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Background: Recently we defined a user-friendly tool to assess complexity of patients hospitalized in medical wards. FCS-1 is an average between the Barthel Index and the Exton-Smith score, while FCS-2 is obtained by using the Charlson score. The aim of this paper is to assess the ability of the FCS to predict mortality in-hospital and after 1-3-6-12-months. In this perspective, we performed comparisons with the validated Multidimensional Prognostic Index (MPI). Methods: It is a multicenter, prospective observational study, enrolling patients aged over 40, suffering from at least two chronic diseases and consecutively admitted to Internal Medicine departments. For each patient, data from 13 questionnaires were collected. Survival follow-up was conducted at 1-3-6-12 months after discharge. The relationships between cumulative incidences of death with FCS were investigated with logistic regression analyses. ROC curve analyses were performed in order to compare the predictiveness of the logistic models based on FCS with respect to those with MPI taken as reference. Results: A cohort of 541 patients was evaluated. A 10-point higher value for FCS-1 and FCS-2 leads to an increased risk of 1-year death equal to 25.0% and 27.1%, respectively. In case of in-hospital mortality, the relevant percentages were 63.1% and 15.3%. The logistic model based on FCS is significantly more predictive than the model based on MPI (which requires an almost doubled number of items) for all the time-points considered. Conclusions: Assessment of prognosis of patients has the potential to guide clinical decision-making and lead to better care. We propose a new, efficient and easy-to-use instrument based on FCS, which demonstrated a good predictive power for mortality in patients hospitalized in medical wards. This tool may be of interest for clinical practice, since it well balances feasibility (requiring the compilation of 34 items, taking around 10 minutes) and performance. [ABSTRACT FROM AUTHOR]

In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models. [ABSTRACT FROM AUTHOR]

Reducing child undernutrition is a key social policy objective of the Ethiopian government. Despite substantial reduction over the last decade and a half, child undernutrition is still high; with 48 percent of children either stunted, underweight or wasted, undernutrition remains an important child health challenge. The existing literature highlights that targeting of efforts to reduce undernutrition in Ethiopia is inefficient, in part due to lack of data and updated information. This paper remedies some of this shortfall by estimating levels of stunting and underweight in each woreda for 2014. The estimates are small area estimations based on the 2014 Demographic and Health Survey and the latest population census. It is shown that small area estimations are powerful predictors of undernutrition, even compared to household characteristics, such as wealth and education, and hence a valuable targeting metric. The results show large variations in share of children undernourished within each region, more than between regions. The results also show that the locations with larger challenges depend on the chosen undernutrition statistic, as the share, number and concentration of undernourished children point to vastly different locations. There is also limited correlation between share of children underweight and stunted across woredas, indicating that different locations face different challenges. [ABSTRACT FROM AUTHOR]

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. [ABSTRACT FROM AUTHOR]

Background: The development of cognitive and socioemotional skills early in life influences later health and well-being. Existing estimates of unmet developmental potential in low- and middle-income countries (LMICs) are based on either measures of physical growth or proxy measures such as poverty. In this paper we aim to directly estimate the number of children in LMICs who would be reported by their caregivers to show low cognitive and/or socioemotional development.Methods and Findings: The present paper uses Early Childhood Development Index (ECDI) data collected between 2005 and 2015 from 99,222 3- and 4-y-old children living in 35 LMICs as part of the Multiple Indicator Cluster Survey (MICS) and Demographic and Health Surveys (DHS) programs. First, we estimate the prevalence of low cognitive and/or socioemotional ECDI scores within our MICS/DHS sample. Next, we test a series of ordinary least squares regression models predicting low ECDI scores across our MICS/DHS sample countries based on country-level data from the Human Development Index (HDI) and the Nutrition Impact Model Study. We use cross-validation to select the model with the best predictive validity. We then apply this model to all LMICs to generate country-level estimates of the prevalence of low ECDI scores globally, as well as confidence intervals around these estimates. In the pooled MICS and DHS sample, 14.6% of children had low ECDI scores in the cognitive domain, 26.2% had low socioemotional scores, and 36.8% performed poorly in either or both domains. Country-level prevalence of low cognitive and/or socioemotional scores on the ECDI was best represented by a model using the HDI as a predictor. Applying this model to all LMICs, we estimate that 80.8 million children ages 3 and 4 y (95% CI 48.1 million, 113.6 million) in LMICs experienced low cognitive and/or socioemotional development in 2010, with the largest number of affected children in sub-Saharan Africa (29.4.1 million; 43.8% of children ages 3 and 4 y), followed by South Asia (27.7 million; 37.7%) and the East Asia and Pacific region (15.1 million; 25.9%). Positive associations were found between low development scores and stunting, poverty, male sex, rural residence, and lack of cognitive stimulation. Additional research using more detailed developmental assessments across a larger number of LMICs is needed to address the limitations of the present study.Conclusions: The number of children globally failing to reach their developmental potential remains large. Additional research is needed to identify the specific causes of poor developmental outcomes in diverse settings, as well as potential context-specific interventions that might promote children's early cognitive and socioemotional well-being. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the relationship between the ground reaction force-time profile of a countermovement jump (CMJ) and fatigue, specifically focusing on predicting the onset of neuromuscular versus metabolic fatigue using the CMJ. Method: Ten recreational athletes performed 5 CMJs at time points prior to, immediately following, and at 0.5, 1, 3, 6, 24 and 48 h after training, which comprised repeated sprint sessions of low, moderate, or high workloads. Features of the concentric portion of the CMJ force-time signature at the measurement time points were analysed using Principal Components Analysis (PCA) and functional PCA (fPCA) to better understand fatigue onset given training workload. In addition, Linear Mixed Effects (LME) models were developed to predict the onset of fatigue. Results: The first two Principal Components (PCs) using PCA explained 68% of the variation in CMJ features, capturing variation between athletes through weighted combinations of force, concentric time and power. The next two PCs explained 9.9% of the variation and revealed fatigue effects between 6 to 48 h after training for PC3, and contrasting neuromuscular and metabolic fatigue effects in PC4. fPCA supported these findings and further revealed contrasts between metabolic and neuromuscular fatigue effects in the first and second half of the force-time curve in PC3, and a double peak effect in PC4. Subsequently, CMJ measurements up to 0.5 h after training were used to predict relative peak CMJ force, with mean squared errors of 0.013 and 0.015 at 6 and 48 h corresponding to metabolic and neuromuscular fatigue. Conclusion: The CMJ was found to provide a strong predictor of neuromuscular and metabolic fatigue, after accounting for force, concentric time and power. This method can be used to assist coaches to individualise future training based on CMJ response to the immediate session. [ABSTRACT FROM AUTHOR]

We investigated what factors may foster or hinder physicians’ cancer screening risk literacy–specifically the ability to understand evidence regarding screening effectiveness and make evidence-based recommendations to patients. In an experiment, physicians in training (interns and residents) read statistical information about outcomes from screening for cancer, and had to decide whether to recommend it to a patient. We manipulated the effectiveness of the screening (effective vs. ineffective at reducing mortality) and the demand of the patient to get screened (demand vs. no demand). We assessed participants’ comprehension of the presented evidence and recommendation to the patient, as well as a-priori screening beliefs (e.g., that screening is always a good choice), numeracy, science literacy, knowledge of screening statistics, statistical education, and demographics. Stronger positive a-priori screening beliefs, lower knowledge of screening statistics, and lower numeracy were related to worse comprehension of the evidence. Physicians recommended against the ineffective screening but only if they showed good comprehension of the evidence. Physicians’ recommendations were further based on the perceived benefits from screening but not on perceived harms, nor the patient’s demands. The current study demonstrates that comprehension of cancer screening statistics and the ability to infer the potential benefits for patients are essential for evidence-based recommendations. However, strong beliefs in favor of screening fostered by promotion campaigns may influence how physicians evaluate evidence about specific screenings. Fostering physician numeracy skills could help counteract such biases and provide evidence-based recommendations to patients. [ABSTRACT FROM AUTHOR]

