In 1999, the Institute of Medicine estimated as many as 98,000 patients died each year in US hospitals as the result of medical errors. Five years later, another report estimated 195,000 people died unnecessarily. A recent study of patient safety in American hospitals concluded that 87% of Medicare deaths identified over a three-year period were "potentially preventable." The rapid response team (RRT) has been recommended as an effective strategy for reducing avoidable patient deaths as measured by patient safety indicator #4 (PSI#4), Death among surgical inpatients with treatable serious complications [formerly failure to rescue]. There is no research evidence to support the recommendation. The purpose of this exploratory research study was to describe RRT characteristics, determine RRT penetration, and measure PSI#4 (Death among surgical inpatients) rates among hospitals in a large metropolitan area hospital council. A retrospective, descriptive design was used during analysis of survey data collected from members of the hospital council and secondary analysis of administrative data submitted by the same hospitals to a regional data warehouse. All of the hospitals represented by survey submissions had implemented RRTs. The majority of teams was nurse-led and could be activated by a wide range of hospital staff and family members. The hospitals used evidence-based criteria for RRT activation. There was a downward trend in the regional PSI#4 rate from 2003 to 2008, which was not statistically significant, but may be considered clinically significant. Nurse administrators viewed RRTs as effectively supporting nursing care. This study provided a first look at RRTs in relation to an untested patient safety indicator that measured avoidable patient deaths. More research with a larger sample size with adequate power to support statistical analysis of differences in PSI #4 rates over time will provide evidence regarding relationships among hospital characteristics, RRT characteristics, and avoidable deaths among surgical inpatients. / text

近年來電腦等高科技的快速成長，進而促進資訊化的過程。資料庫的蓬勃發展，使得資料大量累積，長久之下，卻造成資料過多，資訊不足的嚴重問題。因此資料庫內的知識探索議題也隨之興起，而資料採礦（Data Mining）的過程，更是其中重要的一環。 相同的，預防醫學資訊的發展，流行病學資料庫中亦累積了大量關於死亡統計資料，而這些資料中，隱藏可能存在的知識，能加強我們對疾病進展的瞭解。若將資料採礦的概念應用於流行病學領域，相信必能相輔相成。 本研究的重點在於結合統計軟體 STATISTICA，以Visual Basic 6.0語言開發一個簡易的資料採礦系統之使用者介面，並將資料採礦技術應用於死亡統計資料中。系統中的挖掘方法主要採用敘述統計、交叉分析與多變量分析中的群集分析與區別分析，根據行政院衛生署統計室所提供之民國八十三年至八十八年台灣地區人口死亡原因資料，來發現隱藏在資料中的趨勢與模式。 / For the past decade the development in computer technology has advanced so rapidly that it brings forth the enormous supply of data information. As time passes by the data information has been increasingly accumulated yet little can be inferred from the data thus resulting a loss of information which might be of significant. Bearing with the existence of such issue, this research presents the process of data mining as one of the solution. Similarly, the data base in the field of medical science may have contained a large amount of information. If one can appropriately apply the application of data mining into this huge database then we may be able to extract some valuable findings. The focus of this research is to develop a user friendly operating system using Visual Basic 6.0 and integrates the statistical software-STATISTICA into the operating system. The research applies the application of data mining on the death data provided by Statistics Office, Department of Health from 1994 to 1999. The methods used in this application are descriptive statistics, cross tabulation, cluster analysis, and discriminant analysis of multivariate analysis in an attempt to find out if there is any pattern in the cause of death.

This thesis examines how to find the best fit to a series of data points when curve fitting using power-exponential models. We describe the different numerical methods such as the Gauss-Newton and Levenberg-Marquardt methods to compare them for solving non-linear least squares of curve fitting using different power-exponential functions. In addition, we show the results of numerical experiments that illustrate the effectiveness of this approach.Furthermore, we show its application to the practical problems by using different sets of data such as death rates and rocket-triggered lightning return strokes based on the transmission line model.

