nursing Activities Score per patient, the number of patients per nurse and the Nursing Activities Score per nurse in the different cohorts and time periods. Differences were tested by a Chi-square, non-parametric Wilcoxon or Student's t-test dependent on the distribution of the data., Results: Our results showed both a significant higher number of patients per nurse (1.1 versus 1.0, p<0.001) and a significant higher Nursing Activities Score per Intensive Care nurse (76.5 versus 50.0, p<0.001) in the COVID-19 period compared to the non-COVID period. The Nursing Activities Score was significantly higher in COVID-19 patients compared to both the pneumonia patients (55.2 versus 50.0, p<0.001) and the non-COVID patients (55.2 versus 42.6, p<0.001), mainly due to more intense hygienic procedures, mobilization and positioning, support and care for relatives and respiratory care., Conclusion: With this study we showed the impact of COVID-19 patients on the planning of nursing care on the Intensive Care. The COVID-19 patients caused a high nursing workload, both in number of patients per nurse and in Nursing Activities Score per nurse., Competing Interests: Declaration of Competing Interest The author M.E. Hoogendoorn and co-authors S. Brinkman, J.J. Spijkstra, R.J. Bosman, and N.F. de Keizer are members of the board of NICE. The funding by the NICE foundation does not alter the authors' adherence to the policies of Critical Care Nursing on sharing data and materials., (Copyright © 2021. Published by Elsevier Ltd.)

There is increasing international interest in place-based approaches to improve early childhood development (ECD) outcomes. The available data and evidence are limited and precludes well informed policy and practice change. Developing the evidence-base for community-level effects on ECD is one way to facilitate more informed and targeted community action. This paper presents overall final findings from the Kids in Communities Study (KiCS), an Australian mixed methods investigation into community-level effects on ECD in five domains of influence-physical, social, governance, service, and sociodemographic. Twenty five local communities (suburbs) across Australia were selected based on 'diagonality type' i.e. whether they performed better (off-diagonal positive), worse (off-diagonal negative), or 'as expected' (on-diagonal) on the Australian Early Development Census (AEDC) relative to their socioeconomic profile. The approach was designed to determine replicable and modifiable factors that were separate to socioeconomic status. Between 2015-2017, stakeholder interviews (n = 146), parent and service provider focus groups (n = 51), and existing socio-economic and early childhood education and care administrative data were collected. Qualitative and quantitative data analyses were undertaken to understand differences between 14 paired disadvantaged local communities (i.e. on versus off-diagonal). Further analysis of qualitative data elicited important factors for all 25 local communities. From this, we developed a draft set of 'Foundational Community Factors' (FCFs); these are the factors that lay the foundations of a good community for young children., Competing Interests: No authors have competing interests.

Background: A range of classification systems are in use for the measurement of nursing workload in Intensive Care Units. However, it is unknown to what extent the measured (objective) nursing workload, usually in terms of the amount of nursing activities, is related to the workload actually experienced (perceived) by nurses., Objectives: The aim of this study was to assess the association between the objective nursing workload and the perceived nursing workload and to identify other factors associated with the perceived nursing workload., Methods: We measured the objective nursing workload with the Nursing Activities Score and the perceived nursing workload with the NASA-Task Load Index during 228 shifts in eight different Intensive Care Units. We used linear mixed-effect regression models to analyze the association between the objective and perceived nursing workload. Furthermore, we investigated the association of patient characteristics (severity of illness, comorbidities, age, body mass index, and planned or unplanned admission), education level of the nurse, and contextual factors (numbers of patients per nurse, the type of shift (day, evening, night) and day of admission or discharge) with perceived nursing workload. We adjusted for confounders., Results: We did not find a significant association between the observed workload per nurse and perceived nursing workload (p=0.06). The APACHE-IV Acute Physiology Score of a patient was significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.02). None of the other patient characteristics was significantly associated with perceived nursing workload. Being a certified nurse or a student nurse was the only nursing or contextual factor significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.03)., Conclusion: Workload is perceived differently by nurses compared to the objectively measured workload by the Nursing Activities Score. Both the severity of illness of the patient and being a student nurse are factors that increase the perceived nursing workload. To keep the workload of nurses in balance, planning nursing capacity should be based on the Nursing Activities Score, on the severity of patient illness and the graduation level of the nurse., (Copyright © 2020. Published by Elsevier Ltd.)

Background: Several instruments have been developed to measure nursing workload. The commonly used Nursing Activities Score (NAS) and Therapeutic Intervention Scoring System (TISS) are applied to all types of ICU patients. Former research showed that NAS explained 59 to 81% of actual nursing time, whereas the Therapeutic Intervention Scoring System (TISS) described only 43% of the actual nursing time. In both models the development was not based on time measurements., Objectives: The aim of this study was to develop a time-based model which can assess patient related nursing workload more accurately and to evaluate whether patient characteristics influence nursing time and therefore should be included in the model., Design: Observational study design., Setting: All 82 Dutch ICUs participate in the National Intensive Care Evaluation (NICE) quality registry. Fifteen of these ICUs are participating in the newly implemented voluntary nursing capacity module. Seven of these ICUs voluntarily participated in this study., Participants: The patient(s) that were under the responsibility of a chosen nurse were followed by the observer during the entire shift., Methods: Time spent per nursing activity per patient was measured in different shifts in seven Dutch ICUs. Nursing activities were measured using an in-house developed web application. Three different models of varying complexity (1. nursing activities only; 2. nursing activities and case-mix correction; 3. complex model with case-mix correction per nursing activity) were developed to explain the total amount of nursing time per patient. The performance of the three models was assessed in 1000 bootstrap samples using the squared Pearson correlation coefficient (R 2 ), Root Mean Squared Prediction Error (RMSPE), Mean Absolute Prediction Error (MAPE), and prediction bias., Results: In total 287 unique patients have been observed in 371 shifts. Model one's Pearson's R was 0.89 (95%CI 0.86-0.92), model two with case-mix correction 0.90 (95%CI 0.88-0.93), and the third complex model 0.64 (95%CI 0.56-0.72) compared with the actual patient related nursing workload., Conclusion: The newly developed Nurse Operation Workload (NOW) model outperforms existing models in measuring nursing workload, while it includes a lower number of activities and therewith lowers the registration burden. Case-mix correction does not further improve the performance of this model. The patient related nursing workload measured by the NOW gives insight in the actual nursing time needed by patients and can therefore be used to evaluate the average workload per patient per nurse., (Copyright © 2020. Published by Elsevier Ltd.)