Purpose: Ageing in place is one of the greatest desires of elderly people. Assistive digital technologies could potentially delay the institutionalization of the elderly people and allow them ageing in place. This study develops a population-wide cost estimating framework for adopting digital technologies that can improve the quality of life of elderly people through examining an Australian region. Methods: We developed a five-stage cost estimation framework, which involved progressive forecasting of elderly population and direct cost estimation methods. The forecasting and cost estimation models have been set for a 10-year period because the prediction accuracy from cross-sectional data is better in the short to medium term compared to the long-term. For cost estimation, we categorised the ageing population on the basis of the number of chronic diseases that they have contracted. Costs of assistive technologies were collected from open sources. The model has been tested in the Fitzroy and Central West, a regional area of Queensland in Australia. A stakeholder panel discussion in a workshop format was used to validate the appropriateness of the proposed framework and the study findings. Results: This study identified eight common chronic diseases with different comorbidity patterns in Australia. We also identified the required assistive technologies to assist patients with chronic diseases. This study estimated that annual per capita cost for technological intervention could range from AUD 4,169 to AUD 7,551 on the basis of different price margins of the technologies. Conclusion: The approach of categorising the aged cohorts on the basis of the number of chronic diseases helps estimate population-wide costs compared to using single technology intervention costs for a particular chronic disease cohort. The cost estimation framework and the method developed in this study can assist the government to estimate costs for ageing-in-place programs. [ABSTRACT FROM AUTHOR]

The need for replication of initial results has been rediscovered only recently in many fields of research. In preclinical biomedical research, it is common practice to conduct exact replications with the same sample sizes as those used in the initial experiments. Such replication attempts, however, have lower probability of replication than is generally appreciated. Indeed, in the common scenario of an effect just reaching statistical significance, the statistical power of the replication experiment assuming the same effect size is approximately 50%—in essence, a coin toss. Accordingly, we use the provocative analogy of “replicating” a neuroprotective drug animal study with a coin flip to highlight the need for larger sample sizes in replication experiments. Additionally, we provide detailed background for the probability of obtaining a significant p value in a replication experiment and discuss the variability of p values as well as pitfalls of simple binary significance testing in both initial preclinical experiments and replication studies with small sample sizes. We conclude that power analysis for determining the sample size for a replication study is obligatory within the currently dominant hypothesis testing framework. Moreover, publications should include effect size point estimates and corresponding measures of precision, e.g., confidence intervals, to allow readers to assess the magnitude and direction of reported effects and to potentially combine the results of initial and replication study later through Bayesian or meta-analytic approaches. [ABSTRACT FROM AUTHOR]

Background: Despite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and Findings: Data came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee-Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.Conclusions: To our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050. [ABSTRACT FROM AUTHOR]

As dog bite injuries are a considerable problem in modern society, in order to reduce such injuries, breed-specific legislation has been introduced in a number of countries. Whilst many studies have shown a lack of effect with such legislation, the commonly used methodology is known to be flawed. Therefore, the aim of this study is to investigate the effect of the Danish breed-specific legislation on the number of dog bite injuries using more credible methods. A time series intervention method was used on a detailed dataset from Odense University Hospital, Denmark, regarding dog bite injuries presented to the emergency department. The results indicate that banning certain breeds has a highly limited effect on the overall levels of dog bite injuries, and that an enforcement of the usage of muzzle and leash in public places for these breeds also has a limited effect. Despite using more credible and sound methods, this study supports previous studies showing that breed-specific legislation seems to have no effect on dog bite injuries. In order to minimise dog bite injuries in the future, it would seem that other interventions or non-breed-specific legislation should be considered as the primary option. [ABSTRACT FROM AUTHOR]

Spatio-temporal Bayesian modeling, a method based on regional statistics, is widely used in epidemiological studies. Using Bayesian theory, this study builds a spatio-temporal Bayesian model specific to urban crime to analyze its spatio-temporal patterns and determine any developing trends. The associated covariates and their changes are also analyzed. The model is then used to analyze data regarding burglaries that occurred in Wuhan City in China from January to August 2013. Of the diverse socio-economic variables associated with crime rate, including population, the number of local internet bars, hotels, shopping centers, unemployment rate, and residential zones, this study finds that the burglary crime rate is significantly correlated with the average resident population per community and number of local internet bars. This finding provides a scientific reference for urban safety protection. [ABSTRACT FROM AUTHOR]

Background: A previous single-center study established a mathematical model for predicting the adult height (AH) in girls with idiopathic central precocious puberty (CPP). Objective: To perform internal and external validations by comparing the actual AH to the calculated AH established by this model and to update it. Methods: The original formula, calculated AH (cm) = 2.21 (height at initial evaluation, SD) + 2.32 (target height, SD) - 1.83 (luteinizing hormone/follicle-stimulating hormone peaks ratio) + 159.68, was established in a sample of 134 girls (group 4) and was applied to additional girls with CPP seen in the same center (group 1, n = 35), in Germany (group 2, n = 43) and in the Netherlands (group 3, n = 72). This formula has been updated based on these extended data, and both versions are available at the following location: . Results: Despite the differences among the 4 groups in terms of their characteristics at the initial evaluation and the percentages of patients treated with the gonadotropin-releasing hormone analogue, they have similar calculated and actual AHs. The actual AHs are 162.2±7.0, 163.0±7.6, 162.4±7.7 and 162.1±5.6 cm in groups 1 to 4, respectively. They are highly correlated with the AHs calculated by the formula established in the original group (group 4), with R at 0.84, 0.67 and 0.69 in groups 1 to 3, respectively. When the actual AHs and the AHs predicted by the Bayley and Pinneau method are compared, the R is 0.76, 0.51 and 0.64 in groups 1 to 3, respectively. The absolute differences between actual AHs and the calculated AHs are greater than 1 SD (5.6 cm) in 15%, 35% and 28% of the patients in groups 1 to 3, respectively. Conclusion: This study validates and updates the previously established formula for predicting AH in girls with CPP. This updated formula can help clinicians to make treatment decisions. [ABSTRACT FROM AUTHOR]

Age and cortical structure are both associated with cognition, but characterizing this relationship remains a challenge. A popular approach is to use functional network organization of the cortex as an organizing principle for post-hoc interpretations of structural results. In the current study, we introduce two complimentary approaches to structural analyses that are guided by a-priori functional network maps. Specifically, we systematically investigated the relationship of cortical structure (thickness and surface area) of distinct functional networks to two cognitive domains sensitive to age-related decline thought to rely on both common and distinct processes (executive function and episodic memory) in older adults. We quantified the cortical structure of individual functional network’s predictive ability and spatial extent (i.e., number of significant regions) with cognition and its mediating role in the age-cognition relationship. We found that cortical thickness, rather than surface area, predicted cognition across the majority of functional networks. The default mode and somatomotor network emerged as particularly important as they appeared to be the only two networks to mediate the age-cognition relationship for both cognitive domains. In contrast, thickness of the salience network predicted executive function and mediated the age-cognition relationship for executive function. These relationships remained significant even after accounting for global cortical thickness. Quantifying the number of regions related to cognition and mediating the age-cognition relationship yielded similar patterns of results. This study provides a potential approach to organize and describe the apparent widespread regional cortical structural relationships with cognition and age in older adults. [ABSTRACT FROM AUTHOR]

Background: The high prevalence of metabolic syndrome (MetS) and cardiovascular diseases (CVD) is observed among Kazakhs in Xinjiang. Because MetS may significantly predict the occurrence of CVD, the inclusion of CVD-related indicators in metabolic network may improve the predictive ability for a CVD-risk model for Kazakhs in Xinjiang. Methods: The study included 2,644 subjects who were followed for 5 years or longer. CVD cases were identified via medical records of the local hospitals from April 2016 to August 2017. Factor analysis was performed in 706 subjects (267 men and 439 women) with MetS to extract CVD-related potential factors from 18 biomarkers tested in a routine health check-up, served as a synthetic predictor (SP). We evaluated the predictive ability of the CVD-risk model using age and SP, logistic regression discrimination for internal validation (n = 384; men = 164, women = 220) and external validation (n = 219; men = 89, women = 130), calculated the probability of CVD for each participant, and receiver operating characteristic curves. Results: According to the diagnostic criteria of JIS, the prevalence of MetS in Kazakh was 30.9%. Seven potential factors with a similar pattern were obtained from men and women and comprised the CVD predictors. When predicting CVD in the internal validation, the area under the curve (AUC) were 0.857 (95%CI 0.807–0.898) for men and 0.852 (95%CI 0.809–0.889) for women, respectively. In the external validation, the AUC to predict CVD were 0.914 (95%CI 0.832–0.963) for men and 0.848 (95%CI 0.774–0.905) for women. It is suggested that SP might serve as a useful tool in identifying CVD with in Kazakhs, especially for Kazakhs men. Conclusions: Among 7 potential factors were extracted from 18 biomarkrs in Kazakhs with MetS, and SP may be used for CVD risk assessment. [ABSTRACT FROM AUTHOR]

