Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

An effective heuristic for the P-median problem with application to ambulance location

We consider the p-median problem which is to find the location of p-facilitiesso as to minimize the average weighted distance or time between demandpoints and service centers. Many heuristic algorithms have been proposed for thisproblem. In this paper we present a simple new heuristic which is effective formoderately size problem.The heuristic uses a reduction and an exchange procedure.Our methodology is tested on 400 randomly generated problems with 10 to 50customer locations as well as 6 well known literature test problems. We also compareour method with the Branch and Bound method in terms of quality and computationaltime using a larger problem size of 150 customer locations. For the random problemsthe generated solutions were on average within 0.61 % of the optimum. A similarresult was achieved for the literature test problems. A comparative analysis withliterature heuristics supports the superiority of our method. The computational time ofour heuristic is 0.75 % of the Branch and Bound Method. We also apply our heuristicto a case study involving the location of emergency vehicles (ambulances) in PerthCity (Australia)

The impact of mathematics and statistics support at the Academic Learning Centre, Central Queensland University

The Mathematics and Statistics Support Centre (MSSC) at Central Queensland University (CQU), which is part of the Academic Learning Centre (ALC), has been in operation since 1984 as the first MSSC in Australia. The Mathematics Learning Support (MLS) centres have spread to almost every university in Australia currently. The MSSC at CQU offers free support services for students who need mathematics and statistics help or advice for their courses. In this study we analyse the differences of the impact of mathematics and statistics support services on mature age students and secondary school-leaver (traditional) students study habits, confidence and opinion towards mathematics and statistics, using data collected from students who used the ALC at CQU in the second half of 2016. The results suggest a positive impact of mathematics and statistics support on mature age students’ study habits, confidence and opinion towards mathematics and statistics. Chi-squared tests have shown that there are differences in the proportion of mature students and traditional students who have changed their study habits, increased their confidence levels and changed their opinion on mathematics with p values of 0.001, 0.009 and 0.023, respectively. The study also shows that the majority of students are satisfied with the services provided at the MSSC and believe that the services provided helped them to solve or minimize difficulties in working through mathematics- and/or statistics-related learning activities at CQU. Based on the findings of the current study we conclude that the use of learning support centres is an effective way to solve or minimize difficulties associated with mathematics and statistics learning at a tertiary institution

Health and economic development: with particular reference to malaria control programmes in Africa

Growth spurts in countries where malaria was eradicated (countries within the sub-tropical areas in Europe, Asia and America) prompt many analysts to view future economic success in Africa as dependent on the improvement of the malaria situation in Africa. Gallup and Sachs (2001) observed that the economic growth in the five years following eradication of countries that have eradicated malaria (in the past half century) have almost always been substantially higher than growth in their region. Based on this analogy it has been a long-standing belief that malaria is hurting economic development in Africa

Health, emergency facilities and development

Health is a major factor in development and it is central to the theory about human capital and endogenous growth. This is because health affects all other economic and development activities. The World Health Organization’s (2003) call for “Health for all” which argues that “everybody needs and is entitled to the highest possible standard of health” is a coherent and indispensable vision for global health and development. The importance of health for development is also highlighted in the Millennium Development Goals (MDGs) where three of the eight MDGs goals focused on health. So far global actions to promote health for development have focused heavily on primary health care and it is right to do so given the importance of the burden of diseases in low andmiddle income countries (LMICs). However, there is a missing link. Despite their importance, emergency facilities and emergency services have become the poorer cousins of the global health and development effort. We analyze the relationship between emergency facilities, health care delivery and development and develop a simple heuristic or mathematical algorithm for effective location of facilities for regional or diversified health care systems. Smaller systems are considered here to increase understanding and ease of use by stakeholders as well as ownership of facilities for effectiveness of services delivery

Do we need optimization models to locate health service facilities?