Objectives: Assessment of all-cause mortality of intracerebral hemorrhage and ischemic stroke patients admitted to the ICU and comparison to the mortality of other critically ill ICU patients classified into six other diagnostic subgroups and the general Dutch population., Design: Observational cohort study., Setting: All ICUs participating in the Dutch National Intensive Care Evaluation database., Patients: All adult patients admitted to these ICUs between 2010 and 2015; patients were followed until February 2017., Interventions: None., Measurements and Main Results: Of all 370,386 included ICU patients, 7,046 (1.9%) were stroke patients, 4,072 with ischemic stroke, and 2,974 with intracerebral hemorrhage. Short-term mortality in ICU-admitted stroke patients was high with 30 days mortality of 31% in ischemic stroke and 42% in intracerebral hemorrhage. In the longer term, the survival curve gradient among ischemic stroke and intracerebral hemorrhage patients stabilized. The gradual alteration of mortality risk after ICU admission was assessed using left-truncation with increasing minimum survival period. ICU-admitted stroke patients who survive the first 30 days after suffering from a stroke had a favorable subsequent survival compared with other diseases necessitating ICU admission such as patients admitted due to sepsis or severe community-acquired pneumonia. After having survived the first 3 months after ICU admission, multivariable Cox regression analyses showed that case-mix adjusted hazard ratios during the follow-up period of up to 3 years were lower in ischemic stroke compared with sepsis (adjusted hazard ratio, 1.21; 95% CI, 1.06-1.36) and severe community-acquired pneumonia (adjusted hazard ratio, 1.57; 95% CI, 1.39-1.77) and in intracerebral hemorrhage patients compared with these groups (adjusted hazard ratio, 1.14; 95% CI, 0.98-1.33 and adjusted hazard ratio, 1.49; 95% CI, 1.28-1.73)., Conclusions: Stroke patients who need intensive care treatment have a high short-term mortality risk, but this alters favorably with increasing duration of survival time after ICU admission in patients with both ischemic stroke and intracerebral hemorrhage, especially compared with other populations of critically ill patients such as sepsis or severe community-acquired pneumonia patients.

This paper examines the magnitude and source of gender gaps in cognitive and social-emotional skills in early primary grades in rural Indonesia. Relative to boys, girls score more than 0.17 SD higher in tests of language and mathematics (cognitive skills) and between 0.18 and 0.27 SD higher in measures of social competence and emotional maturity (social-emotional skills). We use Oaxaca-Blinder decomposition to investigate the extent to which gender differences in early schooling and parenting practices explain these gender gaps in skills. For cognitive skills, differences in early schooling between boys and girls explain between 9% and 11% of the gender gap whereas differences in parenting practices explain merely 3%-5% of the gender gap. This decomposition result is driven largely by children living in villages with high-quality preschools. In contrast, for social-emotional skills, differences in parenting styles toward boys and girls explain between 13% and 17% of the gender gap, while differences in early schooling explain only 0%-6% of the gender gap., (© 2019 The World Bank Developmental Science © 2019 John Wiley & Sons Ltd.)

Background: Multiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality., Methods: In this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value < 0.05)., Results: In 400,248 ICU patients observed, hospital mortality was 11.8% and 1-year mortality 21.8%. At ICU admission, the combined model predicted 1-year mortality with an AUC of 0.84 (95% CI 0.84-0.84). When analyzed separately, the acute component progressively lost predictive power. From an ICU admission of at least 3 days, antecedent characteristics significantly exceeded the predictive value of the acute component for 1-year mortality, AUC 0.68 (95% CI 0.68-0.69) versus 0.67 (95% CI 0.67-0.68) (p value < 0.001). For hospital mortality, antecedent characteristics outperformed the acute component from a LOS of at least 7 days, comprising 7.8% of patients and accounting for 52.4% of all bed days. ICU LOS predicted 1-year mortality with an AUC of 0.52 (95% CI 0.51-0.53) and hospital mortality with an AUC of 0.54 (95% CI 0.53-0.55) for patients with a LOS of at least 7 days., Conclusions: Comparing the predictive value of factors influencing 1-year mortality for patients with increasing ICU LOS, antecedent patient characteristics are more predictive than the acute component for patients with an ICU LOS of at least 3 days. For hospital mortality, antecedent patient characteristics outperform the acute component for patients with an ICU LOS of at least 7 days. After the first week of ICU admission, LOS itself is not predictive of hospital nor 1-year mortality.