Background: A predictive model of scores of difficult intubation (DI) may help physicians screen for airway difficulty to reduce morbidity and mortality in obese patients. The present study aimed to set up and evaluate the predictive performance of a newly developed, practical, multivariate DI model for obese patients. Methods: A prospective multi-center study was undertaken on adults with a body mass index (BMI) of 30 kg/m2 or more who were undergoing conventional endotracheal intubation. The BMI and 10 preoperative airway tests (namely, malformation of the teeth in the upper jaw, the modified Mallampati test [MMT], the upper lip bite test, neck mobility testing, the neck circumference [NC], the length of the neck, the interincisor gap, the hyomental distance, the thyromental distance [TM] and the sternomental distance) were examined. A DI was defined as one with an intubation difficulty scale (IDS) score ≥ 5. Results: The 1,015 patients recruited for the study had a mean BMI of 34.2 (standard deviation: 4.3 kg/m2). The proportions for easy intubation, slight DI and DI were 81%, 15.8% and 3.2%, respectively. Drawing on the results of a multivariate analysis, clinically meaningful variables related to obesity (namely, BMI, MMT, and the ratio of NC to TM) were used to build a predictive model for DI. Nevertheless, the best model only had a fair predictive performance. The area under the receiver operating characteristic curve (AUC) was 0.71 (95% confidence interval 0.68–0.84). Conclusions: The predictive performance of the selected model showed limited benefit for preoperative screening to predict DI among obese patients. [ABSTRACT FROM AUTHOR]

Objective: Alcohol, tobacco and cannabis use in adolescence is associated with adverse outcomes. Characterizing adolescent substance misusers, however, is difficult due to the wide range of risk and protective factors linked to substance use. The aim of the present study was to examine the role of the Individual, Family, School, Peer, and Social Environment on alcohol (lifetime and risky), tobacco (risky only), and cannabis use (lifetime and riskiness). Method: Data were analyzed from a national sample of 5,680 adolescents, capturing substance use behavior alongside risk and protective factors across Individual, Family, School, Peer and Social domains. We applied a sophisticated machine learning classifier to develop models of alcohol, tobacco and cannabis initiation and misuse. Results: We found highly accurate (area under curve of receiver-operator-characteristic for out-of-sample performance was > .88) and replicable (over multiple iterations and in comparison with permuted outcomes) dissociable psychosocial profiles of alcohol, tobacco and cannabis use. Alongside common predictors (peer relations and externalizing behavior), dissociable risk and resilience factors were observed. Adolescent profiles of alcohol use were distinguished by the contribution of multiple domains. In contrast, tobacco use was characterized by a small number of individual variables, including female gender and poor perceived academic position. Cannabis use was differentiated by the distinct contribution of Individual risk factors, in particular male gender and feelings of anger. Differential associations were also evident, with the strength and direction of association differing substantially across substances. Conclusion: This study indicates that the relationship between the environment and substance use is more complex than previously thought. [ABSTRACT FROM AUTHOR]

Background: Over 18 million adults have initiated life-saving antiretroviral therapy (ART) in resource-poor settings; however, mortality and lost-to-follow-up rates continue to be high among patients in their first year after treatment start. Clinical decision tools are needed to identify patients at high risk for poor outcomes in order to provide individualized risk assessment and intervention. This study aimed to develop and externally validate risk prediction tools that estimate the probability of dying or of being lost to follow-up (LTF) during the year after starting ART. Methods: We used a derivation cohort of 7,031 adults age 15–70 years initiating ART from 2007 to 2013 at 6 clinics in Haiti; 242 (3.5%) had documented death and 1,521 (21.6%) were LTF at 1 year after starting ART. The following routinely collected data were used as predictors in two logistic regression models (one to predict death and another to predict LTF): age, gender, weight, CD4 count, WHO Stage, and diagnosis of tuberculosis (TB). The validation cohort consisted of 1,835 adults initiating ART at a different HIV clinic in Haiti during 2012. We assessed model discrimination by measuring the C-statistic, and measured model calibration by how closely the predicted probabilities approximated actual probabilities of the two outcomes. We derived a nomogram and a point-based risk score from the predictive models. Findings: The model predicting death within the year after starting ART had a C-statistic of 0.75 (95% CI 0.74 to 0.81). There was no evidence for significant overfitting and the predictions were well calibrated. The strongest predictors of 1-year mortality were male gender, low weight, low CD4 count, advanced WHO stage, and the absence of TB. In the validation cohort, the C-statistic was 0.69 (95% CI 0.59 to 0.77). A point-based risk score for death had a C-statistic 0.73 (95% CI 0.69 to 0.76) and categorizes patients as low risk (<2% risk of death), average risk (3–4%), and high-risk (8–10%) and very high-risk (14–19%) with likelihood ratios to be used in settings where the baseline risk is different from our study population. The model predicting LTF did not discriminate well (C-statistic 0.59). Conclusions: A simple risk-score using routinely collected data can predict 1-year mortality after ART initiation for HIV-positive adults in Haiti. However, predicting lost to follow-up using routinely collected data was not as successful. The next step is to assess whether use of this risk score can identify patients who need tailored services to reduce mortality in resource-poor settings such as Haiti. [ABSTRACT FROM AUTHOR]

Purpose: To assess and improve the effectiveness of ICU care, in-hospital mortality rates are often used as principal quality indicator for benchmarking purposes. Two other often used, easily quantifiable, quality indicators to assess the efficiency of ICU care are based on readmission to the ICU and ICU length of stay. Our aim was to examine whether there is an association between case-mix adjusted outcome-based quality indicators in the general ICU population as well as within specific subgroups. Materials and methods: We included patients admitted in 2015 of all Dutch ICUs. We derived the standardized in-hospital mortality ratio (SMR); the standardized readmission ratio (SRR); and the standardized length of stay ratio (SLOSR). We expressed association through Pearson’s correlation coefficients. Results: The SMR ranged from 0.6 to 1.5; the SRR ranged from 0.7 to 2.1; and the SLOSR ranged from 0.7 to 1.3. For the total ICU population we found no significant associations. We found a positive, non-significant, association between SMR and SLOSR for admissions with low-mortality risk, (r = 0.25; p = 0.024), and a negative association between these indicators for admissions with high-mortality risk (r = -0.49; p<0.001). Conclusion: Overall, we found no association at ICU population level. Differential associations were found between performance on mortality and length of stay within different risk strata. We recommend users of quality information to take these three outcome indicators into account when benchmarking ICUs as they capture different aspects of ICU performance. Furthermore, we suggest to report quality indicators for patient subgroups. [ABSTRACT FROM AUTHOR]

