Survival Rates in Trauma Patients Following Health Care Reform in Massachusetts

IMPORTANCE: Massachusetts introduced health care reform (HCR) in 2006, expecting to expand health insurance coverage and improve outcomes. Because traumatic injury is a common acute condition with important health, disability, and economic consequences, examination of the effect of HCR on patients hospitalized following injury may help inform the national HCR debate. OBJECTIVE: To examine the effect of Massachusetts HCR on survival rates of injured patients. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of 1,520,599 patients hospitalized following traumatic injury in Massachusetts or New York during the 10 years (2002-2011) surrounding Massachusetts HCR using data from the State Inpatient Databases. We assessed the effect of HCR on mortality rates using a difference-in-differences approach to control for temporal trends in mortality. INTERVENTION: Health care reform in Massachusetts in 2006. MAIN OUTCOME AND MEASURE: Survival until hospital discharge. RESULTS: During the 10-year study period, the rates of uninsured trauma patients in Massachusetts decreased steadily from 14.9% in 2002 to 5.0.% in 2011. In New York, the rates of uninsured trauma patients fell from 14.9% in 2002 to 10.5% in 2011. The risk-adjusted difference-in-difference assessment revealed a transient increase of 604 excess deaths (95% CI, 419-790) in Massachusetts in the 3 years following implementation of HCR. CONCLUSIONS AND RELEVANCE: Health care reform did not affect health insurance coverage for patients hospitalized following injury but was associated with a transient increase in adjusted mortality rates. Reducing mortality rates for acutely injured patients may require more comprehensive interventions than simply promoting health insurance coverage through legislation

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Trust and control interrelations: New perspectives on the trust control nexus

This article is the post-print version of the published article that may be accessed at the link below. Copyright @ 2007 Sage Publications.This article introduces the special issue on New Perspectives on the Trust-Control Nexus in Organizational Relations. Trust and control are interlinked processes commonly seen as key to reach effectiveness in inter- and intraorganizational relations. The relation between trust and control is, however, a complex one, and research into this relation has given rise to various and contradictory interpretations of how trust and control relate. A well-known discussion is directed at whether trust and control are better conceived as substitutes, or as complementary mechanisms of governance. The articles in this special issue bring the discussion on the relationship between both concepts a step further by identifying common factors, distinctive mechanisms, and key implications relevant for theory building and empirical research. By studying trust and control through different perspectives and at different levels of analysis, the articles provide new theoretical insights and empirical evidence on the foundations of the trust-control interrelations

ASCORE: an up-to-date cardiovascular risk score for hypertensive patients reflecting contemporary clinical practice developed using the (ASCOT-BPLA) trial data.

A number of risk scores already exist to predict cardiovascular (CV) events. However, scores developed with data collected some time ago might not accurately predict the CV risk of contemporary hypertensive patients that benefit from more modern treatments and management. Using data from the randomised clinical trial Anglo-Scandinavian Cardiac Outcomes Trial-BPLA, with 15 955 hypertensive patients without previous CV disease receiving contemporary preventive CV management, we developed a new risk score predicting the 5-year risk of a first CV event (CV death, myocardial infarction or stroke). Cox proportional hazard models were used to develop a risk equation from baseline predictors. The final risk model (ASCORE) included age, sex, smoking, diabetes, previous blood pressure (BP) treatment, systolic BP, total cholesterol, high-density lipoprotein-cholesterol, fasting glucose and creatinine baseline variables. A simplified model (ASCORE-S) excluding laboratory variables was also derived. Both models showed very good internal validity. User-friendly integer score tables are reported for both models. Applying the latest Framingham risk score to our data significantly overpredicted the observed 5-year risk of the composite CV outcome. We conclude that risk scores derived using older databases (such as Framingham) may overestimate the CV risk of patients receiving current BP treatments; therefore, 'updated' risk scores are needed for current patients

Correlates of Complete Childhood Vaccination in East African Countries.

