Statistical data for special education services; annual report.

Description based on: fiscal year 1969; title from cover.

Quadrennial statistical report of services to crippled and afflicted children.

Description based on: July 1, 1943 through June 30, 1947

Demand for manufactured foods, manufacturers' services, and farm products in food manufacturing : a statistical analysis /

Caption title.

Statistical report of Division of Services to Crippled Children.

"Report of the services, and expenditures for medical care of crippled and afflicted children for the fiscal years ..."

Quality-based benefit design in health insurance : the impact of a product benefit design change on the utilisation of oral health services by members of a private health insurance fund in regional and rural New South Wales, Australia

Objective: To examine the impact on dental utilisation following the introduction of a participating provider scheme (Regional and Rural Oral Health Program {RROHP)). In this model dentists receive higher third party payments from a private health insurance fund for delivering an agreed range of preventive and diagnostic benefits at no out-ofpocket cost to insured patients. Data source/Study setting: Hospitals Contribution Fund of Australia (HCF) dental claims for all members resident in New South Wales over the six financial years from l99811999 to 200312004. Study design: This cohort study involves before and after analyses of dental claims experience over a six year period for approximately 81,000 individuals in the intervention group (HCF members resident in regional and rural New South Wales, Australia) and 267,000 in the control group (HCF members resident in the Sydney area). Only claims for individuals who were members of HCF at 31 December 1997 were included. The analysis groups claims into the three years prior to the establishment of the RROHP and the three years subsequent to implementation. Data collection/Extraction methods: The analysis is based on all claims submitted by users of services for visits between 1 July 1988 and 30 June 2004. In these data approximately 1,000,000 services were provided to the intervention group and approximately 4,900,000 in the control group. Principal findings: Using Statistical Process Control (SPC) charts, special cause variation was identified in total utilisation rate of private dental services in the intervention group post implementation. No such variation was present in the control group. On average in the three years after implementation of the program the utilisation rate of dental services by regional and rural residents of New South Wales who where members of HCF grew by 12.6%, over eight times the growth rate of 1.5% observed in the control group (HCF members who were Sydney residents). The differences were even more pronounced in the areas of service that were the focus of the program: diagnostic and preventive services. Conclusion: The implementation of a benefit design change, a participating provider scheme, that involved the removal of CO-payments on a defined range of preventive and diagnostic dental services combined with the establishment and promotion of a network of dentists, appears to have had a marked impact on HCF members' utilisation of dental services in regional and rural New South Wales, Australia.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states—perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of “excess” zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to “excess” zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed—and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Disseminação de dados estatísticos georeferenciados através de WEB services e software livre.

Diferentes organizações públicas e privadas coletam e disponibilizam uma massa de dados sobre a realidade sócio-econômica das diferentes nações. Há hoje, da parte do governo brasileiro, um interesse manifesto de divulgar uma gama diferenciada de informações para os mais diversos perfis de usuários. Persiste, contudo, uma série de limitações para uma divulgação mais massiva e democrática, entre elas, a heterogeneidade das fontes de dados, sua dispersão e formato de apresentação pouco amigável. Devido à complexidade inerente à informação geográfica envolvida, que produz incompatibilidade em vários níveis, o intercâmbio de dados em sistemas de informação geográfica não é problema trivial. Para aplicações desenvolvidas para a Web, uma solução são os Web Services que permitem que novas aplicações possam interagir com aquelas que já existem e que sistemas desenvolvidos em plataformas diferentes sejam compatíveis. Neste sentido, o objetivo do trabalho é mostrar as possibilidades de construção de portais usando software livre, a tecnologia dos Web Services e os padrões do Open Geospatial Consortium (OGC) para a disseminação de dados espaciais. Visando avaliar e testar as tecnologias selecionadas e comprovar sua efetividade foi desenvolvido um exemplo de portal de dados sócio-econômicos, compreendendo informações de um servidor local e de servidores remotos. As contribuições do trabalho são a disponibilização de mapas dinâmicos, a geração de mapas através da composição de mapas disponibilizados em servidores remotos e local e o uso do padrão OGC WMC. Analisando o protótipo de portal construído, verifica-se, contudo, que a localização e requisição de Web Services não são tarefas fáceis para um usuário típico da Internet. Nesta direção, os trabalhos futuros no domínio dos portais de informação geográfica poderiam adotar a tecnologia Representational State Transfer (REST).

The utility of survey and administrative data to generate information for research and outcomes-based oral health services development

The aim of this research, which focused on the Irish adult population, was to generate information for policymakers by applying statistical analyses and current technologies to oral health administrative and survey databases. Objectives included identifying socio-demographic influences on oral health and utilisation of dental services, comparing epidemiologically-estimated dental treatment need with treatment provided, and investigating the potential of a dental administrative database to provide information on utilisation of services and the volume and types of treatment provided over time. Information was extracted from the claims databases for the Dental Treatment Benefit Scheme (DTBS) for employed adults and the Dental Treatment Services Scheme (DTSS) for less-well-off adults, the National Surveys of Adult Oral Health, and the 2007 Survey of Lifestyle Attitudes and Nutrition in Ireland. Factors associated with utilisation and retention of natural teeth were analysed using count data models and logistic regression. The chi-square test and the student’s t-test were used to compare epidemiologically-estimated need in a representative sample of adults with treatment provided. Differences were found in dental care utilisation and tooth retention by Socio-Economic Status. An analysis of the five-year utilisation behaviour of a 2003 cohort of DTBS dental attendees revealed that age and being female were positively associated with visiting annually and number of treatments. Number of adults using the DTBS increased, and mean number of treatments per patient decreased, between 1997 and 2008. As a percentage of overall treatments, restorations, dentures, and extractions decreased, while prophylaxis increased. Differences were found between epidemiologically-estimated treatment need and treatment provided for those using the DTBS and DTSS. This research confirms the utility of survey and administrative data to generate knowledge for policymakers. Public administrative databases have not been designed for research purposes, but they have the potential to provide a wealth of knowledge on treatments provided and utilisation patterns.

Transition from Children’s to Adult Services forYoung People with Life-limiting Conditions: Findings from a Questionnaire Survey in Northern Ireland

Research aims: 
To describe service provision for the transition from children’s to adult services for young people with life-limiting conditions in Northern Ireland, and to identify organisational factors that promote or inhibit effective transition. 
Study population: 
Health, social, educational and charitable organisations providing transition services to young people with life-limiting conditions in Northern Ireland. 
Study design and methods: 
A questionnaire has been developed by the research team drawing on examples from the literature and the advice of an expert advisory group. The questionnaire was piloted with clinicians,academics and researchers in June 2013. The questionnaire focuses on components of practice which may promote continuity in the transition from child to adult care for young people with a life-limiting condition. The survey will be distributed throughout Northern Ireland to an estimated 75 organisations, following the Dillman total design survey method. Numerical data will be analysed using PASW Statistical software to generate descriptive statistics along with a thematic analysis of data generated by open-ended questions. 
Results and interpretations: 
The survey will provide a description of services, transition policies, approaches to managing transition, categories of service users, the ages at which transition starts and completes, experiences with minority ethnic groups, the input of service users to the process, organisational factors promoting or hindering effective transition, links between services, and service providers’ recommendations for improvements in services.The outcomes will be an overview of the transition services currently provided in Northern Ireland identifying models of good practice and the key factors influencing the quality, safety and continuity of care. Survey results are due early in 2014.

Annual statistical report fiscal year 2013

The South Carolina Department of Probation, Parole and Pardon Services annually publishes a statistical report providing a description of the offender population and the agency's programs.