In old age, sufficient protein intake is important to preserve muscle mass and function. Around 50% of older adults (65+ y) consumes ≤1.0 g/kg adjusted body weight (BW)/day (d). There is no rapid method available to screen for low protein intake in old age. Therefore, we aimed to develop and validate a short food questionnaire to screen for low protein intake in community-dwelling older adults. We used data of 1348 older men and women (56–101 y) of the LASA study (the Netherlands) to develop the questionnaire and data of 563 older men and women (55–71 y) of the HELIUS study (the Netherlands) for external validation. In both samples, protein intake was measured by the 238-item semi-quantitative HELIUS food frequency questionnaire (FFQ). Multivariable logistic regression analysis was used to predict protein intake ≤1.0 g/kg adjusted BW/d (based on the HELIUS FFQ). Candidate predictor variables were FFQ questions on frequency and amount of intake of specific foods. In both samples, 30% had a protein intake ≤1.0 g/kg adjusted BW/d. Our final model included adjusted body weight and 10 questions on the consumption (amount on average day or frequency in 4 weeks) of: slices of bread (number); glasses of milk (number); meat with warm meal (portion size); cheese (amount and frequency); dairy products (like yoghurt) (frequency); egg(s) (frequency); pasta/noodles (frequency); fish (frequency); and nuts/peanuts (frequency). The area under the receiver operating characteristic curve (AUC) was 0.889 (95% CI 0.870–0.907). The calibration slope was 1.03 (optimal slope 1.00). At a cut-off of ≤0.8 g/kg adjusted BW/d, the AUC was 0.916 (96% CI 0.897–0.936). Applying the regression equation to the HELIUS sample, the AUC was 0.856 (95% CI 0.824–0.888) and the calibration slope 0.92. Regression coefficients were therefore subsequently shrunken by a linear factor 0.92. To conclude, the short food questionnaire (Pro55+) can be used to validly screen for protein intake ≤1.0 g/kg adjusted BW/d in community-dwelling older adults. An online version can be found at . External validation in other countries is recommended. [ABSTRACT FROM AUTHOR]

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features. [ABSTRACT FROM AUTHOR]

Background: There are two main types of strategies to identify target population for lung cancer screening: 1) strategies based on age and cumulative smoking criteria, 2) risk prediction models allowing the calculation of an individual risk. The objective of this study was to compare different strategies to identify the proportion of the Spanish population at high risk of developing lung cancer, susceptible to be included in a lung cancer screening programme. Methods: Cross-sectional study. We used the data of the Spanish National Interview Health Survey (ENSE) of 2011–2012 (21,006 individuals) to estimate the proportion of participants at high risk of developing lung cancer. This estimation was performed using the U.S. national lung screening trial (NLST) criteria and a 6-year prediction model (PLCOm2012), both independently and in combination. Results: The prevalence of individuals at high risk of developing lung cancer according to the NLST criteria was 4.9% (7.9% for men, 2.4% for women). Among the 1,034 subjects who met the NLST criteria, 533 (427 men and 106 women) had a 6-year lung cancer risk ≥2.0%. The combination of these two selection strategies showed that 2.5% of the Spanish population had a high risk of developing lung cancer. However, this selection process did not take into account different groups of subjects <75 years old having an individual risk of lung cancer ≥2%, such as heavy smokers <55 years old who were long-time former smokers, and ever-smokers having smoked <30 pack-years with other risk factors. Conclusions: Further research is needed to determine which selection strategy achieves a higher benefit/harm ratio and to assess other prevention strategies for individuals with elevated risk for lung cancer but who do not meet the screening eligibility criteria. [ABSTRACT FROM AUTHOR]

Objectives: The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. Methods: Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. Results: Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. ‘ready gettin leave’) can explain much of the variance associated between socioeconomics and excessive alcohol consumption. Conclusions: Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained. [ABSTRACT FROM AUTHOR]

Aims: Social scientists have postulated that the discrepancy between achievements and expectations affects individuals' subjective well-being. Still, little has been done to qualify and quantify such a psychological effect. Our empirical analysis assesses the consequences of positive and negative affective forecasting errors—the difference between realized and expected subjective well-being—on the subsequent level of subjective well-being. Data: We use longitudinal data on a representative sample of 13,431 individuals from the German Socio-Economic Panel. In our sample, 52% of individuals are females, average age is 43 years, average years of education is 11.4 and 27% of our sample lives in East Germany. Subjective well-being (measured by self-reported life satisfaction) is assessed on a 0–10 discrete scale and its sample average is equal to 6.75 points. Methods: We develop a simple theoretical framework to assess the consequences of positive and negative affective forecasting errors—the difference between realized and expected subjective well-being—on the subsequent level of subjective well-being, properly accounting for the endogenous adjustment of expectations to positive and negative affective forecasting errors, and use it to derive testable predictions. Given the theoretical framework, we estimate two panel-data equations, the first depicting the association between positive and negative affective forecasting errors and the successive level of subjective well-being and the second describing the correlation between subjective well-being expectations for the future and hedonic failures and successes. Our models control for individual fixed effects and a large battery of time-varying demographic characteristics, health and socio-economic status. Results and conclusions: While surpassing expectations is uncorrelated with subjective well-being, failing to match expectations is negatively associated with subsequent realizations of subjective well-being. Expectations are positively (negatively) correlated to positive (negative) forecasting errors. We speculate that in the first case the positive adjustment in expectations is strong enough to cancel out the potential positive effects on subjective well-being of beaten expectations, while in the second case it is not, and individuals persistently bear the negative emotional consequences of not achieving expectations. [ABSTRACT FROM AUTHOR]

Background: Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts. Methods: We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal National Population Health Survey and sex-specific Generalized Estimating Equations to predict the 10-year risk of obesity among adults 18 and older. The model was validated using a bootstrap approach accounting for the survey design. Model performance was measured by the Brier statistic, discrimination was measured by the C-statistic, and calibration was assessed using the Hosmer-Lemeshow Goodness of Fit Chi Square (HL χ2). Results: Predictive factors included baseline body mass index, age, time and their interactions, smoking status, living arrangements, education, alcohol consumption, physical activity, and ethnicity. OPoRT showed good performance for males and females (Brier 0.118 and 0.095, respectively), excellent discrimination (C statistic ≥ 0.89) and achieved calibration (HL χ2 <20). Conclusion: OPoRT is a valid and reliable algorithm that can be applied to routinely collected survey data to estimate the risk of obesity and identify groups at increased risk of obesity. These results can guide prevention efforts aimed at reducing the population burden of obesity. [ABSTRACT FROM AUTHOR]

COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also the top performer in sensitivity, specificity, and ppv when predicting a patient’s need for emergency care. [ABSTRACT FROM AUTHOR]

Objective: The adenoma detection rate (ADR) varies widely between physicians, possibly due to patient population differences, hampering direct ADR comparison. We developed and validated a prediction model for adenoma detection in an effort to determine if physicians’ ADRs should be adjusted for patient-related factors. Materials and methods: Screening and surveillance colonoscopy data from the cross-sectional multicenter cluster-randomized Endoscopic Quality Improvement Program-3 (EQUIP-3) study (NCT02325635) was used. The dataset was split into two cohorts based on center. A prediction model for detection of ≥1 adenoma was developed using multivariable logistic regression and subsequently internally (bootstrap resampling) and geographically validated. We compared predicted to observed ADRs. Results: The derivation (5 centers, 35 physicians, overall-ADR: 36%) and validation (4 centers, 31 physicians, overall-ADR: 40%) cohort included respectively 9934 and 10034 patients (both cohorts: 48% male, median age 60 years). Independent predictors for detection of ≥1 adenoma were: age (optimism-corrected odds ratio (OR): 1.02; 95%-confidence interval (CI): 1.02–1.03), male sex (OR: 1.73; 95%-CI: 1.60–1.88), body mass index (OR: 1.02; 95%-CI: 1.01–1.03), American Society of Anesthesiology physical status class (OR class II vs. I: 1.29; 95%-CI: 1.17–1.43, OR class ≥III vs. I: 1.57; 95%-CI: 1.32–1.86), surveillance versus screening (OR: 1.39; 95%-CI: 1.27–1.53), and Hispanic or Latino ethnicity (OR: 1.13; 95%-CI: 1.00–1.27). The model’s discriminative ability was modest (C-statistic in the derivation: 0.63 and validation cohort: 0.60). The observed ADR was considerably lower than predicted for 12/66 (18.2%) physicians and 2/9 (22.2%) centers, and considerably higher than predicted for 18/66 (27.3%) physicians and 4/9 (44.4%) centers. Conclusion: The substantial variation in ADRs could only partially be explained by patient-related factors. These data suggest that ADR variation could likely also be due to other factors, e.g. physician or technical issues. [ABSTRACT FROM AUTHOR]

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed. [ABSTRACT FROM AUTHOR]