Objectives: Studies have shown contradicting results on the association of nursing workload and mortality. Most of these studies expressed workload as patients per nurse ratios; however, this does not take into account that some patients require more nursing time than others. Nursing time can be quantified by tools like the Nursing Activities Score. We investigated the association of the Nursing Activities Score per nurse ratio, respectively, the patients per nurse ratio with in-hospital mortality in ICUs., Design: Retrospective analysis of the National Intensive Care Evaluation database., Setting: Fifteen Dutch ICUs., Patients: All ICU patients admitted to and registered ICU nurses working at 15 Dutch ICUs between January 1, 2016, and January 1, 2018, were included. The association of mean or day 1 patients per nurse ratio and Nursing Activities Score per nurse ratio with in-hospital mortality was analyzed using logistic regression models., Interventions: None., Measurements and Main Results: Nursing Activities Score per nurse ratio greater than 41 for both mean Nursing Activities Score per nurse ratio as well as Nursing Activities Score per nurse ratio on day 1 were associated with a higher in-hospital mortality (odds ratios, 1.19 and 1.17, respectively). After case-mix adjustment the association between a Nursing Activities Score per nurse ratio greater than 61 for both mean Nursing Activities Score per nurse ratio as well as Nursing Activities Score per nurse ratio on day 1 and in-hospital mortality remained significant (odds ratios, 1.29 and 1.26, respectively). Patients per nurse ratio was not associated with in-hospital mortality., Conclusions: A higher Nursing Activities Score per nurse ratio was associated with higher in-hospital mortality. In contrast, no association was found between patients per nurse ratios and in-hospital mortality in The Netherlands. Therefore, we conclude that it is more important to focus on the nursing workload that the patients generate rather than on the number of patients the nurse has to take care of in the ICU.

Introduction: The Intensive Care Unit is a resource intense service with a high nursing workload per patient resulting in a low ratio of patients per nurse. This review aims to identify existing scoring systems for measuring nursing workload on the Intensive Care and assess their validity and reliability to quantify the needed nursing time., Methods: We conducted a systematic review of the literature indexed before 01/Mar/2018 in the bibliographic databases MEDLINE, Embase, and Cinahl. Full-text articles were selected and data on systems measuring nursing workload on the Intensive Care and translation of this workload into the amount of nursing time needed was extracted., Results: We included 71 articles identifying 34 different scoring systems of which 27 were included for further analysis as these described a translation of workload into nursing time needed. Almost all systems were developed with nurses. The validity of most scoring systems was evaluated by comparing them with another system (59%) or by using time measurements (26%). The most common way to translate workload-scores into nursing time needed was by categorizing the Nurse:Patient-ratios. Validation of the Nurse:Patient-ratios was mostly evaluated by comparing the results with other systems or with the actual planning and not with objective time measurements., Conclusion: Despite the large attention given to nursing workload systems for Intensive Care, only a few systems objectively evaluated the validity and reliability of measuring nursing workload with moderate results. The Nursing Activity Score system performed best. Poor methodology for the translation of workload scores into Nurse:Patient-ratio weakens the value of nursing workload scoring systems in daily Intensive Care practice., (Copyright © 2019. Published by Elsevier Ltd.)

Objectives: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs)., Design: Prospective cohort study., Setting: A total of 306 ICUs from 24 European countries., Participants: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male)., Measurements: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves., Results: The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die., Conclusion: A predictive model of cumulative events predicts 30-day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity., (© 2019 The Authors. Journal of the American Geriatrics Society published by Wiley Periodicals, Inc. on behalf of The American Geriatrics Society.)

Disadvantaged communities tend to have poorer early childhood development outcomes. Access to safe, secure, and stable housing is a well-known social determinant of health but there is a need to examine key features of neighbourhood housing that reduce early childhood development inequities. The 2012 Australian Early Development Census (AEDC), a population-wide measure of early childhood development, and the Australian Bureau of Statistics Socio-economic Index for Areas Index of Relative Socio-economic Disadvantage were used to select fourteen disadvantaged local communities in five Australian states and territories based on those performing better (off-diagonal), or as expected (on-diagonal) on the AEDC relative to their socio-economic profile. Between 2015-2017, qualitative and quantitative housing data were collected in the local communities. In total, 87 interviews with stakeholders, 30 focus groups with local service providers and parents, and Australian Census dwelling information were analysed. A comparative case study approach was used to examine differences in housing characteristics (e.g., public housing, density, affordability, and tenure) between disadvantaged local communities performing 'better than expected' and 'as expected' on early childhood development. Perceived better housing affordability, objectively measured housing tenure (ownership) and perceived and objectively measured lower-density public housing were housing characteristics that emerged as points of difference for disadvantaged local communities where children had relatively better early childhood development outcomes. These characteristics are potential modifiable and policy sensitive housing levers for reducing early childhood development inequities.