Chaque année, au moins 300 millions de personnes sont touchées par le paludisme et plus d’1 million d’entre elles en décèdent. Le neuropaludisme reste une complication mortelle, notamment chez les jeunes enfants d’Afrique. En dehors de la résistance aux antipaludiques, un défi de la lutte contre le paludisme est de définir la place des traitements protecteurs de l’hôte associés aux antipaludiques. En effet, le taux de mortalité résiduelle au cours du neuropaludisme est d’environ 20%, malgré un traitement adapté. Ce travail décrit les modèles expérimentaux de neuropaludisme, la place des neuroprotecteurs dans cette pathologie, puis, l’efficacité de deux neuroprotecteurs, l’érythropoïétine recombinante et les statines, au cours d’un neuropaludisme expérimental. Enfin, la pertinence de la neuroprotection sera documentée par une étude de preuve de concept chez l’Homme en zone d’endémie. / There are at least 300 million cases of malaria each year, resulting in more than a million deaths. Cerebral malaria is the most severe complication of malaria especially in young African children. Besides drug resistance, another challenge in the fight against malaria is the protective treatment of the host combined to the conventional antimalarial treatment. This opportunity came with the consideration of cerebral malaria residual case fatality rate of about 20%, despite a timely adequate antimalarial treatment. This work describes the available experimental model of cerebral malaria, the place of neuroprotective therapies in this disease and the efficacy of two neuroprotective drugs, recombinant erythropoietin and statins, during experimental cerebral malaria. Last, a proof-of-concept study documented the relevance of neuroprotection during human cerebral malaria in endemic area

In this work, we focus on enhancing the efficacy of predictive modeling in operational management decision making in two different settings: Essay 1 focuses on demand forecasting for the companies and the second study utilizes longitudinal data to analyze the illicit drug seizure and overdose deaths in the United States. In Essay 1, we utilize an operational system (newsvendor model) to evaluate the forecast method outcome and provide guidelines for forecast method (the exponential smoothing model) performance assessment and judgmental adjustments. To assess the forecast outcome, we consider not only the common forecast error minimization approach but also the profit maximization at the end of the forecast horizon. Including profit in our assessment enables us to determine if error minimization always results in maximum profit. We also look at the different levels of profit margin to analyze their impact on the forecasting method performance. Our study also investigates how different demand patterns influence maximizing the forecasting method performance. Our study shows that the exponential smoothing model family has a better performance in high-profit products, and the rate of decrease in performance versus demand uncertainty is higher in a stationary demand environment.In the second essay, we focus on illicit drug overdose death rate. Illicit drug overdose deaths are the leading cause of injury death in the United States. In 2017, overdose death reached the highest ever recorded level (70,237), and statistics show that it is a growing problem. The age adjusted rate of drug overdose deaths in 2017 (21.7 per 100,000) is 9.6% higher than the rate in 2016 (19.8 per 100,000) (U. S. Drug Enforcement Administration, 2018, p. V). Also, Marijuana consumption among youth has increased since 2009. The magnitude of the illegal drug trade and its resulting problems have led the government to produce large and comprehensive datasets on a variety of phenomena relating to illicit drugs. In this study, we utilize these datasets to examine how marijuana usage among youth influence excessive drug usage. We measure excessive drug usage in terms of drug overdose death rate per state. Our study shows that illegal marijuana consumption increases excessive drug use. Also, we analyze the pattern of most frequently seized illicit drugs and compare it with drugs that are most frequently involved in a drug overdose death. We further our analysis to study seizure patterns across layers of heroin and cocaine supply chain across states. This analysis reveals that most active layers of the heroin supply chain in the American market are retailers and wholesalers, while multi-kilo traffickers are the most active players in the cocaine supply chain. In summary, the studies in this dissertation explore the use of analytical, descriptive, and predictive models to detect patterns to improve efficacy and initiate better operational management decision making.

Thesis (M. A. (Nursing)) -- University of Limpopo, 2020 / Background: Long patient waiting time for services is demonstrated by daily long queues of patients in Primary Health Care (PHC) and hospitals Outpatients departments. Aim: The aim of the study was to determine the factors contributing to long waiting time at Blouberg Health Centre (BHC), Capricorn District, Limpopo Province. Methods: A quantitative, descriptive and cross-sectional research design was used to describe factors contributing to long waiting time. The study population consisted 31356 patients in the financial year 2017/2018. Simple random probability sampling was used to select 395 respondents. Data were collected using self-developed questionnaire. All questionnaires were completed and returned. The 395 questionnaires were then analyzed using the Statistical Package for Social Sciences (SPSS, version 25). Descriptive statistics were used to analyze and describe and summarized data. Findings: The findings were presented in the form of distribution graphs and tables. Inferential statistics were used based on probability and allowed judgement to be made about variables. The study revealed factors considered most important were lack of commitment; full time study leaves at the same year; workshops; sick leaves; increased population; sitting in tearoom for hours; many foreign national without passports; staff shortages; laissez faire working style and transfers or escorting patients, while the nearby Hellen Franz Hospital (HFH) also transfer to the same hospitals, leading to mismanagement of budget reduced manpower and increased death rate. Recommendations: The study recommends that all Primary Health Care (PHC) settings should use numbers for patients when entering facilities to monitor the queuing and prevent dissatisfaction that can result from long waiting. Conclusion: It is of paramount importance to provide clearer, transparent information to the recipients of the Primary health care services that they might receive. The provincial coordinators are accountable to the waiting time management with the assistance of the PHC specialist nurse and Nursing Management.