Background: Retinopathy of prematurity (ROP) is a blinding morbidity of preterm infants. Our current screening criteria have remained unchanged since their inception and lack the ability to identify those at greatest risk. Objectives: We sought to comprehensively analyze numerous proposed maternal, infant, and environmental ROP risk variables in a robustly phenotyped population using logistic regression to determine the most predictive model for ROP development and severity. We further sought to determine the statistical interaction between significant ROP risk variables, which has not previously been done in the field of ROP. We hypothesize that our comprehensive analysis will allow for better identification of risk variables that independently correlate with ROP disease. Going forward, this may allow for improved infant risk stratification along a time continuum from prenatal to postnatal development, making prevention more feasible. Methods: We performed a retrospective cohort analysis of preterm infants referred for ROP screening in one neonatal intensive care unit from 2010–2015. The primary outcome measure was presence of ROP. Secondary outcome measures were ROP requiring treatment and severe ROP not clearly meeting current treatment criteria. Univariate, stepwise regression and statistical interaction analyses of 57 proposed ROP risk variables was performed to identify variables which were significantly associated with each outcome measure. Results: We identified 457 infants meeting our inclusion criteria. Within this cohort, numerous factors showed a significant individual association with our ROP outcome measures; however, stepwise regression analysis found the most predictive model for overall ROP risk included estimated gestational age, birth weight, the need for any surgery, and maternal magnesium prophylaxis. The corresponding Area Under the Curve (AUC) for this model was 0.8641, while the traditional model of gestational age and birth weight predicted ROP disease less well with an AUC of 0.8489. Development of severe ROP was best predicted by estimated gestational age (week), the need for any surgery and increased probability of death or moderate-severe BPD at 7 days. Finally, the model most predictive for type 1 ROP included estimated gestational age (week) and the presence of severe chronic lung disease. No significant statistical interaction was found between variables. Conclusions: Our work is unique as we report comprehensive analysis of the greatest number of proposed ROP risk variables to date in a robustly phenotyped population. We describe novel risk models for our ROP outcome measures and demonstrate independence of these variables using statistical modeling not previously applied to ROP. This may better allow for individual infant risk stratification and importantly mitigation of future risk. [ABSTRACT FROM AUTHOR]

Background: The optimal approach to titrate vasopressor therapy is unclear. Recent sepsis guidelines recommend a mean arterial pressure (MAP) target of 65 mmHg and higher for chronic hypertensive patients. As data emerge from clinical trials comparing blood pressure targets for vasopressor therapy, an accurate description of usual care is required to interpret study results. Our aim was to measure MAP values during vasopressor therapy in Canadian intensive care units (ICUs) and to compare these with stated practices and guidelines. Method: In a multicenter prospective cohort study of critically ill adults with severe hypotension, we recorded MAP and vasopressor doses hourly. We investigated variability across patients and centres using multivariable regression models and Analysis of variance (ANOVA), respectively. Results: We included data from 56 patients treated in 6 centers. The mean (standard deviation [SD]) age and Acute Physiology and Chronic Health Evaluation (APACHE) II score were 64 (14) and 25 (8). Half (28 of 56) of the patients were at least 65 years old, and half had chronic hypertension. The patient-averaged MAP while receiving vasopressors was 75 mm Hg (6) and the median (1st quartile, 3rd quartile) duration of vasopressor therapy was 43 hours (23, 84). MAP achieved was not associated with history of underlying hypertension (p = 0.46) but did vary by center (p<0.001). Conclusions: In this multicenter, prospective observational study, the patient-level average MAP while receiving vasopressors for severe hypotension was 75 mmHg, approximately 10 mmHg above current recommendations and stated practices. Moreover, our results do not support the notion that clinicians tailor vasopressor therapy to individual patient characteristics such as underlying chronic hypertension but MAP achieved while receiving vasopressors varied by site. [ABSTRACT FROM AUTHOR]

Objective: Most small for gestational age pregnancies are unrecognised before birth, resulting in substantial avoidable perinatal mortality and morbidity. Our objective was to develop multivariable prediction models for small for gestational age combining clinical risk factors and biomarkers at 15±1 weeks’ with ultrasound parameters at 20±1 weeks’ gestation. Methods: Data from 5606 participants in the Screening for Pregnancy Endpoints (SCOPE) cohort study were divided into Training (n = 3735) and Validation datasets (n = 1871). The primary outcomes were All-SGA (small for gestational age with birthweight <10th customised centile), Normotensive-SGA (small for gestational age with a normotensive mother) and Hypertensive-SGA (small for gestational age with an hypertensive mother). The comparison group comprised women without the respective small for gestational age phenotype. Multivariable analysis was performed using stepwise logistic regression beginning with clinical variables, and subsequent additions of biomarker and then ultrasound (biometry and Doppler) variables. Model performance was assessed in Training and Validation datasets by calculating area under the curve. Results: 633 (11.2%) infants were All-SGA, 465(8.2%) Normotensive-SGA and 168 (3%) Hypertensive-SGA. Area under the curve (95% Confidence Intervals) for All-SGA using 15±1 weeks’ clinical variables, 15±1 weeks’ clinical+ biomarker variables and clinical + biomarkers + biometry /Doppler at 20±1 weeks’ were: 0.63 (0.59–0.67), 0.64 (0.60–0.68) and 0.69 (0.66–0.73) respectively in the Validation dataset; Normotensive-SGA results were similar: 0.61 (0.57–0.66), 0.61 (0.56–0.66) and 0.68 (0.64–0.73) with small increases in performance in the Training datasets. Area under the curve (95% Confidence Intervals) for Hypertensive-SGA were: 0.76 (0.70–0.82), 0.80 (0.75–0.86) and 0.84 (0.78–0.89) with minimal change in the Training datasets. Conclusion: Models for prediction of small for gestational age, which combine biomarkers, clinical and ultrasound data from a cohort of low-risk nulliparous women achieved modest performance. Incorporation of biomarkers into the models resulted in no improvement in performance of prediction of All-SGA and Normotensive-SGA but a small improvement in prediction of Hypertensive-SGA. Our models currently have insufficient reliability for application in clinical practice however, they have potential utility in two-staged screening tests which include third trimester biomarkers and or fetal biometry. [ABSTRACT FROM AUTHOR]

Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in a high-dimensional setting defined by a large set of observational data, and 2) uncover potential predictors of diabetes. The Jackson Heart Study collected data at baseline and in two follow-up visits from 5,301 African Americans. We excluded those with baseline diabetes and no follow-up, leaving 3,633 individuals for analyses. Over a mean 8-year follow-up, 584 participants developed diabetes. The full RF model evaluated 93 variables including demographic, anthropometric, blood biomarker, medical history, and echocardiogram data. We also used RF metrics of variable importance to rank variables according to their contribution to diabetes prediction. We implemented other models based on logistic regression and RF where features were preselected. The RF full model performance was similar (AUC = 0.82) to those more parsimonious models. The top-ranked variables according to RF included hemoglobin A1C, fasting plasma glucose, waist circumference, adiponectin, c-reactive protein, triglycerides, leptin, left ventricular mass, high-density lipoprotein cholesterol, and aldosterone. This work shows the potential of RF for incident diabetes prediction while dealing with high-dimensional data. [ABSTRACT FROM AUTHOR]

Given the negative consequences of divorce on health and well-being, it is important to try to identify its predictors. In the current study we used data from the National Survey of Midlife Development (N = 2801) to examine the longitudinal effects of lack of joint goal planning with a romantic relationship partner on divorce over a 10-year period. Multilevel regression analyses showed that lack of joint planning with the relationship partner was associated with a 19% increase in the odds of divorce, even when controlling for various demographic (i.e., age, gender, relationship length, number of children in the household), individual (i.e., neuroticism, positive affect, negative affect, physical symptoms, planning), and relationship (i.e., marital empathy, partner strain, partner disagreement, marital satisfaction, commitment). These results demonstrate the importance of considering one’s partner when making decisions and plans for the future, given that it has clear implications for relationship dissolution. [ABSTRACT FROM AUTHOR]

