Multi-source statistics:Basic situations and methods

Many National Statistical Institutes (NSIs), especially in Europe, are moving from single‐source statistics to multi‐source statistics. By combining data sources, NSIs can produce more detailed and more timely statistics and respond more quickly to events in society. By combining survey data with already available administrative data and Big Data, NSIs can save data collection and processing costs and reduce the burden on respondents. However, multi‐source statistics come with new problems that need to be overcome before the resulting output quality is sufficiently high and before those statistics can be produced efficiently. What complicates the production of multi‐source statistics is that they come in many different varieties as data sets can be combined in many different ways. Given the rapidly increasing importance of producing multi‐source statistics in Official Statistics, there has been considerable research activity in this area over the last few years, and some frameworks have been developed for multi‐source statistics. Useful as these frameworks are, they generally do not give guidelines to which method could be applied in a certain situation arising in practice. In this paper, we aim to fill that gap, structure the world of multi‐source statistics and its problems and provide some guidance to suitable methods for these problems

Effects of acute substance use and pre-injury substance abuse on traumatic brain injury severity in adults admitted to a trauma centre

<p>Abstract</p> <p>Background</p> <p>The aims of this study were to describe the occurrence of substance use at the time of injury and pre-injury substance abuse in patients with moderate-to-severe traumatic brain injury (TBI). Effects of acute substance use and pre-injury substance abuse on TBI severity were also investigated.</p> <p>Methods</p> <p>A prospective study of 111 patients, aged 16-55 years, injured from May 2005 to May 2007 and hospitalised at the Trauma Referral Centre in Eastern Norway with acute TBI (Glasgow Coma Scale 3-12). Based on structural brain damages shown on a computed tomography (CT) scan, TBI severity was defined by modified Marshall classification as less severe (score <3) and more severe (score ≥3). Clinical definition of substance use (alcohol and/or other psychoactive substances) was applied when hospital admission records reflected blood alcohol levels or a positive drug screen, or when a physician verified influence by examining the patient. Pre-injury substance abuse (alcohol and drug problems) was screened by using the CAGE questionnaire.</p> <p>Results</p> <p>Forty-seven percent of patients were positive for substance use on admission to hospital. Significant pre-injury substance abuse was reported by 26% of patients. Substance use at the time of injury was more frequent in the less severe group (p = 0.01). The frequency of pre-injury substance abuse was higher in the more severe group (30% vs. 23%). In a logistic regression model, acute substance use at time of injury tended to decrease the probability of more severe intracranial injury, but the effect was not statistically significant after adjusting for age, gender, education, cause of injury and substance abuse, OR = 0.39; 95% CI 0.11-1.35, p = 0.14. Patients with positive screens for pre-injury substance abuse (CAGE ≥2) were more likely to have more severe TBI in the adjusted regression analyses, OR = 4.05; 95% CI 1.10-15.64, p = 0.04.</p> <p>Conclusions</p> <p>Acute <b>s</b>ubstance use was more frequent in patients with less severe TBI caused by low-energy events such as falls, violence and sport accidents. Pre-injury substance abuse increased the probability of more severe TBI caused by high-energy trauma such as motor vehicle accidents and falls from higher levels. Preventive efforts to reduce substance consumption and abuse in at-risk populations are needed.</p

Gender differences in self reported long term outcomes following moderate to severe traumatic brain injury

<p>Abstract</p> <p>Background</p> <p>The majority of research on health outcomes after a traumatic brain injury is focused on male participants. Information examining gender differences in health outcomes post traumatic brain injury is limited. The purpose of this study was to investigate gender differences in symptoms reported after a traumatic brain injury and to examine the degree to which these symptoms are problematic in daily functioning.</p> <p>Methods</p> <p>This is a secondary data analysis of a retrospective cohort study of 306 individuals who sustained a moderate to severe traumatic brain injury 8 to 24 years ago. Data were collected using the Problem Checklist (PCL) from the Head Injury Family Interview (HIFI). Using Bonferroni correction, group differences between women and men were explored using Chi-square and Wilcoxon analysis.</p> <p>Results</p> <p>Chi-square analysis by gender revealed that significantly more men reported difficulty setting realistic goals and restlessness whereas significantly more women reported headaches, dizziness and loss of confidence. Wilcoxon analysis by gender revealed that men reported sensitivity to noise and sleep disturbances as significantly more problematic than women, whereas for women, lack of initiative and needing supervision were significantly more problematic in daily functioning.</p> <p>Conclusion</p> <p>This study provides insight into gender differences on outcomes after traumatic brain injury. There are significant differences between problems reported by men compared to women. This insight may facilitate health service planners and clinicians when developing programs for individuals with brain injury.</p