Objective: To evaluate the associations of four individual lifestyle factors with frailty., Methods: We used cross-sectional data from 11,539 participants of the Rotterdam Study, a population-based cohort, running from 1990 till now. A frailty index was used with a range from 0 to 100 (higher values indicating increasing frailty). We examined physical activity, dietary quality, alcohol intake, and smoking and calculated a sum-score of these, with a range from 0 (lowest) to 8 (highest). The associations between each lifestyle factor and the lifestyle score with frailty were evaluated., Results: Each lifestyle factor was independently associated with frailty. Participants with high physical activity levels had lower frailty scores than participants with low physical activity (β = -4.70,95%CI = -5.10,-4.30). High diet quality, compared to low diet quality was associated with less frailty (β=-0.88,95%CI = -1.35,-0.42). Low alcohol intake was associated more frailty (β = 0.84, 95%CI = 0.39, 1.29). Never-smokers or former smokers had on average 1.15 (95%CI = -1.60,-0.69) and 1.28 (95%CI = -1.78,-0.79) better frailty scores than smokers. A one-unit increment of the lifestyle score was associated with lower frailty (β = -0.62;95%CI = -0.84,-0.53)., Conclusions: The prevention of frailty can lead to lower health care costs and a higher quality of life among the growing group of elderly people. Our results emphasize that there is an urgent need for preventions that combine several lifestyle factors to improve healthy ageing., (Copyright © 2018 Elsevier B.V. All rights reserved.)

A strategy of defining and checking explicitly formulated patient-specific treatments targets or "daily goals" in the intensive care unit has been associated with improved communication. We investigated the effect of incorporation of daily goals into daily care planning on length of stay in the intensive care unit. Furthermore, the type of daily goals and deviations from daily goals in daily care with or without documented reason were evaluated. Four university hospitals in the Netherlands, of which 2 study "daily goal" hospitals and 2 control hospitals, participated in a prospective before-after study. During the before phase of the study, daily goals were formulated by the attending physician but kept blinded from doctors and nurses caring for the patient. During the after phase of the study, daily goals were integrated in the care plan for patients admitted to the 2 study hospitals but not for patients admitted to the control hospitals. The implementation of daily goals was, after case-mix correction, not associated with a change in intensive care unit length of stay. However, this study showed that an improved administrative discipline, that is, the recording of the reason why a daily goal or standard protocol was not accomplished, is in favor of the daily goal implementation.

Aims: Childhood maltreatment and a family history of a schizophrenia spectrum disorder (SSD) are each associated with social-emotional dysfunction in childhood. Both are also strong risk factors for adult SSDs, and social-emotional dysfunction in childhood may be an antecedent of these disorders. We used data from a large Australian population cohort to determine the independent and moderating effects of maltreatment and parental SSDs on early childhood social-emotional functioning., Methods: The New South Wales Child Development Study combines intergenerational multi-agency data using record linkage methods. Multiple measures of social-emotional functioning (social competency, prosocial/helping behaviour, anxious/fearful behaviour; aggressive behaviour, and hyperactivity/inattention) on 69 116 kindergarten children (age ~5 years) were linked with government records of child maltreatment and parental presentations to health services for SSD. Multivariable analyses investigated the association between maltreatment and social-emotional functioning, adjusting for demographic variables and parental SSD history, in the population sample and in sub-cohorts exposed and not exposed to parental SSD history. We also examined the association of parental SSD history and social-emotional functioning, adjusting for demographic variables and maltreatment., Results: Medium-sized associations were identified between maltreatment and poor social competency, aggressive behaviour and hyperactivity/inattention; small associations were revealed between maltreatment and poor prosocial/helping and anxious/fearful behaviours. These associations did not differ greatly when adjusted for parental SSD, and were greater in magnitude among children with no history of parental SSD. Small associations between parental SSD and poor social-emotional functioning remained after adjusting for demographic variables and maltreatment., Conclusions: Childhood maltreatment and history of parental SSD are associated independently with poor early childhood social-emotional functioning, with the impact of exposure to maltreatment on social-emotional functioning in early childhood of greater magnitude than that observed for parental SSDs. The impact of maltreatment was reduced in the context of parental SSDs. The influence of parental SSDs on later outcomes of maltreated children may become more apparent during adolescence and young adulthood when overt symptoms of SSD are likely to emerge. Early intervention to strengthen childhood social-emotional functioning might mitigate the impact of maltreatment, and potentially also avert future psychopathology.

