Introduction: An injury surveillance system was piloted in 2011 to monitor injuries in Canadian Forces. This evaluation of the key system attributes examined system performance. Methods: A retrospective chart review, a coding reliability study, a completeness of forms study and a key informant interview. Results: Sensitivity was 0.36 (95% CI: 0.28, 0.46). The system was missing patients over age 35. Kappa coefficients over 0.80 demonstrated good agreement. Completeness of forms study demonstrated high percentages of response for most questions and lower rates for questions related to using protective equipment, and consent for information sharing. Interviews proved acceptability to stakeholders, usefulness for identifying clusters and trends, simple and complete data collection, and flexibility. Conclusion: The injury surveillance system had good potential for several reasons: data collection did not require additional work in clinics; the system was well accepted and partially proved usefulness and timeliness in identifying unusual injury events.

Introduction: An injury surveillance system was piloted in 2011 to monitor injuries in Canadian Forces. This evaluation of the key system attributes examined system performance. Methods: A retrospective chart review, a coding reliability study, a completeness of forms study and a key informant interview. Results: Sensitivity was 0.36 (95% CI: 0.28, 0.46). The system was missing patients over age 35. Kappa coefficients over 0.80 demonstrated good agreement. Completeness of forms study demonstrated high percentages of response for most questions and lower rates for questions related to using protective equipment, and consent for information sharing. Interviews proved acceptability to stakeholders, usefulness for identifying clusters and trends, simple and complete data collection, and flexibility. Conclusion: The injury surveillance system had good potential for several reasons: data collection did not require additional work in clinics; the system was well accepted and partially proved usefulness and timeliness in identifying unusual injury events.

No description available.

Doctor of Philosophy / All surveillance systems are based on an effective general surveillance system because this is the system that detects emerging diseases and the re-introduction of disease to a previously disease free area. General surveillance requires comprehensive coverage of the population through an extensive network of relationships between animal producers and observers and surveillance system officers. This system is under increasing threat in Australia (and many other countries) due to the increased biomass, animal movements, rate of disease emergence, and the decline in resource allocation for surveillance activities. The Australian surveillance system is state-based and has a complex management structure that includes State and Commonwealth government representatives, industry stakeholders (such as producer bodies) and private organisations. A developing problem is the decline in the effectiveness of the general surveillance system in the extensive (remote) cattle producing regions of northern Australia. The complex organisational structure of surveillance in Australia contributes to this, and is complicated by the incomplete capture of data (as demonstrated by slow uptake of electronic individual animal identification systems), poorly developed and integrated national animal health information systems, and declining funding streams for field and laboratory personnel and infrastructure. Of major concern is the reduction in contact between animal observers and surveillance personnel arising from the decline in resource allocation for surveillance. Fewer veterinarians are working in remote areas, fewer producers use veterinarians, and, as a result, fewer sick animals are being investigated by the general surveillance system. A syndrome is a collection of signs that occur in a sick individual. Syndromic surveillance is an emerging approach to monitoring populations for change in disease levels and is based on statistical monitoring of the distribution of signs, syndromes and associations between health variables in a population. Often, diseases will have syndromes that are characteristic and the monitoring of these syndromes may provide for early detection of outbreaks. Because the process uses general signs, this method may support the existing (struggling) general surveillance system for the extensive cattle producing regions of northern Australia. Syndromic surveillance systems offer many potential advantages. First, the signs that are monitored can be general and include any health-related variable. This generality provides potential as a detector of emerging diseases. Second, many of the data types used occur early in a disease process and therefore efficient syndromic surveillance systems can detect disease events in a timely manner. There are many hurdles to the successful deployment of a syndromic surveillance system and most relate to data. An effective system will ideally obtain data from multiple sources, all data will conform to a standard (therefore each data source can be validly combined), data coverage will be extensive (across the population) and data capture will be in real time (allowing early detection). This picture is one of a functional electronic data world and unfortunately this is not the norm for either human or animal heath. Less than optimal data, lack of data standards, incomplete coverage of the population and delayed data transmission result in a loss of sensitivity, specificity and timeliness of detection. In human syndromic surveillance, most focus has been placed on earlier detection of mass bioterrorism events and this has concentrated research on the problems of electronic data. Given the current state of animal health data, the development of efficient detection algorithms represents the least of the hurdles. However, the world is moving towards increased automation and therefore the problems