It is well established that increasing mesh number in the circumference of a diamond-mesh trawl codend can reduce size selection for round fish, whereas selection for flat fish species is unaffected. This effect has also been documented in Mediterranean trawl fisheries. In contrast, no information is available with regard to the effect of increasing mesh number in the circumference of square-mesh codends on the size selection of round fish and flat fish species. A field study was devised to bridge this gap and formulate proposals aimed at improving trawl fishery management. Size selection data were collected for a round fish species, red mullet (Mullus barbatus), and two flat fish species, Mediterranean scaldfish (Arnoglossus laterna) and solenette (Buglossidium luteum). Fishing trials were conducted in the Adriatic Sea (Central Mediterranean) using three square-mesh codends that differed only in mesh number around the circumference. Results demonstrated that increasing the number of meshes from 107 to 213 reduced the 50% retention length (L50) for red mullet by 2.5 cm but did not affect size selection for the two flat fish species. In some fisheries, regulatory provisions regarding the number of meshes in the circumference should therefore be carefully considered both for diamond- and square-mesh codends. [ABSTRACT FROM AUTHOR]

Introduction: Falls are the leading cause of injury in older people. Reducing falls could reduce financial pressures on health services. We carried out this research to develop a falls risk model, using routine primary care and hospital data to identify those at risk of falls, and apply a cost analysis to enable commissioners of health services to identify those in whom savings can be made through referral to a falls prevention service. Methods: Multilevel logistical regression was performed on routinely collected general practice and hospital data from 74751 over 65’s, to produce a risk model for falls. Validation measures were carried out. A cost-analysis was performed to identify at which level of risk it would be cost-effective to refer patients to a falls prevention service. 95% confidence intervals were calculated using a Monte Carlo Model (MCM), allowing us to adjust for uncertainty in the estimates of these variables. Results: A risk model for falls was produced with an area under the curve of the receiver operating characteristics curve of 0.87. The risk cut-off with the highest combination of sensitivity and specificity was at p = 0.07 (sensitivity of 81% and specificity of 78%). The risk cut-off at which savings outweigh costs was p = 0.27 and the risk cut-off with the maximum savings was p = 0.53, which would result in referral of 1.8% and 0.45% of the over 65’s population respectively. Above a risk cut-off of p = 0.27, costs do not exceed savings. Conclusions: This model is the best performing falls predictive tool developed to date; it has been developed on a large UK city population; can be readily run from routine data; and can be implemented in a way that optimises the use of health service resources. Commissioners of health services should use this model to flag and refer patients at risk to their falls service and save resources. [ABSTRACT FROM AUTHOR]

Objectives: Road traffic crashes that involve very high numbers of fatalities and injuries arouse public concern wherever they occur. In China, there are two categories of such crashes: a crash that results in 10–30 fatalities, 50–100 serious injuries or a total cost of 50–100 million RMB ($US8-16m) is a “serious road traffic crash” (SRTC), while a crash that is even more severe or costly is a “particularly serious road traffic crash” (PSRTC). The aim of this study is to identify the main factors affecting different types of these crashes (single-vehicle, head-on, rear-end and side impact) with the ultimate goal of informing prevention activities and policies. Methods: Detailed descriptions of the SRTCs and PSRTCs that occurred from 2007 to 2014 were collected from the database “In-depth Investigation and Analysis System for Major Road Traffic Crashes” (IIASMRTC), which is maintained by the Traffic Management Research Institute of the Ministry of Public Security of China (TMRI). 18 main risk factors, which were categorized into four areas (participant, vehicle, road and environment-related) were chosen as potential independent variables for the multinomial logistic regression analysis. Comparisons were made among the single-vehicle, head-on, rear-end and side impact crashes in terms of factors affecting crash occurrence. Findings: Five risk factors were significant for the six multinomial logistic regression models, which were location, vertical alignment, roadside safety rating, driver distraction and overloading of cargo. It was indicated that intersections were more likely to have side impact SRTCs and PSRTCs, especially with poor visibility at night. Overloaded freight vehicles were more likely to be involved in a rear-end crash than other freight vehicles. Driver distraction is an important risk factor for head-on crashes, while vertical alignment and roadside safety rating are positively associated with single-vehicle crashes. Conclusion: Based on the findings, promising measures were proposed to prevent each type of SRTC and PSRTC, which governmental or regulatory agencies could employ to plan strategies to reduce SRTCs and PSRTCs and support lifesaving policies. [ABSTRACT FROM AUTHOR]

Despite myriad efforts among social scientists, epidemiologists, and clinicians to identify variables with strong linkages to mortality, few researchers have evaluated statistically the relative strength of a comprehensive set of predictors of survival. Here, we determine the strongest predictors of five-year mortality in four national, prospective studies of older adults. We analyze nationally representative surveys of older adults in four countries with similar levels of life expectancy: England (n = 6113, ages 52+), the US (n = 2023, ages 50+), Costa Rica (n = 2694, ages 60+), and Taiwan (n = 1032, ages 53+). Each survey includes a broad set of demographic, social, health, and biological variables that have been shown previously to predict mortality. We rank 57 predictors, 25 of which are available in all four countries, net of age and sex. We use the area under the receiver operating characteristic curve and assess robustness with additional discrimination measures. We demonstrate consistent findings across four countries with different cultural traditions, levels of economic development, and epidemiological transitions. Self-reported measures of instrumental activities of daily living limitations, mobility limitations, and overall self-assessed health are among the top predictors in all four samples. C-reactive protein, additional inflammatory markers, homocysteine, serum albumin, three performance assessments (gait speed, grip strength, and chair stands), and exercise frequency also discriminate well between decedents and survivors when these measures are available. We identify several promising candidates that could improve mortality prediction for both population-based and clinical populations. Better prognostic tools are likely to provide researchers with new insights into the behavioral and biological pathways that underlie social stratification in health and may allow physicians to have more informed discussions with patients about end-of-life treatment and priorities. [ABSTRACT FROM AUTHOR]

Background: Disease progression in multiple sclerosis (MS) is highly variable and predicting prognosis is notoriously challenging. Patients’ prognosis beliefs, responses to prognostic uncertainty and experiences of prognosis-related communication with healthcare professionals (HCPs) have received little study. These issues have implications for patients’ psychological adjustment and are important in the context of the recent development of personalised prognosis forecasting tools. This study explored patient perspectives on the experience of prognostic uncertainty, the formation of expectations about personal prognosis and the nature of received and desired prognosis communication. Methods: 15 MS patients participated in in-depth semi-structured interviews which were analysed using inductive thematic analysis. Results: Six themes captured key aspects of the data: Experiencing unsatisfactory communication with HCPs, Appreciating and accepting prognostic uncertainty, Trying to stay present-focused, Forming and editing personal prognosis beliefs, Ambivalence towards forecasting the future, and Prognosis information delivery. MS patients report having minimal communication with HCPs about prognosis. Over time MS patients appear to develop expectations about their disease trajectories, but do so with minimal HCP input. Provision of prognosis information by HCPs seems to run counter to patients’ attempts to remain present-focused. Patients are often ambivalent about prognosis forecasting and consider it emotionally dangerous and of circumscribed usefulness. Conclusions: HCPs must carefully consider whether, when and how to share prognosis information with patients; specific training may be beneficial. Future research should confirm findings about limited HCP-patient communication, distinguish predictors of patients’ attitudes towards prognostication and identify circumstances under which prognostic forecasting benefits patients. [ABSTRACT FROM AUTHOR]

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the “big data” era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures. [ABSTRACT FROM AUTHOR]