Objective: Mindfulness is being promoted in schools as a prevention program despite a current small evidence base. The aim of this research was to conduct a rigorous evaluation of the .b ("Dot be") mindfulness curriculum, with or without parental involvement, compared to a control condition., Method: In a randomized controlled design, students (M age 13.44, SD 0.33; 45.4% female) across a broad range of socioeconomic indicators received the nine lesson curriculum delivered by an external facilitator with (N = 191) or without (N = 186) parental involvement, or were allocated to a usual curriculum control group (N = 178). Self-report outcome measures were anxiety, depression, weight/shape concerns, wellbeing and mindfulness., Results: There were no differences in outcomes between any of the three groups at post-intervention, six or twelve month follow-up. Between-group effect sizes (Cohen's d) across the variables ranged from 0.002 to 0.37. A wide range of moderators were examined but none impacted outcome., Conclusions: Further research is required to identify the optimal age, content and length of mindfulness programs for adolescents in universal prevention settings., Trial Registration: ACTRN12615001052527., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The relationship between features of the neighbourhood built environment and early child development was investigated using area-level data from the Australian Early Development Census. Overall 9.0% of children were developmentally vulnerable on the Physical Health and Well-being domain, 8.1% on the Social Competence domain and 8.1% on the Emotional Maturity domain. After adjustment for socio-demographic factors, Local Communities with the highest quintile of home yard space had significantly lower odds of developmental vulnerability on the Emotional Maturity domain. Residing in a Local Community with fewer main roads was associated with a decrease in the proportion of children developmentally vulnerable on the Social Competence domain. Overall, sociodemographic factors were more important than aspects of the neighbourhood physical environment for explaining variation between Local Communities in the developmental vulnerability of children., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Purpose: To describe the trends in short-term and long-term mortality in very elderly intensive care unit (ICU) patients between 2008 and 2014., Methods: A retrospective cohort study was conducted using data from the National Intensive Care Evaluation Foundation from 31 Dutch ICUs. Generalized linear mixed-effects models were used to determine the change in adjusted short-term mortality (ICU/hospital deaths) and long-term mortality (3, 6, and 12 months after ICU admission) over the period 2008-2014 in very elderly patients and in patients less than 80 years old admitted to the ICU., Results: A total of 216,196 patients admitted to 31 ICUs in the period from 2008 to 2014 were included in the study, including 28,284 (13.1%) very elderly patients (80 years or older). Follow-up data for determination of 3-, 6-, and 12-month mortality were available for, respectively, 210,005 (97.1%), 202,551 (93.7%), and 176,847 (81.8%) ICU admissions. The crude ICU and in-hospital mortality decreased, respectively, from 17.6% to 13.0% and from 30.7% to 21.0%. The annual risk-adjusted ICU and in-hospital mortality of very elderly patients (adjusted for APACHE III score, comorbidities, and admission type) decreased significantly during the study period [adjusted odds ratio 0.97 (0.95-0.99) and 0.92 (0.91-0.93), respectively]. Additionally, the annual risk-adjusted 3-, 6-, and 12-month mortality decreased significantly from 2008 to 2014 [adjusted odds ratio 0.96 (0.95-0.97), 0.96 (0.94-0.97), and 0.97 (0.95-0.98), respectively]. A similar significant annual decrease in risk-adjusted short-term and long-term mortality was observed in patients aged less than 80 years., Conclusions: Both short-term and long-term risk-adjusted mortality decreased significantly during the study period in both very elderly ICU patients and patients aged less than 80 years in the Netherlands. This study clearly shows that in our setting very elderly patients benefit almost as much as their younger counterparts from improvement in quality of care over time.

The amount of family financial resources available in early life influences child health and development. Using data from the 2000 and 2007 waves of the Indonesian Family Life Survey, we estimated the associations of early-life poverty (at age <7 years) and poverty in later childhood (at age 7-14 years) with cognitive function at age 7-14 years. Our analysis provided little support for the idea that an early intervention to support household income has a larger effect than intervention later in childhood; both seemed equally important. We also decomposed the effect of poverty at age <7 years into direct and indirect effects mediated through poverty and schooling/home environment at age 7-14 years. For decomposing the effects, we used 3 approaches: 1) joint mediators, 2) path-specific, and 3) intervention analog. Being exposed to poverty before age 7 years had a larger direct effect (difference in cognitive function z score) on child cognitive function at age 7-14 years (i.e., joint mediators β = -0.07, 95% confidence interval: -0.12, -0.02) than the indirect effects mediated through later poverty at age 7-14 years (β = -0.01, 95% confidence interval: -0.04, 0.01) and school attendance/home environment at age 7-14 years. The effect of poverty on cognitive function was small; nevertheless, financial intervention may still benefit children's cognitive function., (© The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Healthy childhood development in the early years is critical for later adult health and well-being. Early childhood development (ECD) research has focused primarily on individual, family and school factors, but largely ignored community factors. The Kids in Communities Study (KiCS) will test and investigate community-level influences on child development across Australia., Methods and Analysis: Cross-sectional mixed-methods study exploring community-level effects in 25 Australian local communities; selection based on community socioeconomic status (SES) and ECD using the Australian Early Development Census (AEDC), a population measure of child development, to create a local community 'diagonality type', that is, those performing better or worse (off-diagonal), or as expected (on-diagonal) on the AEDC relative to their SES. Data collection includes stakeholder interviews, parent and service provider focus groups, and surveys with general community residents and service providers, mapping of neighbourhood design and local amenities and services, analysis of policy documents, and the use of existing sociodemographic and early childhood education and care data. Quantitative data will be used to test associations between local community diagonality type, and ECD based on AEDC scores. Qualitative data will provide complementary and deeper exploration of these same associations., Ethics and Dissemination: The Royal Children's Hospital Human Research Ethics Committee approved the study protocol (#30016). Further ethics approvals were obtained from State Education and Health departments and Catholic archdioceses where required. ECD community-level indicators will eventually be derived and made publically available. Findings will be published in peer-reviewed journals, community reports, websites and policy briefs to disseminate results to researchers, and key stakeholders including policymakers, practitioners and (most importantly) the communities involved., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Mindfulness-based interventions show consistent benefits in adults for a range of pathologies, but exploration of these approaches in youth is an emergent field, with limited measures of mindfulness for this population. This study aimed to investigate whether multifactor scales of mindfulness can be used in adolescents. A series of studies are presented assessing the performance of a recently developed adult measure, the Comprehensive Inventory of Mindfulness Experiences (CHIME) in 4 early adolescent samples. Study 1 was an investigation of how well the full adult measure (37 items) was understood by youth (N = 292). Study 2 piloted a revision of items in child friendly language with a small group (N = 48). The refined questionnaire for adolescents (CHIME-A) was then tested in Study 3 in a larger sample (N = 461) and subjected to exploratory factor analysis and a range of external validity measures. Study 4 was a confirmatory factor analysis in a new sample (N = 498) with additional external validity measures. Study 5 tested temporal stability (N = 120). Results supported an 8-factor 25-item measure of mindfulness in adolescents, with excellent model fit indices and sound internal consistency for the 8 subscales. Although the CFA supported an overarching factor, internal reliability of a combined total score was poor. The development of a multifactor measure represents a first step toward testing developmental models of mindfulness in young people. This in turn will aid construction of evidence based interventions that are not simply downward derivations of adult mindfulness programs. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Context: Intoxicated patients are frequently admitted from the emergency room to the ICU for observational reasons. The question is whether these admissions are indeed necessary., Objective: The aim of this study was to develop a model that predicts the need of ICU treatment (receiving mechanical ventilation and/or vasopressors <24 h of the ICU admission and/or in-hospital mortality)., Materials and Methods: We performed a retrospective cohort study from a national ICU-registry, including 86 Dutch ICUs. We aimed to include only observational admissions and therefore excluded admissions with treatment, at the start of the admission that can only be applied on the ICU (mechanical ventilation or CPR before admission). First, a generalized linear mixed-effects model with binominal link function and a random intercept per hospital was developed, based on covariates available in the first hour of ICU admission. Second, the selected covariates were used to develop a prediction model based on a practical point system. To determine the performance of the prediction model, the sensitivity, specificity, positive, and negative predictive value of several cut-off points based on the assigned number of points were assessed., Results: 9679 admissions between January 2010 until January 2015 were included for analysis. In total, 632 (6.5%) of the patients admitted to the ICU eventually turned out to actually need ICU treatment. The strongest predictors for ICU treatment were respiratory insufficiency, age >55 and a GCS <6. Alcohol and "other poisonings" (e.g., carbonmonoxide, arsenic, cyanide) as intoxication type and a systolic blood pressure ≥130 mmHg were indicators that ICU treatment was likely unnecessary. The prediction model had high sensitivity (93.4%) and a high negative predictive value (98.7%)., Discussion and Conclusion: Clinical use of the prediction model, with a high negative predictive value (98.7%), would result in 34.3% less observational admissions.