with current data can be expected to be resolved in the next decade. Despite the lack of large scale deployment of these systems, the question is becoming when, not whether these system will contribute. The observations of a stock worker are always the start of the surveillance pathway in animal health. Traditionally this required the worker to contact a veterinarian who would investigate unusual cases with the pathway ending in laboratory samples and specific diagnostic tests. The process is inefficient as only a fraction of cases observed by stock workers end in diagnostic samples. These observations themselves are most likely to be amenable to capture and monitoring using syndromic surveillance techniques. A pilot study of stock workers in the extensive cattle producing Lower Gulf region of Queensland demonstrated that experienced non-veterinary observers of cattle can describe the signs that they see in sick cattle in an effective manner. Lay observers do not posses a veterinary vocabulary, but the provision of a system to facilitate effective description of signs resulted in effective and standardised description of disease. However, most producers did not see personal benefit from providing this information and worried that they might be exposing themselves to regulatory impost if they described suspicious signs. Therefore the pilot study encouraged the development of a syndromic surveillance system that provides a vocabulary (a template) for lay observers to describe disease and a reason for them to contribute their data. The most important disease related drivers for producers relate to what impact the disease may have in their herd. For this reason, the Bovine Syndromic Surveillance System (BOSSS) was developed incorporating the Bayesian cattle disease diagnostic program BOVID. This allowed the observer to receive immediate information from interpretation of their observation providing a differential list of diseases, a list of questions that may help further differentiate cause, access to information and other expertise, and opportunity to benchmark disease performance. BOSSS was developed as a web-based reporting system and used a novel graphical user interface that interlinked with an interrogation module to enable lay observers to accurately and fully describe disease. BOSSS used a hierarchical reporting system that linked individual users with other users along natural reporting pathways and this encouraged the seamless and rapid transmission of information between users while respecting confidentiality. The system was made available for testing at the state level in early 2006, and recruitment of producers is proceeding. There is a dearth of performance data from operational syndromic surveillance systems. This is due, in part, to the short period that these systems have been operational and the lack of major human health outbreaks in areas with operational systems. The likely performance of a syndromic surveillance system is difficult to theorise. Outbreaks vary in size and distribution, and quality of outbreak data capture is not constant. The combined effect of a lack of track record and the many permutations of outbreak and data characteristics make computer simulation the most suitable method to evaluate likely performance. A stochastic simulation model of disease spread and disease reporting by lay observers throughout a grid of farms was modelled. The reporting characteristics of lay observers were extrapolated from the pilot study and theoretical disease was modelled (as a representation of newly emergent disease). All diseases were described by their baseline prevalence and by conditional sign probabilities (obtained from BOVID and from a survey of veterinarians in Queensland). The theoretical disease conditional sign probabilities were defined by the user. Their spread through the grid of farms followed Susceptible-Infected-Removed (SIR) principles (in herd) and by mass action between herds. Reporting of disease events and signs in events was modelled as a probabilistic event using sampling from distributions. A non-descript disease characterised by gastrointestinal signs and a visually spectacular disease characterised by neurological signs were modelled, each over three outbreak scenarios (least, moderately and most contagious). Reports were examined using two algorithms. These were the cumulative sum (CuSum) technique of adding excess of cases (above a maximum limit) for individual signs and the generic detector What’s Strange About Recent Events (WSARE) that identifies change to variable counts or variable combination counts between time periods. Both algorithms detected disease for all disease and outbreak characteristics combinations. WSARE was the most efficient algorithm, detecting disease on average earlier than CuSum. Both algorithms had high sensitivity and excellent specificity. The timeliness of detection was satisfactory for the insidious gastrointestinal disease (approximately 24 months after introduction), but not sufficient for the visually spectacular neurological disease (approximately 20 months) as the traditional surveillance system can be expected to detect visually spectacular diseases in reasonable time. Detection efficiency was not influenced greatly by the proportion of producers that report or by the proportion of cases or the number of signs per case that are reported. The modelling process demonstrated that a syndromic surveillance system in this remote region is likely to be a useful addition to the existing system. Improvements that are planned include development of a hand-held computer version and enhanced disease and syndrome mapping capability. The increased use of electronic recording systems, including livestock identification, will facilitate the deployment of BOSSS. Long term sustainability will require that producers receive sufficient reward from BOSSS to continue to provide reports over time. This question can only be answered by field deployment and this work is currently proceeding.