Background: Preterm infants are at high risk of developing respiratory syncytial virus (RSV)-associated lower respiratory tract infection (LRTI). This observational epidemiologic study evaluated RSV disease burden and risk factors for RSV-associated LRTI hospitalization in preterm infants 33 weeks+0 days to 35 weeks+6 days gestational age not receiving RSV prophylaxis. Methods: Preterm infants ≤6 months of age during RSV season (1 October 2013–30 April 2014) were followed at 72 sites across 23 countries from September 2013–July 2014 (study period). RSV testing was performed according to local clinical practice. Factors related to RSV-associated hospitalization for LRTI were identified using multivariable logistic regression with backward selection. Results: Of the 2390 evaluable infants, 204 and 127 were hospitalized for LRTI during the study period and RSV season, respectively. Among these subjects, 64/204 and 46/127, respectively, were hospitalized for confirmed RSV LRTI. Study period and RSV season normalized RSV hospitalization rates (per 100 infant years) were 4.1 and 6.1, respectively. Factors associated with an increased risk of RSV-related LRTI hospitalization in multivariable analyses were smoking of family members (P<0.0001), non-hemodynamically significant congenital heart disease diagnosis (P = 0.0077), maternal age of ≤25 years at delivery (P = 0.0009), low maternal educational level (P = 0.0426), household presence of children aged 4 to 5 years (P = 0.0038), age on 1 October ≤3 months (P = 0.0422), and presence of paternal atopy (P<0.0001). Conclusions: During the 2013–2014 RSV season across 23 countries, for preterm infants 33–35 weeks gestation ≤6 months old on 1 October not receiving RSV prophylaxis, confirmed RSV LRTI hospitalization incidence was 4.1 per 100 infant years during the study period and 6.1 per 100 infant years during the RSV season. This study enhances the findings of single-country studies of common risk factors for severe RSV infection in preterm infants and suggests that combinations of 4–6 risk factors may be used to accurately predict risk of RSV hospitalization. These findings may be useful in the identification of infants most at risk of severe RSV infection. [ABSTRACT FROM AUTHOR]

Background: Low back pain (LBP) is a major health problem. Globally it is responsible for the most years lived with disability. The most problematic type of LBP is chronic LBP (pain lasting longer than 3 mo); it has a poor prognosis and is costly, and interventions are only moderately effective. Targeting interventions according to risk profile is a promising approach to prevent the onset of chronic LBP. Developing accurate prognostic models is the first step. No validated prognostic models are available to accurately predict the onset of chronic LBP. The primary aim of this study was to develop and validate a prognostic model to estimate the risk of chronic LBP.Methods and Findings: We used the PROGRESS framework to specify a priori methods, which we published in a study protocol. Data from 2,758 patients with acute LBP attending primary care in Australia between 5 November 2003 and 15 July 2005 (development sample, n = 1,230) and between 10 November 2009 and 5 February 2013 (external validation sample, n = 1,528) were used to develop and externally validate the model. The primary outcome was chronic LBP (ongoing pain at 3 mo). In all, 30% of the development sample and 19% of the external validation sample developed chronic LBP. In the external validation sample, the primary model (PICKUP) discriminated between those who did and did not develop chronic LBP with acceptable performance (area under the receiver operating characteristic curve 0.66 [95% CI 0.63 to 0.69]). Although model calibration was also acceptable in the external validation sample (intercept = -0.55, slope = 0.89), some miscalibration was observed for high-risk groups. The decision curve analysis estimated that, if decisions to recommend further intervention were based on risk scores, screening could lead to a net reduction of 40 unnecessary interventions for every 100 patients presenting to primary care compared to a "treat all" approach. Limitations of the method include the model being restricted to using prognostic factors measured in existing studies and using stepwise methods to specify the model. Limitations of the model include modest discrimination performance. The model also requires recalibration for local settings.Conclusions: Based on its performance in these cohorts, this five-item prognostic model for patients with acute LBP may be a useful tool for estimating risk of chronic LBP. Further validation is required to determine whether screening with this model leads to a net reduction in unnecessary interventions provided to low-risk patients. [ABSTRACT FROM AUTHOR]

Background: Prognostic assessment in patients with hepatocellular carcinoma (HCC) remains controversial. Using the Italian Liver Cancer (ITA.LI.CA) database as a training set, we sought to develop and validate a new prognostic system for patients with HCC.Methods and Findings: Prospective collected databases from Italy (training cohort, n = 3,628; internal validation cohort, n = 1,555) and Taiwan (external validation cohort, n = 2,651) were used to develop the ITA.LI.CA prognostic system. We first defined ITA.LI.CA stages (0, A, B1, B2, B3, C) using only tumor characteristics (largest tumor diameter, number of nodules, intra- and extrahepatic macroscopic vascular invasion, extrahepatic metastases). A parametric multivariable survival model was then used to calculate the relative prognostic value of ITA.LI.CA tumor stage, Eastern Cooperative Oncology Group (ECOG) performance status, Child-Pugh score (CPS), and alpha-fetoprotein (AFP) in predicting individual survival. Based on the model results, an ITA.LI.CA integrated prognostic score (from 0 to 13 points) was constructed, and its prognostic power compared with that of other integrated systems (BCLC, HKLC, MESIAH, CLIP, JIS). Median follow-up was 58 mo for Italian patients (interquartile range, 26-106 mo) and 39 mo for Taiwanese patients (interquartile range, 12-61 mo). The ITA.LI.CA integrated prognostic score showed optimal discrimination and calibration abilities in Italian patients. Observed median survival in the training and internal validation sets was 57 and 61 mo, respectively, in quartile 1 (ITA.LI.CA score ≤ 1), 43 and 38 mo in quartile 2 (ITA.LI.CA score 2-3), 23 and 23 mo in quartile 3 (ITA.LI.CA score 4-5), and 9 and 8 mo in quartile 4 (ITA.LI.CA score > 5). Observed and predicted median survival in the training and internal validation sets largely coincided. Although observed and predicted survival estimations were significantly lower (log-rank test, p < 0.001) in Italian than in Taiwanese patients, the ITA.LI.CA score maintained very high discrimination and calibration features also in the external validation cohort. The concordance index (C index) of the ITA.LI.CA score in the internal and external validation cohorts was 0.71 and 0.78, respectively. The ITA.LI.CA score's prognostic ability was significantly better (p < 0.001) than that of BCLC stage (respective C indexes of 0.64 and 0.73), CLIP score (0.68 and 0.75), JIS stage (0.67 and 0.70), MESIAH score (0.69 and 0.77), and HKLC stage (0.68 and 0.75). The main limitations of this study are its retrospective nature and the intrinsically significant differences between the Taiwanese and Italian groups.Conclusions: The ITA.LI.CA prognostic system includes both a tumor staging-stratifying patients with HCC into six main stages (0, A, B1, B2, B3, and C)-and a prognostic score-integrating ITA.LI.CA tumor staging, CPS, ECOG performance status, and AFP. The ITA.LI.CA prognostic system shows a strong ability to predict individual survival in European and Asian populations. [ABSTRACT FROM AUTHOR]

When a dataset is imbalanced, the prediction of the scarcely-sampled subpopulation can be over-influenced by the population contributing to the majority of the data. The aim of this study was to develop a Bayesian modelling approach with balancing informative prior so that the influence of imbalance to the overall prediction could be minimised. The new approach was developed in order to weigh the data in favour of the smaller subset(s). The method was assessed in terms of bias and precision in predicting model parameter estimates of simulated datasets. Moreover, the method was evaluated in predicting optimal dose levels of tobramycin for various age groups in a motivating example. The bias estimates using the balancing informative prior approach were smaller than those generated using the conventional approach which was without the consideration for the imbalance in the datasets. The precision estimates were also superior. The method was further evaluated in a motivating example of optimal dosage prediction of tobramycin. The resulting predictions also agreed well with what had been reported in the literature. The proposed Bayesian balancing informative prior approach has shown a real potential to adequately weigh the data in favour of smaller subset(s) of data to generate robust prediction models. [ABSTRACT FROM AUTHOR]

Background: Cystic Fibrosis (CF) is characterized by chronically inflamed airways, and inflammation even increases during pulmonary exacerbations. These adverse events have an important influence on the well-being, quality of life, and lung function of patients with CF. Prediction of exacerbations by inflammatory markers in exhaled breath condensate (EBC) combined with early treatment may prevent these pulmonary exacerbations and may improve the prognosis. Aim: To investigate the diagnostic accuracy of a set of inflammatory markers in EBC to predict pulmonary exacerbations in children with CF. Methods: In this one-year prospective observational study, 49 children with CF were included. During study visits with an interval of 2 months, a symptom questionnaire was completed, EBC was collected, and lung function measurements were performed. The acidity of EBC was measured directly after collection. Inflammatory markers interleukin (IL)-6, IL-8, tumor necrosis factor α (TNF-α), and macrophage migration inhibitory factor (MIF) were measured using high sensitivity bead based flow immunoassays. Pulmonary exacerbations were recorded during the study and were defined in two ways. The predictive power of inflammatory markers and the other covariates was assessed using conditionally specified models and a receiver operating characteristic curve (SAS version 9.2). In addition, k-nearest neighbors (KNN) algorithm was applied (SAS version 9.2). Results: Sixty-five percent of the children had one or more exacerbations during the study. The conditionally specified models showed an overall correct prediction rate of 55%. The area under the curve (AUC) was equal to 0.62. The results obtained with the KNN algorithm were very similar. Conclusion: Although there is some evidence indicating that the predictors outperform random guessing, the general diagnostic accuracy of EBC acidity and the EBC inflammatory markers IL-6, IL-8, TNF-α and MIF is low. At present it is not possible to predict pulmonary exacerbations in children with CF with the chosen biomarkers and the method of EBC analysis. The biochemical measurements of EBC markers should be improved and other techniques should be considered. [ABSTRACT FROM AUTHOR]