Anxiety, depression and eating disorders show peak emergence during adolescence and share common risk factors. School-based prevention programs provide a unique opportunity to access a broad spectrum of the population during a key developmental window, but to date, no program targets all three conditions concurrently. Mindfulness has shown promising early results across each of these psychopathologies in a small number of controlled trials in schools, and therefore this study investigated its use in a randomised controlled design targeting anxiety, depression and eating disorder risk factors together for the first time. Students (M age 13.63; SD = .43) from a broad band of socioeconomic demographics received the eight lesson, once weekly.b ("Dot be") mindfulness in schools curriculum (N = 132) or normal lessons (N = 176). Anxiety, depression, weight/shape concerns and wellbeing were the primary outcome factors. Although acceptability measures were high, no significant improvements were found on any outcome at post-intervention or 3-month follow-up. Adjusted mean differences between groups at post-intervention were .03 (95% CI: -.06 to -.11) for depression, .01 (-.07 to -.09) for anxiety, .02 (-.05 to -.08) for weight/shape concerns, and .06 (-.08 to -.21) for wellbeing. Anxiety was higher in the mindfulness than the control group at follow-up for males, and those of both genders with low baseline levels of weight/shape concerns or depression. Factors that may be important to address for effective dissemination of mindfulness-based interventions in schools are discussed. Further research is required to identify active ingredients and optimal dose in mindfulness-based interventions in school settings., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Purpose: The initial aim of this multiagency, multigenerational record linkage study is to identify childhood profiles of developmental vulnerability and resilience, and to identify the determinants of these profiles. The eventual aim is to identify risk and protective factors for later childhood-onset and adolescent-onset mental health problems, and other adverse social outcomes, using subsequent waves of record linkage. The research will assist in informing the development of public policy and intervention guidelines to help prevent or mitigate adverse long-term health and social outcomes., Participants: The study comprises a population cohort of 87,026 children in the Australian State of New South Wales (NSW). The cohort was defined by entry into the first year of full-time schooling in NSW in 2009, at which time class teachers completed the Australian Early Development Census (AEDC) on each child (with 99.7% coverage in NSW). The AEDC data have been linked to the children's birth, health, school and child protection records for the period from birth to school entry, and to the health and criminal records of their parents, as well as mortality databases., Findings to Date: Descriptive data summarising sex, geographic and socioeconomic distributions, and linkage rates for the various administrative databases are presented. Child data are summarised, and the mental health and criminal records data of the children's parents are provided., Future Plans: In 2015, at age 11 years, a self-report mental health survey was administered to the cohort in collaboration with government, independent and Catholic primary school sectors. A second record linkage, spanning birth to age 11 years, will be undertaken to link this survey data with the aforementioned administrative databases. This will enable a further identification of putative risk and protective factors for adverse mental health and other outcomes in adolescence, which can then be tested in subsequent record linkages., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Objective: To examine the risk of poor child development according to week of gestation at birth, among children born ≥ 37 weeks' gestation., Design: Population-based study using linked data (n = 12,601)., Setting: South Australia., Population: All births ≥ 37 weeks' gestation., Methods: Relative risks of developmental vulnerability for each week of gestation were calculated with adjustment for confounders and addressing missing information., Main Outcome Measures: Child development was documented by teachers during a national census of children attending their first year of school in 2009, using the Australian Early Development Index (AEDI). Children scoring in the lowest 10% of the AEDI were categorised as developmentally vulnerable., Results: The percentage of children vulnerable on one or more AEDI domains for the following gestational ages 37, 38, 39, 40, 41, 42-45 weeks was 24.8, 22.3, 20.6, 20.0, 20.4 and 24.2, respectively. Compared with children born at 40 weeks, the adjusted relative risks [(95% confidence interval (CI)] for vulnerability on ≥ 1 AEDI domain were; 37 weeks 1.13 (0.99-1.28), 38 weeks 1.05 (0.96-1.15), 39 weeks 1.02 (0.94-1.12), 41 weeks 1.00 (0.90-1.11) and 42-45 weeks 1.20 (0.84-1.72)., Conclusions: Children born at 40-41 weeks' gestation may have the lowest risk of developmental vulnerability at school entry, reinforcing the importance of term birth in perinatal care. Early term or post-term gestational age at birth can help clinicians, teachers and parents recognise children with potential developmental vulnerabilities at school entry., (© 2015 Royal College of Obstetricians and Gynaecologists.)