Doctor of Philosophy / All surveillance systems are based on an effective general surveillance system because this is the system that detects emerging diseases and the re-introduction of disease to a previously disease free area. General surveillance requires comprehensive coverage of the population through an extensive network of relationships between animal producers and observers and surveillance system officers. This system is under increasing threat in Australia (and many other countries) due to the increased biomass, animal movements, rate of disease emergence, and the decline in resource allocation for surveillance activities. The Australian surveillance system is state-based and has a complex management structure that includes State and Commonwealth government representatives, industry stakeholders (such as producer bodies) and private organisations. A developing problem is the decline in the effectiveness of the general surveillance system in the extensive (remote) cattle producing regions of northern Australia. The complex organisational structure of surveillance in Australia contributes to this, and is complicated by the incomplete capture of data (as demonstrated by slow uptake of electronic individual animal identification systems), poorly developed and integrated national animal health information systems, and declining funding streams for field and laboratory personnel and infrastructure. Of major concern is the reduction in contact between animal observers and surveillance personnel arising from the decline in resource allocation for surveillance. Fewer veterinarians are working in remote areas, fewer producers use veterinarians, and, as a result, fewer sick animals are being investigated by the general surveillance system. A syndrome is a collection of signs that occur in a sick individual. Syndromic surveillance is an emerging approach to monitoring populations for change in disease levels and is based on statistical monitoring of the distribution of signs, syndromes and associations between health variables in a population. Often, diseases will have syndromes that are characteristic and the monitoring of these syndromes may provide for early detection of outbreaks. Because the process uses general signs, this method may support the existing (struggling) general surveillance system for the extensive cattle producing regions of northern Australia. Syndromic surveillance systems offer many potential advantages. First, the signs that are monitored can be general and include any health-related variable. This generality provides potential as a detector of emerging diseases. Second, many of the data types used occur early in a disease process and therefore efficient syndromic surveillance systems can detect disease events in a timely manner. There are many hurdles to the successful deployment of a syndromic surveillance system and most relate to data. An effective system will ideally obtain data from multiple sources, all data will conform to a standard (therefore each data source can be validly combined), data coverage will be extensive (across the population) and data capture will be in real time (allowing early detection). This picture is one of a functional electronic data world and unfortunately this is not the norm for either human or animal heath. Less than optimal data, lack of data standards, incomplete coverage of the population and delayed data transmission result in a loss of sensitivity, specificity and timeliness of detection. In human syndromic surveillance, most focus has been placed on earlier detection of mass bioterrorism events and this has concentrated research on the problems of electronic data. Given the current state of animal health data, the development of efficient detection algorithms represents the least of the hurdles. However, the world is moving towards increased automation and therefore the problems with current data can be expected to be resolved in the next decade. Despite the lack of large scale deployment of these systems, the question is becoming when, not whether these system will contribute. The observations of a stock worker are always the start of the surveillance pathway in animal health. Traditionally this required the worker to contact a veterinarian who would investigate unusual cases with the pathway ending in laboratory samples and specific diagnostic tests. The process is inefficient as only a fraction of cases observed by stock workers end in diagnostic samples. These observations themselves are most likely to be amenable to capture and monitoring using syndromic surveillance techniques. A pilot study of stock workers in the extensive cattle producing Lower Gulf region of Queensland demonstrated that experienced non-veterinary observers of cattle can describe the signs that they see in sick cattle in an effective manner. Lay observers do not posses a veterinary vocabulary, but the provision of a system to facilitate effective description