Background: We determined the generalisability and cost-impact of adopting antibiotic-impregnated CVCs in all paediatric intensive care units (PICUs) in England, based on results from a large randomised controlled trial (the CATCH trial; ISRCTN34884569). Methods: BSI rates using standard CVCs were estimated through linkage of national PICU audit data (PICANet) with laboratory surveillance data. We estimated the number of BSI averted if PICUs switched from standard to antibiotic-impregnated CVCs by applying the CATCH trial rate-ratio (0.40; 95% CI 0.17,0.97) to the BSI rate using standard CVCs. The value of healthcare resources made available by averting one BSI as estimated from the trial economic analysis was £10,975; 95% CI -£2,801,£24,751. Results: The BSI rate using standard CVCs was 4.58 (95% CI 4.42,4.74) per 1000 CVC-days in 2012. Applying the rate-ratio gave 232 BSI averted using antibiotic CVCs. The additional cost of purchasing antibiotic-impregnated compared with standard CVCs was £36 for each child, corresponding to additional costs of £317,916 for an estimated 8831 CVCs required in PICUs in 2012. Based on 2012 BSI rates, management of BSI in PICUs cost £2.5 million annually (95% uncertainty interval: -£160,986, £5,603,005). The additional cost of antibiotic CVCs would be less than the value of resources associated with managing BSI in PICUs with standard BSI rates >1.2 per 1000 CVC-days. Conclusions: The cost of introducing antibiotic-impregnated CVCs is less than the cost associated with managing BSIs occurring with standard CVCs. The long-term benefits of preventing BSI could mean that antibiotic CVCs are cost-effective even in PICUs with extremely low BSI rates. [ABSTRACT FROM AUTHOR]

Objectives: The main purpose of the present meta-analysis was to examine the criterion-related validity of the distance- and time-based walk/run tests for estimating cardiorespiratory fitness among apparently healthy children and adults. Materials and Methods: Relevant studies were searched from seven electronic bibliographic databases up to August 2015 and through other sources. The Hunter-Schmidt’s psychometric meta-analysis approach was conducted to estimate the population criterion-related validity of the following walk/run tests: 5,000 m, 3 miles, 2 miles, 3,000 m, 1.5 miles, 1 mile, 1,000 m, ½ mile, 600 m, 600 yd, ¼ mile, 15 min, 12 min, 9 min, and 6 min. Results: From the 123 included studies, a total of 200 correlation values were analyzed. The overall results showed that the criterion-related validity of the walk/run tests for estimating maximum oxygen uptake ranged from low to moderate (rp = 0.42–0.79), with the 1.5 mile (rp = 0.79, 0.73–0.85) and 12 min walk/run tests (rp = 0.78, 0.72–0.83) having the higher criterion-related validity for distance- and time-based field tests, respectively. The present meta-analysis also showed that sex, age and maximum oxygen uptake level do not seem to affect the criterion-related validity of the walk/run tests. Conclusions: When the evaluation of an individual’s maximum oxygen uptake attained during a laboratory test is not feasible, the 1.5 mile and 12 min walk/run tests represent useful alternatives for estimating cardiorespiratory fitness. As in the assessment with any physical fitness field test, evaluators must be aware that the performance score of the walk/run field tests is simply an estimation and not a direct measure of cardiorespiratory fitness. [ABSTRACT FROM AUTHOR]

Background: Pediatric hospital mortality from infectious diseases in resource constrained countries remains unacceptably high. Improved methods of risk-stratification can assist in referral decision making and resource allocation. The purpose of this study was to create prediction models for in-hospital mortality among children admitted with suspected infectious diseases. Methods: This two-site prospective observational study enrolled children between 6 months and 5 years admitted with a proven or suspected infection. Baseline clinical and laboratory variables were collected on enrolled children. The primary outcome was death during admission. Stepwise logistic regression minimizing Akaike’s information criterion was used to identify the most promising multivariate models. The final model was chosen based on parsimony. Results: 1307 children were enrolled consecutively, and 65 (5%) of whom died during their admission. Malaria, pneumonia and gastroenteritis were diagnosed in 50%, 31% and 8% of children, respectively. The primary model included an abnormal Blantyre coma scale, HIV and weight-for-age z-score. This model had an area under the curve (AUC) of 0.85 (95% CI, 0.80–0.89) with a sensitivity and specificity of 83% and 76%, respectively. The positive and negative predictive values were 15% and 99%, respectively. Two alternate models with similar performance characteristics were developed withholding HIV and weight-for-age z-score, for use when these variables are not available. Conclusions: Risk stratification of children admitted with infectious diseases can be calculated based on several easily measured variables. Risk stratification at admission can be used for allocation of scarce human and physical resources and to guide referral among children admitted to lower level health facilities. [ABSTRACT FROM AUTHOR]

In this article, we report findings from an original survey experiment investigating the effects of different framings of disease threats on individual risk perceptions and policy priorities. We analyze responses from 1,946 white and African-American participants in a self-administered, web-based survey in the United States. We sought to investigate the effects of: 1) frames emphasizing disparities in the racial prevalence of disease and 2) frames emphasizing non-normative (blameworthy or stigmatized) behavioral risk factors. We find some evidence that when treated with the first frame, African-Americans are more likely to report higher risk of infection (compared to an African-American control group and to whites receiving the same treatment); and that whites are more likely to report trust in government data (compared to a White control group and to African-Americans receiving the same treatment). Notwithstanding, we find no support for our hypotheses concerning the interactive effects of providing both frames, which was a central motivation for our study. We argue that this may be due to very large differences in risk perception at baseline (which generate limits on possible treatment effects) and the fact that in the context of American race relations, it may not be possible to fully differentiate racialized and stigmatized frames. [ABSTRACT FROM AUTHOR]

Introduction: There is high variability amongst physicians’ assessments of appropriate ICU admissions, which may be based on potential assessments of benefit. We aimed to examine whether opinions over benefit of ICU admissions of critically ill medical inpatients differed based on physician specialty, namely intensivists and internists. Materials and Methods: We carried out an anonymous, web-based questionnaire survey containing 5 typical ICU cases to all ICU physicians regardless of their base specialty as well as to all internists in 3 large teaching hospitals. For each case, we asked the participants to determine if the patient was an appropriate ICU admission and to assess different parameters (e.g. baseline function, likelihood of survival to ICU discharge, etc.). Agreement was measured using kappa values. Results: 21 intensivists and 22 internists filled out the survey (response rate = 87.5% and 35% respectively). Predictions of likelihood of survival to ICU admission, hospital discharge and return to baseline were not significantly different between the two groups. However, agreement between individuals within each group was only slight to fair (kappa range = 0.09–0.22). There was no statistically significant difference in predicting ICU survival and prediction of survival to hospital discharge between both groups. The accuracy with which physicians predicted actual outcomes ranged between 35% and 100% and did not significantly differ between the two groups. A greater proportion of internists favoured non resuscitative measures (24.6% of intensivists and 46.9% internists [p = 0.002]). Conclusion: In a case-based survey, physician specialty base did not affect assessments of ICU admission benefit or accuracy in outcome prediction, but resulted in a statistically significant difference in level of care assignments. Of note, significant disagreement amongst individuals in each group was found. [ABSTRACT FROM AUTHOR]