The rapid growth of population in cities and major regional areas, shorter length of stays in hospitals, ageing (and the desire of the elderly to stay longer in their homes), and traffic poses a challenge to health departments in meeting the demand for preventive, emergency and health center services. The changes in factors such as urbanization, demography and the rate of service utilization may affect the optimal distances or cost between patients and healthcare facilities. However, there is limited information about the impact of such changes on the effectiveness of the existing facilities. The interest in facility locations spans a wide range of academic disciplines and industrial activity. Mathematicians, geographers, economists, urban planners, retailers, engineers, hospital administrators, and even politicians campaigning for an election all deal with facility location problems. The increasing interest in location theory is attributed to several factors such as: its widespread applicability at all levels of human activities with beneficial economic effects; the computational complexity of location models; and the variation of location models from problem to problem. The primary objectives of locating facilities can be summarized into three categories. The first category known as the Location Set Covering Problem (LSCP) and the Maximal Covering Location Problem (MCLP) are designed to cover demand within a specified time or distance. The LSCP seeks to locate the minimum number of facilities required to ‘cover’ all demand or population in an area. The MCLP is to locate a predetermined number of facilities to maximize the demand or population that is covered. The second category known as the p-center are designed to minimize maximum distance. The p-center addresses the difficulty of minimizing the maximum distance that a demand or population is from its closet facility given that p facilities are to be located. The third category known as the p-median problem are designed to minimize the average weighted distance or time. The p-median problem finds the location of p facilities to minimize the demand weighted average or total distance between demand or population and their closest facility. The objective of this study is to discuss the importance of the application of optimization models (covering, p-center and the p-median models) to locate emergency healthcare stations. We discuss the history of facility location models to the location of emergency facilities. We present the real application of location models to the location of public facilities such as ambulance and fire station in various parts of the world. We outline the models that are used with the methodology and present the outcomes of the application of the models. We finally apply three discrete location models to real data from Mackay region in Queensland, Australia. We compare existing emergency health care sites with the optimal solutions proposed by the location models. We also discuss the policy implication in terms of cost of using existing facilities as compare to the proposed sites by the location models

An efficient modified greedy algorithm for the p-median problem

The fundamental objectives of locating facilities can be summarized into three categories. The first category refers to those designed to cover demand within a specified time or distance. This objective gives rise to location problems which are known as the Location Set Covering Problem (LSCP) and the Maximal Covering Location Problem (MCLP). The LSCP seeks to locate the minimum number of facilities required to ‘cover’ all demand or population in an area. The MCLP is to locate a predetermined number of facilities to maximize the demand or population that is covered. The second category refers to those designed to minimize maximum distance. This results in a location problem known as the p-center problem which addresses the difficulty of minimizing the maximum distance that a demand or population is from its closet facility given that p facilities are to be located. The third category refers to those designed to minimize the average weighted distance or time. This objective leads to a location problem known as the p-median problem. The p-median problem finds the location of p facilities to minimize the demand weighted average or total distance between demand or population and their closest facility. The p-median problemis a typical combinatorial optimization problem with many practical applications such as location of warehouses, schools, health centers, shops etc. Greedy algorithms are the simplest algorithms to design however it is not easy to understand its capability and limitations. A greedy algorithm solves a global optimization problem by making a sequence of locally optimal decisions. That is a greedy algorithm always chooses the next step of an algorithm that is locally optimal. For example for Facility Location Problem we will consider the facilities for which decisionsr egarding locally optimal locations will be made. The decisions that are made regarding where to locate successive facilities by a greedy method are permanent. That is the greedy algorithms make permanent decisions about the construction of a solution, based on the restricted consideration such as choosing alocation that gives a minimum cost. Greedy algorithms for facility location problems are constructive in principle. They are designed to give solutions of fairly good quality without using much time that is needed to compute better quality solutions by other algorithms. The most natural and simple heuristic for the p-median problem is the greedy algorithm. For the p-median problem to locate facilities, this algorithm picks amost ‘cost-effective’ facility until every required number of facilities p is located. We propose a modified form of the myopic (greedy) algorithm for the p-median problem. The new algorithmis simple and it gives relatively quality solutions. We demonstrated the importance of the removal of extreme values from a distance matrix before locating the first facility. The modification of the algorithm involves the removal of the extreme or large values from each column of the distance matrix. We then determine the first facility (1-median) after the removal of the extreme values. We revert to the original distance matrix after the first facility (1-median) is located. We then determine the additional facilities using the original distance matrix. We compare the results obtained by the original Myopic algorithm with the modified version using the 400 random problems. The results demonstrate the efficiency and superiority of our new method

The p-median problem and health facilities: Cost saving and improvement in healthcare delivery through facility location

The importance of health to economic growth and development is an undisputed fact. Modern advancement in technology and healthcare has contributed to improved health and productivity, but there are many people who cannot access healthcare in a timely fashion. Factors affecting delays in accessing healthcare include inadequate supply, poor location, or lack of healthcare facilities all of which can be exacerbated by increasing healthcare costs and scarcity of resources. In this study, we develop a simple two-stage method based on the p-median problem to investigate the location and access to healthcare (emergency) facilities in urban areas. We compare the results of our new method with the results of similar existing methods using 26-node, 42-node, and 55-node data. We also show the efficiency of our method with exact methods using 150-node random data. Our method compares favorably with optimal and the existing methods