of signs resulted in effective and standardised description of disease. However, most producers did not see personal benefit from providing this information and worried that they might be exposing themselves to regulatory impost if they described suspicious signs. Therefore the pilot study encouraged the development of a syndromic surveillance system that provides a vocabulary (a template) for lay observers to describe disease and a reason for them to contribute their data. The most important disease related drivers for producers relate to what impact the disease may have in their herd. For this reason, the Bovine Syndromic Surveillance System (BOSSS) was developed incorporating the Bayesian cattle disease diagnostic program BOVID. This allowed the observer to receive immediate information from interpretation of their observation providing a differential list of diseases, a list of questions that may help further differentiate cause, access to information and other expertise, and opportunity to benchmark disease performance. BOSSS was developed as a web-based reporting system and used a novel graphical user interface that interlinked with an interrogation module to enable lay observers to accurately and fully describe disease. BOSSS used a hierarchical reporting system that linked individual users with other users along natural reporting pathways and this encouraged the seamless and rapid transmission of information between users while respecting confidentiality. The system was made available for testing at the state level in early 2006, and recruitment of producers is proceeding. There is a dearth of performance data from operational syndromic surveillance systems. This is due, in part, to the short period that these systems have been operational and the lack of major human health outbreaks in areas with operational systems. The likely performance of a syndromic surveillance system is difficult to theorise. Outbreaks vary in size and distribution, and quality of outbreak data capture is not constant. The combined effect of a lack of track record and the many permutations of outbreak and data characteristics make computer simulation the most suitable method to evaluate likely performance. A stochastic simulation model of disease spread and disease reporting by lay observers throughout a grid of farms was modelled. The reporting characteristics of lay observers were extrapolated from the pilot study and theoretical disease was modelled (as a representation of newly emergent disease). All diseases were described by their baseline prevalence and by conditional sign probabilities (obtained from BOVID and from a survey of veterinarians in Queensland). The theoretical disease conditional sign probabilities were defined by the user. Their spread through the grid of farms followed Susceptible-Infected-Removed (SIR) principles (in herd) and by mass action between herds. Reporting of disease events and signs in events was modelled as a probabilistic event using sampling from distributions. A non-descript disease characterised by gastrointestinal signs and a visually spectacular disease characterised by neurological signs were modelled, each over three outbreak scenarios (least, moderately and most contagious). Reports were examined using two algorithms. These were the cumulative sum (CuSum) technique of adding excess of cases (above a maximum limit) for individual signs and the generic detector What’s Strange About Recent Events (WSARE) that identifies change to variable counts or variable combination counts between time periods. Both algorithms detected disease for all disease and outbreak characteristics combinations. WSARE was the most efficient algorithm, detecting disease on average earlier than CuSum. Both algorithms had high sensitivity and excellent specificity. The timeliness of detection was satisfactory for the insidious gastrointestinal disease (approximately 24 months after introduction), but not sufficient for the visually spectacular neurological disease (approximately 20 months) as the traditional surveillance system can be expected to detect visually spectacular diseases in reasonable time. Detection efficiency was not influenced greatly by the proportion of producers that report or by the proportion of cases or the number of signs per case that are reported. The modelling process demonstrated that a syndromic surveillance system in this remote region is likely to be a useful addition to the existing system. Improvements that are planned include development of a hand-held computer version and enhanced disease and syndrome mapping capability. The increased use of electronic recording systems, including livestock identification, will facilitate the deployment of BOSSS. Long term sustainability will require that producers receive sufficient reward from BOSSS to continue to provide reports over time. This question can only be answered by field deployment and this work is currently proceeding.