This review examines evidence of the association between the neighborhood built environment, green spaces and outdoor home area, and early (0-7 years) child health and development. There was evidence that the presence of child relevant neighborhood destinations and services were positively associated with early child development domains of physical health and wellbeing and social competence. Parents׳ perceptions of neighborhood safety were positively associated with children׳s social-emotional development and general health. Population representative studies using objective measures of the built environment and valid measures of early child development are warranted to understand the impact of the built environment on early child health and development., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Purpose: The relationship between the number of patients admitted to an intensive care unit (ICU) volume and mortality is currently the subject of debate. After implementation of a national guideline in 2006, all Dutch ICUs have been classified into three levels based on ICU size, patient volume, ventilation days, and staffing. The goal of this study is to investigate the association between ICU level and mortality of ICU patients in the Netherlands., Methods: We analyzed data from 132,159 patients admitted to 87 ICUs between January 1, 2009 and October 1, 2011. Logistic GEE analyses were performed to assess the influence of ICU level on in-hospital mortality and 90-day mortality in the total ICU population and in different ICU subgroups while adjusting for severity of illness by APACHE IV., Results: No significant differences were found in the adjusted in-hospital mortality of the total ICU population and in different subgroups admitted to level 1, 2 and 3 ICUs. In-hospital mortality in level 2 and 3 ICUs as opposed to level 1 ICUs was 1.06 (0.93-1.22) and 1.10 (0.94-1.29), respectively, and 90-day mortality was 0.92 (0.80-1.06) and 1.01 (0.88-1.17)., Conclusion: We demonstrated that ICU level was not associated with significant differences in the case-mix adjusted in-hospital and long-term mortality of ICU patients. This finding is in contrast with some earlier studies suggesting a volume-outcome relationship. Our results may be explained by the successful implementation of nationwide mandatory quality requirements and adequate staffing in all three levels of ICUs over the last years.

Background: Parental investments in children are an important determinant of human capability formation. We investigated the causal effect of household expenditure on Indonesian children's cognitive function between 2000 and 2007. We also investigated the effect of change in mean cognitive function from a simulation of a hypothetical cash transfer intervention., Methods: A longitudinal analysis using data from the Indonesian Family Life Survey (IFLS) was conducted including 6136 children aged 7 to 14 years in 2000 and still alive in 2007. We used the inverse probability of treatment weighting of a marginal structural model to estimate the causal effect of household expenditure on children's cognitive function., Results: Cumulative household expenditure was positively associated with cognitive function z-score. From the marginal structural model, a 74534 rupiah/month (about US$9) increase in household expenditure resulted in a 0.03 increase in cognitive function z-score [β=0.32, 95% confidence interval (CI) 0.30-0.35] Based on our simulations, among children in the poorest households in 2000 an additional ≈ US$6-10 of cash transfer resulted in a 0.01 unit increase in cognitive function z-score, equivalent to about 6% increase from the mean z-score prior to cash transfer. In contrast, children in the poorest household in 2007 did not benefit from an additional ≈ US$10 cash transfer. We found no overall effect of cash transfers at the total population level., Conclusions: Greater household expenditure had a small causal effect on children's cognitive function. Although cash transfer interventions had a positive effect for poor children, this effect was quite small. Multi-faceted interventions that combine nutrition, cash transfer, improved living conditions and women's education are required to benefit children's cognitive development in Indonesia., (© The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Interpreters are required to aid communication between clinicians and culturally and linguistically diverse (CALD) patients to ensure appropriate and timely care. Demand for interpreting services however, often exceeds supply. A mobile app to translate clinical assessment questions in 10 common languages using pictorial, written and voice-over prompts to assist patient assessments when interpreters are unavailable has been developed. This paper reports on the User Needs Analysis that informed the app. The analysis consisted of focus groups with allied health clinicians to understand pertinent aspects of initial allied health assessments and the communication needs to be addressed in the design of an app-based patient assessment tool. Outcomes show that of primary importance to clinicians was the ability to not only ask the patients questions, but to communicate information to increase understanding of, and ensure compliance with, treatments and interventions to promote patient function and comfort.

Background: There is limited longitudinal data from high-income countries on the sequelae of anaemia during pregnancy. The aim of this study is to examine whether anaemia of pregnancy is associated with adverse perinatal outcomes and with children's developmental vulnerability., Methods: We conducted a population-based study to link routinely collected government administrative data that involved all live births in the state of South Australia 1999-2005 (n = 124 061) and a subset for whom developmental data were collected during a national census of children attending their first year of school in 2009 (n = 13 654). Perinatal outcomes were recorded by midwives using a validated, standardised form. Development was recorded by schoolteachers using the Australian Early Development Index (AEDI). Children in the lowest 10% of AEDI scores are indicative of developmental vulnerability., Results: There were 8764/124 061 (7.1%) cases of anaemia. After adjustment for a range of potentially confounding factors, anaemia of pregnancy was associated with a higher risk of fetal distress [incident rate ratio (IRR) 1.20 [95% CI 1.13, 1.27]] and preterm birth <37 weeks gestation (IRR 1.23 [1.15, 1.31]), slightly higher birthweight [14 g (2, 26)] and newborns were less likely to require resuscitation (IRR 0.94 [0.91, 0.097]). Anaemia of pregnancy was not associated with children's developmental vulnerability after adjustment for maternal, obstetric and sociodemographic covariables, either in complete case analyses (n = 11 949) or after imputation for missing data (n = 13 654)., Conclusions: Anaemia of pregnancy is associated with perinatal complications but not with children's developmental vulnerability at school entry., (© 2014 John Wiley & Sons Ltd.)