Despite the benefits of childhood vaccinations, vaccination rates in low-income countries (LICs) vary widely. Increasing coverage of vaccines to 90% in the poorest countries over the next 10 years has been estimated to prevent 426 million cases of illness and avert nearly 6.4 million childhood deaths worldwide. Consequently, we sought to provide a comprehensive examination of contemporary vaccination patterns in East Africa and to identify common and country-specific barriers to complete childhood vaccination. Using data from the Demographic and Health Surveys (DHS) for Burundi, Ethiopia, Kenya, Rwanda, Tanzania, and Uganda, we looked at the prevalence of complete vaccination for polio, measles, Bacillus Calmette-Guérin (BCG) and DTwPHibHep (DTP) as recommended by the WHO among children ages 12 to 23 months. We conducted multivariable logistic regression within each country to estimate associations between complete vaccination status and health care access and sociodemographic variables using backwards stepwise regression. Vaccination varied significantly by country. In all countries, the majority of children received at least one dose of a WHO recommended vaccine; however, in Ethiopia, Tanzania, and Uganda less than 50% of children received a complete schedule of recommended vaccines. Being delivered in a public or private institution compared with being delivered at home was associated with increased odds of complete vaccination status. Sociodemographic covariates were not consistently associated with complete vaccination status across countries. Although no consistent set of predictors accounted for complete vaccination status, we observed differences based on region and the location of delivery. These differences point to the need to examine the historical, political, and economic context of each country in order to maximize vaccination coverage. Vaccination against these childhood diseases is a critical step towards reaching the Millennium Development Goal of reducing under-five mortality by two-thirds by 2015 and thus should be a global priority

Prevalence of psychosis in black ethnic minorities in Britain: analysis based on three national surveys

Purpose A considerable excess of psychosis in black ethnic minorities is apparent from clinical studies, in Britain, as in other developed economies with white majority populations. This excess is not so marked in population surveys. Equitable health service provision should be informed by the best estimates of the excess. We used national survey data to establish the difference in the prevalence of psychosis between black ethnic groups and the white majority in the British general population. Methods Analysis of the combined datasets (N = 26,091) from the British national mental health surveys of 1993, 2000 and 2007. Cases of psychosis were determined either by the use of the Schedules for Clinical Assessment in Neuropsychiatry (SCAN), or from a combination of screening items. We controlled for sex, age, social class, unemployment, design features and other putative confounders, using a Disease Risk Score. Results People from black ethnic minorities had an excess prevalence rate of psychosis compared with the white majority population. The OR, weighted for study design and response rate, was 2.72 (95 % CI 1.3–5.6, p = 0.002). This was marginally increased after controlling for potential confounders (OR = 2.90, 95 % CI 1.4–6.2, p = 0.006). Conclusions The excess of psychosis in black ethnic minority groups was similar to that in two previous British community surveys, and less than that based on clinical studies. Even so it confirms a considerable need for increased mental health service resources in areas with high proportions of black ethnic minority inhabitants

Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches

Background: Missing data is classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying the most appropriate analysis. The first aim was to compare different methods for identifying this missing data mechanism to determine if they gave consistent conclusions. Secondly, to investigate whether the reminder-response data can be utilised to help identify the missing data mechanism. Methods: Five clinical trial datasets that employed a reminder system at follow-up were used. Some quality of life questionnaires were initially missing, but later recovered through reminders. Four methods of determining the missing data mechanism were applied. Two response data scenarios were considered. Firstly, immediate data only; secondly, all observed responses (including reminder-response). Results: In three of five trials the hypothesis tests found evidence against the MCAR assumption. Logistic regression suggested MAR, but was able to use the reminder-collected data to highlight potential MNAR data in two trials. Conclusion: The four methods were consistent in determining the missingness mechanism. One hypothesis test was preferred as it is applicable with intermittent missingness. Some inconsistencies between the two data scenarios were found. Ignoring the reminder data could potentially give a distorted view of the missingness mechanism. Utilising reminder data allowed the possibility of MNAR to be considered.The Chief Scientist Office of the Scottish Government Health Directorate. Research Training Fellowship (CZF/1/31

The Five Factor Model of personality and evaluation of drug consumption risk

The problem of evaluating an individual's risk of drug consumption and misuse is highly important. An online survey methodology was employed to collect data including Big Five personality traits (NEO-FFI-R), impulsivity (BIS-11), sensation seeking (ImpSS), and demographic information. The data set contained information on the consumption of 18 central nervous system psychoactive drugs. Correlation analysis demonstrated the existence of groups of drugs with strongly correlated consumption patterns. Three correlation pleiades were identified, named by the central drug in the pleiade: ecstasy, heroin, and benzodiazepines pleiades. An exhaustive search was performed to select the most effective subset of input features and data mining methods to classify users and non-users for each drug and pleiad. A number of classification methods were employed (decision tree, random forest, $k$-nearest neighbors, linear discriminant analysis, Gaussian mixture, probability density function estimation, logistic regression and na{\"i}ve Bayes) and the most effective classifier was selected for each drug. The quality of classification was surprisingly high with sensitivity and specificity (evaluated by leave-one-out cross-validation) being greater than 70\% for almost all classification tasks. The best results with sensitivity and specificity being greater than 75\% were achieved for cannabis, crack, ecstasy, legal highs, LSD, and volatile substance abuse (VSA).Comment: Significantly extended report with 67 pages, 27 tables, 21 figure