Protocol compliance and time management in blunt trauma resuscitation.

Item does not contain fulltextOBJECTIVES: To study advanced trauma life support (ATLS) protocol adherence prospectively in trauma resuscitation and to analyse time management of daily multidisciplinary trauma resuscitation at a level 1 trauma centre, for both moderately and severely injured patients. PATIENTS AND METHODS: All victims of severe blunt trauma were consecutively included. Patients with a revised trauma score (RTS) of 12 were resuscitated by a "minor trauma" team and patients with an RTS of less than 12 were resuscitated by a "severe trauma" team. Digital video recordings were used to analyse protocol compliance and time management during initial assessment. RESULTS: From 1 May to 1 September 2003, 193 resuscitations were included. The "minor trauma" team assessed 119 patients, with a mean injury severity score (ISS) of 7 (range 1-45). Overall protocol compliance was 42%, ranging from 0% for thoracic percussion to 93% for thoracic auscultation. The median resuscitation time was 45.9 minutes (range 39.7-55.9). The "severe team" assessed 74 patients, with a mean ISS of 22 (range 1-59). Overall protocol compliance was 53%, ranging from 4% for thoracic percussion to 95% for thoracic auscultation. Resuscitation took 34.8 minutes median (range 21.6-44.1). CONCLUSION: Results showed the current trauma resuscitation to be ATLS-like, with sometimes very low protocol compliance rates. Timing of secondary survey and radiology and thus time efficiency remains a challenge in all trauma patients. To assess the effect of trauma resuscitation protocols on outcome, protocol adherence needs to be improved

A Bayesian analysis of design parameters in survey data collection

In the design of surveys, a number of input parameters such as contact propensities, participation propensities, and costs per sample unit play a decisive role. In ongoing surveys, these survey design parameters are usually estimated from previous experience and updated gradually with new experience. In new surveys, these parameters are estimated from expert opinion and experience with similar surveys. Although survey institutes have fair expertise and experience, the postulation, estimation, and updating of survey design parameters is rarely done in a systematic way. This article presents a Bayesian framework to include and update prior knowledge and expert opinion about the parameters. This framework is set in the context of adaptive survey designs in which different population units may receive different treatment given quality and cost objectives. For this type of survey, the accuracy of design parameters becomes even more crucial to effective design decisions. The framework allows for a Bayesian analysis of the performance of a survey during data collection and in between waves of a survey. We demonstrate the utility of the Bayesian analysis using a simulation study based on the Dutch Health Survey

A Bayesian analysis of design parameters in survey data collection

In the design of surveys, a number of input parameters such as contact propensities, participation propensities, and costs per sample unit play a decisive role. In ongoing surveys, these survey design parameters are usually estimated from previous experience and updated gradually with new experience. In new surveys, these parameters are estimated from expert opinion and experience with similar surveys. Although survey institutes have fair expertise and experience, the postulation, estimation, and updating of survey design parameters is rarely done in a systematic way. This article presents a Bayesian framework to include and update prior knowledge and expert opinion about the parameters. This framework is set in the context of adaptive survey designs in which different population units may receive different treatment given quality and cost objectives. For this type of survey, the accuracy of design parameters becomes even more crucial to effective design decisions. The framework allows for a Bayesian analysis of the performance of a survey during data collection and in between waves of a survey. We demonstrate the utility of the Bayesian analysis using a simulation study based on the Dutch Health Survey