La presente investigación tiene como objetivo implementar una propuesta de mejora para la gestión administrativa para la optimización del sistema de vigilancia en la Oficina de Seguridad en una Entidad Pública en Lima Metropolitana 2017. The objective of this research is to implement an improvement proposal for administrative management for the optimization of the surveillance system in the Security Office in a Public Entity in Metropolitan Lima 2017.

The purpose of this study was to compare the health profiles of women seeking residential treatment for substance abuse with women in the community. These 2 data sets consisted of a total of 621 participants. An additional aim of the present study was to examine whether these health profiles differ between the monosubstance abusing and polysubstance abusing women within the treatment group. There were a total of 257 participants in this group. All analyses controlled for the effects of age, insurance, marital status, employment and race/ethnicity. Binary logistic regressions were used to compare between and within the specified groups on the following variables: asthma, dyslipidemia, diabetes, Hepatitis B vaccination, HIV testing, hypertension, Pap smear testing, mental health problems, overweight/obesity and smoking. A follow-up analyses examined whether differences in the variables could be explained by the effects of specific control variables. Results suggested that differences in four outcomes might be explained by a single or smaller number of specific control variables. The overall results revealed that age was one of the strongest predictors of differences between the treatment and community group. When we controlled for age, marital status, low socioeconomic status (insurance, employment) and ethnicity we found that only two variables were significantly different. Women in residential showed significantly more smoking and mental health symptoms than were found in the community sample. There were no significant differences in the health profiles of polysubstance substance abusing than were found in monosubstance abusing women. The findings of the present study indicate that women seeking treatment are individuals with similar health disorders and health maintaining behaviors as the general population of women. However, women seeking treatment have significant increases in mental health disorders and smoking. Older age was related to increases in the odds of having dyslipidemia, diabetes, hypertension, and decreases in the odds of being immunized for Hepatitis B, tested for HIV, and having a Pap test in the last year. Due to anticipated-age related disorders, screening for dyslipidemia, diabetes, and hypertension should be provided for older women seeking admission to treatment. Substance abuse treatment centers for women should provide for mental health services and offer smoking cessation.

Yes / Avoidable hospital mortality is often attributable to inadequate patient vital signs monitoring, and failure to recognise or respond to clinical deterioration. The processes involved with vital sign collection and charting; their integration, interpretation and analysis; and the delivery of decision support regarding subsequent clinical care are subject to potential error and/or failure. Objective To determine whether introducing an electronic physiological surveillance system (EPSS), specifically designed to improve the collection and clinical use of vital signs data, reduced hospital mortality. Methods A pragmatic, retrospective, observational study of seasonally adjusted in-hospital mortality rates in three main hospital specialties was undertaken before, during and after the sequential deployment and ongoing use of a hospital-wide EPSS in two large unconnected acute general hospitals in England. The EPSS, which uses wireless handheld computing devices, replaced a paper-based vital sign charting and clinical escalation system. Results During EPSS implementation, crude mortality fell from a baseline of 7.75% (2168/27 959) to 6.42% (1904/29 676) in one hospital (estimated 397 fewer deaths), and from 7.57% (1648/21 771) to 6.15% (1614/26 241) at the second (estimated 372 fewer deaths). At both hospitals, multiyear statistical process control analyses revealed abrupt and sustained mortality reductions, coincident with the deployment and increasing use of the system. The cumulative total of excess deaths reduced in all specialties with increasing use of the system across the hospital. Conclusions The use of technology specifically designed to improve the accuracy, reliability and availability of patients’ vital signs and early warning scores, and thereby the recognition of and response to patient deterioration, is associated with reduced mortality in this study.