Objective: Surviving Sepsis Campaign bundles have been associated with reduced mortality in severe sepsis and septic shock patients. Case-mix adjusted mortality evaluations have not been performed to compare hospitals participating in sepsis bundle programs with those not participating. We aimed to achieve an individual bundle target adherence more than 80% and a relative mortality reduction of at least 15% (absolute mortality reduction 5.2%) at the end of 2012., Design: Prospective multicenter cohort study in participating and nonparticipating centers., Setting: Eighty-two ICUs in The Netherlands., Patients: In total, 213,677 adult ICU patients admitted to all ICUs among which 8,387 severe sepsis patients at 52 participating ICUs and 8,031 severe sepsis patients at 30 nonparticipating ICUs., Interventions: A national program to screen patients for severe sepsis and septic shock and implement Surviving Sepsis Campaign bundles to complete within 6 and 24 hours after ICU admission., Measurements and Main Results: Bundle target adherence and case-mix adjusted in-hospital mortality were evaluated through odds ratios of time since program initiation by logistic generalized estimating equation analyses (July 2009 through January 2013). Outcomes were adjusted for age, gender, admission type, severity of illness, and sepsis diagnosis location. Participation duration was associated with improved bundle target adherence (adjusted odds ratio per month = 1.024 [1.016-1.031]) and decreased in-hospital mortality (adjusted odds ratio per month = 0.992 [0.986-0.997]) equivalent to 5.8% adjusted absolute mortality reduction over 3.5 years. Mortality reduced in screened patients with other diagnoses (1.9% over 3.5 yr, adjusted odds ratio per month = 0.995 [0.9906-0.9996]) and did not change in nonscreened patients in participating ICUs, nor in patients with sepsis or other diagnoses in nonparticipating ICUs., Conclusions: Implementation of a national sepsis program resulted in improved adherence to sepsis bundles in severe sepsis and septic shock patients and was associated with reduced adjusted in-hospital mortality only in participating ICUs, suggesting direct impact of sepsis screening and bundle application on in-hospital mortality.

Objective: To assess in-hospital and long-term mortality of Dutch ICU patients admitted with an acute intoxication., Design: Cohort of ICU admissions from a national ICU registry linked to records from an insurance claims database., Setting: Eighty-one ICUs (85% of all Dutch ICUs)., Patients: Seven thousand three hundred thirty-one admissions between January 1, 2008, and October 1, 2011., Interventions: None., Measurements and Main Results: Kaplan-Meier curves were used to compare the unadjusted mortality of the total intoxicated population and for specific intoxication subgroups based on the Acute Physiology and Chronic Health Evaluation IV reasons for admission: 1) alcohol(s), 2) analgesics, 3) antidepressants, 4) street drugs, 5) sedatives, 6) poisoning (carbon monoxide, arsenic, or cyanide), 7) other toxins, and 8) combinations. The case-mix adjusted mortality was assessed by the odds ratio adjusted for age, gender, severity of illness, intubation status, recurrent intoxication, and several comorbidities. The ICU mortality was 1.2%, and the in-hospital mortality was 2.1%. The mortality 1, 3, 6, 12, and 24 months after ICU admission was 2.8%, 4.1%, 5.2%, 6.5%, and 9.3%, respectively. Street drugs had the highest mortality 2 years after ICU admission (12.3%); a combination of different intoxications had the lowest (6.3%). The adjusted observed mortality showed that intoxications with street drugs and "other toxins" have a significant higher mortality 1 month after ICU admission (odds ratioadj = 1.63 and odds ratioadj= 1.73, respectively). Intoxications with alcohol or antidepressants have a significant lower mortality 1 month after ICU admission (odds ratioadj = 0.50 and odds ratioadj = 0.46, respectively). These differences were not found in the adjusted mortality 3 months upward of ICU admission., Conclusions: Overall, the mortality 2 years after ICU admission is relatively low compared with other ICU admissions. The first 3 months after ICU admission there is a difference in mortality between the subgroups, not thereafter. Still, the difference between the in-hospital mortality and the mortality after 2 years is substantial.

The incidence of sepsis continues to increase. However, over the past decade marked reductions in sepsis-related in-hospital mortality have been reported. Large variations in the presentation and severity of illness may be encountered in ICU patients with severe sepsis, which might preclude the success of screening and guideline programmes. However, the authors of this article were able to prove that a national programme involving screening and a package of interventions did lower relative in-hospital mortality by 16.7% over 3.5 years in 52 participating hospitals in the Netherlands. In-hospital mortality did not change in 30 non-participating hospitals. Therefore, the authors recommend implementing updated guidelines, sepsis quality indicators and programmes with a package of interventions to further reduce sepsis mortality. Furthermore, additional research on long term consequences in sepsis survivors is warranted.