The Swedish death rate related to narcotics is amongst the highest within the EU. The purpose of this study is to use process tracing while investigating and understanding the high death rate among people who use narcotics in Sweden. The focus is on whether the Swedish drug policy is driven by morals instead of scientific proof and asks whether morals are the main cause of the high death rate in Sweden. The study utilizes the “threshold theory” and the theory of “symbolic politics.” To do this the following questions are being answered: “How has the Swedish zero tolerance towards narcotics looked like since the 1970s until present day?” and “are there any signs to proof that the Swedish drug policy is driven more by morals than by scientific evidence, and if so, what are they?” The study also tests the hypothesis that the high death rate is related to the restrictive drug policy in Sweden. In order to answer this the study collects data and regulations from different time periods in Sweden thru a qualitative method. The study confirms that the Swedish drug policy is driven by moral while overlooking scientific proof and it is also likely that the hypothesis of the study is true.

The purpose of this study was to determine and compare the outcomes of tuberculosis (TB) treatment among Human Immunodeficiency Virus (HIV) co-infected TB patients, and identify factors associated with these outcomes. A quantitative cross-sectional analytic design was used. Patient level secondary data was collected and analysed for the study. A total of 575 TB patients, including 360 non-HIV infected, 169 HIV co-infected and 46 without a documented HIV status, were enrolled. The overall treatment success rate was 91.5%, and HIV co-infected TB patients had a high rate (11.8%) of unfavourable outcomes. The cure rate was significantly lower (10.1% versus 24.2%) and the death rate higher in HIV co-infected patients (8.3% versus 2.5%). Age and TB classification were significantly associated with treatment outcome. No association was found with starting ART, Cotrimoxazole prophylactic treatment or enrolment in HIV care, but 22% of HIV co-infected TB patients were taking ART when they developed TB disease / Health Studies / M.A. (Public Health)

Mortality information is very important for national planning and health of a country. Mortality rate forecasting is a basic contribution for the projection of financial improvement of pension plans, well-being and social strategy planning. In the first part of the thesis, we fit the selected mortality rate models, namely the Power-exponential function based model, the ModifiedPerks model and the Heligman and Pollard (HP4) model to the data obtained from the HumanMortality Database [22] for the male population ages 1–70 of the USA, Japan and Australia. We observe that the Heligman and Pollard (HP4) model performs well and better fit the data as compared to the Power-exponential function based model and the Modified Perks model. The second part is to systematically compare the quality of the mortality rate forecasting using the second order Lee–Carter method with the selected mortality rate models. The results indicate that Power-exponential function based model and the Heligman and Pollard (HP4) model gives a more reliable forecast depending on individual countries.

The purpose of this study was to determine and compare the outcomes of tuberculosis (TB) treatment among Human Immunodeficiency Virus (HIV) co-infected TB patients, and identify factors associated with these outcomes. A quantitative cross-sectional analytic design was used. Patient level secondary data was collected and analysed for the study. A total of 575 TB patients, including 360 non-HIV infected, 169 HIV co-infected and 46 without a documented HIV status, were enrolled. The overall treatment success rate was 91.5%, and HIV co-infected TB patients had a high rate (11.8%) of unfavourable outcomes. The cure rate was significantly lower (10.1% versus 24.2%) and the death rate higher in HIV co-infected patients (8.3% versus 2.5%). Age and TB classification were significantly associated with treatment outcome. No association was found with starting ART, Cotrimoxazole prophylactic treatment or enrolment in HIV care, but 22% of HIV co-infected TB patients were taking ART when they developed TB disease / Health Studies / M.A. (Public Health)