<p dir="ltr">The use of autonomous vehicles, such as Unmanned Aerial Systems (UASs), Unmanned Ground Vehicles (UGVs), and Unmanned Surface Vessels (USVs), has globally increased in various applications. Their rising popularity and high accessibility have also increased the use of UASs in criminal or hazardous actions.</p><p dir="ltr">It is beneficial to rapidly compute possible surveillance system evasive paths to evaluate the effectiveness of a given sensor deployment scheme. To find these evasive trajectories, we assume full knowledge of the current and future state of the surveillance system. This assumption allows the defender to identify worst-case trajectories to counteract. The surveillance system path planning presented in this work can be leveraged for game theoretic sensor deployment.</p><p dir="ltr">A sensor deployment scheme determines the overall surveillance efficiency. Through redeployment after each assessment, it aims to approach an equilibrium that maximizes defense capabilities. Therefore, a method of evaluation that models mobile, directional sensors is demanded.</p><p dir="ltr">In response to this demand, this thesis explores the design of a computationally efficient path-planning algorithm for the space-time domain. The Space-Time Parallel RRT* (STP-RRT*) algorithm obtains multiple goal candidates, drawn from a uniform distribution over the time horizon. A set of parallel RRT* trees is simultaneously populated by each goal candidate. By leveraging a connect heuristic from RRT-Connect, parallel goal trees converge to an RRT* tree populated from a start point. This simultaneous tree growth structure returns a computation complexity of O(N log(N)), where N is the number of random samples.</p><p dir="ltr">Due to its low complexity, the STP-RRT* algorithm is suitable to be used as an evaluation metric that computes the cost of the infiltration path of a malicious autonomous system to assess the performance of the deployment layout. The feedback assessment can be used for the surveillance system redeployment to strengthen the vulnerability.</p><p dir="ltr">To identify potential and existing bottlenecks in the algorithm, a computation complexity analysis is conducted, and complexity reduction techniques are employed. Given that surveillance system characteristics are known, 1-dimensional and 2-dimensional environments are generated where positions and surveillance patterns of stationary and dynamic obstacles are randomly selected. In each randomized environment, the STP-RRT*, RRT*, and ST-RRT* are evaluated by comparing success rate, computation time, tree size, and normalized cost through 100-trial Monte Carlo simulations. Under the provided conditions, the proposed STP-RRT* algorithm outperforms two other algorithms with an improved mean success rate and reduced mean computation time by 10.02% and 12.88%, respectively, while maintaining a similar cost level, showing its potential application in surveillance-evasive path-planning problems for surveillance deployment evaluation. Finally, we integrate our algorithm with Nav2, an open-source navigation stack for various robotics applications, including UAV, UGV, and USV. We demonstrate its effectiveness via software-in-the-loop (SiTL) experiments utilizing open-source autopilot software.</p>

ABSTRACT: Temporary migration, especially men moving to their place of work, was an intrinsic feature of the former Apartheid system in South Africa. Since the demise of Apartheid an increasing proportion of women have also been migrating to their place of work, and oscillating between work place and home. Temporary migration can be defined as oscillating migration between a home base and at least one other place, usually for work, but also for other reasons like education. This study demonstrates that in the Agincourt study population, in the rural northeast of South Africa, adult female temporary migration is an increasing trend. By conducting a survival analysis, the study evaluates the mortality outcomes, specifically infant and child mortality rates, of children born to female temporary migrants compared with children of non-migrant women. Based on the findings presented we accept the null hypothesis that there is presently no discernable impact (positive or negative) of maternal temporary migration on infant and child mortality. There seems to be a slight protective factor associated with mother’s migration when tested at a univariate level. However, through multivariate analysis, it is shown that this advantage relates to the higher education status of migrating mothers. When women become tertiary educated there is a survival advantage to their children and these women are also more likely to migrate. The study highlights greater child mortality risks associated with settled Mozambicans (former refugees) and unmarried mothers. Both of these risk factors reflect the impact of high levels of social deprivation.