Effective method for locating facilities

There has been an increasing interest in the problem of effective facility location over the pastfive decades. The location of these important facilities arises in the service and manufacturing industries. The fundamental questions that arise concern the number and location of facilities such as: schools; hospitals; ambulances; warehouses; factories; department stores; police stations; waste material dumps; fire stations; needed to achieve a prescribed level of service and output. The main concern of many location problems is to place facilities to optimize some spatially dependent objectives such as: minimize average travel time or distance between demand points and servers; minimize a cost function of travel or response time. These optimization problems are complicated with the need to meet a number of specified constraints that relate to safety, demand, available resources, level of service and time. Indeed, the optimization problems that arise in practice are computationally difficult (NP hard) to solve by exact methods. An important problem is the p-median problem which is to find the location of p-facilities so as to minimize the average weighted distance or time between demand points and service centers. Many heuristic algorithms have been proposed for this problem due to the difficulty in obtaining solutions by the exact methods. We discussed below a reduction concept applied to p-median problem as follows. Consider a weighted p-median problem with a distance matrix given as D = (dij ). Note that each row (column) of D is associated with a demand (facility) location. We say that column k dominates column l if dik ≤ dil for all i ≠ k. We use the term strongly dominates in the case of strict inequalities. Observe that locating a facility at a dominated location l would provide no advantage to locating a facility at k except possibly in serving the demands of customers in location l. Further, strongly dominated columns would only be used for ‘self-serve’. Consequently, dominated column can be dropped to generate a feasible solution and the location can later be considered as a possible ‘self-service’ facility. We extend the concept of dominance somewhat further as follows. We say columns k and l dominate column j if dij ≥ min {dik , dil} for all i ≠ j . In this case there is no advantage in using location j (except for serving customers in location j) when locations k and l are used. So again we can drop the dominated column j if columns k and l are used. The term strongly is used as before. We further extend this concept of dominance as follows. We say that column k partially dominates column l if dik ≤ dil for at least half or more of the entries for which i ≠ k . Similarly, we say columns k and l partially dominate column j if dij ≥ min {dik , dil} for at least half or more of the entries for which i ≠ j . Partially dominated columns correspond to nodes which may be assigned ‘self-serve’ facilities in the original and the reduced matrix. In this paper, we developed a new greedy algorithm based on a concept known as dominance to obtain solutions for the p-median problem. This concept reduces the number of columns of a distance matrix by considering potential facilities that are near and those that are far from the population or demand. We illustrate our ideas and the algorithm with an example. We further applied the new algorithm to effectively locate additional ambulance stations in the Central and South East metropolitan areas of Perth to complement the existing ones. We also compare the performance of our new Greedy Reduction Algorithm (GRA) with the existing greedy algorithm of the p-median problem

The optimal location of ambulance station in a regional area: The case of Mackay

The provision of efficient and effective emergency service such as ambulance service is a task faced by most cities and major regional centres. The emergency medical service is very necessary and plays a vital role in reducing death or serious complication from life threatening health incident. Over the years, there have been several major initiatives to improve the access to and quality of emergency care in Queensland. In 2009-2010, public hospital Emergency Departments (ED) in Australia covered about 7.4 million emergency cases of which there were over 3 million ambulance incidents. Mackay is a regional city in Queensland, Australia’s east coast. The Mackay Metropolitan which comprises of 24 suburbs is prone to natural disasters such as cyclones and flood. The Mackay region depends highly on emergency services especially during disasters. The current ambulance locations in the Mackay Local Ambulance Service Network (LASN) are significantly underperforming with regard to not meeting the target response time according to Queensland 2014 ambulance report. Early response to emergency calls is important and crucial for human survival. The response time is a function of the distance between the emergency facility and emergency demand. It is therefore important to locate emergency facility such that the distance to be travelled by an ambulance in response to emergency call is minimized. The p-median problem finds the location of p facilities to minimize the demand weighted average or total distance between demand or population and their closest facility. The objective of this study is to discuss the importance of the application of the p-median model to locate emergency stations. We compare existing ambulance stations with the optimal solutions proposed by the p-median location models in the Mackay region. We determine the cost of assessing the facilities that are located using the p-median model and showed the cost saving of the model when optimal locations are compared with locations when facilities are added to the existing ambulance locations optimally