Treatment choices for fevers in children under-five years in a rural Ghanaian district

<p>Abstract</p> <p>Background</p> <p>Health care demand studies help to examine the behaviour of individuals and households during illnesses. Few of existing health care demand studies examine the choice of treatment services for childhood illnesses. Besides, in their analyses, many of the existing studies compare alternative treatment options to a single option, usually self-medication. This study aims at examining the factors that influence the choices that caregivers of children under-five years make regarding treatment of fevers due to malaria and pneumonia in a rural setting. The study also examines how the choice of alternative treatment options compare with each other.</p> <p>Methods</p> <p>The study uses data from a 2006 household socio-economic survey and health and demographic surveillance covering caregivers of 529 children under-five years of age in the Dangme West District and applies a multinomial probit technique to model the choice of treatment services for fevers in under-fives in rural Ghana. Four health care options are considered: self-medication, over-the-counter providers, public providers and private providers.</p> <p>Results</p> <p>The findings indicate that longer travel, waiting and treatment times encourage people to use self-medication and over-the-counter providers compared to public and private providers. Caregivers with health insurance coverage also use care from public providers compared to over-the-counter or private providers. Caregivers with higher incomes use public and private providers over self-medication while higher treatment charges and longer times at public facilities encourage caregivers to resort to private providers. Besides, caregivers of female under-fives use self-care while caregivers of male under-fives use public providers instead of self-care, implying gender disparity in the choice of treatment.</p> <p>Conclusions</p> <p>The results of this study imply that efforts at curbing under-five mortality due to malaria and pneumonia need to take into account care-seeking behaviour of caregivers of under-fives as well as implementation of strategies.</p

Seeking treatment for symptomatic malaria in Papua New Guinea

Background: Malaria places a significant burden on the limited resources of many low income countries. Knowing more about why and where people seek treatment will enable policy makers to better allocate the limited resources. This study aims to better understand what influences treatment-seeking behaviour for malaria in one such low-income country context, Papua New Guinea (PNG). Methods: Two culturally, linguistically and demographically different regions in PNG were selected as study sites. A cross sectional household survey was undertaken in both sites resulting in the collection of data on 928 individuals who reported suffering from malaria in the previous four weeks. A probit model was then used to identify the factors determining whether or not people sought treatment for presumptive malaria. Multinomial logit models also assisted in identifying the factors that determined where people sought treatments. Results: Results in this study build upon findings from other studies. For example, while distance in PNG has previously been seen as the primary factor in influencing whether any sort of treatment will be sought, in this study cultural influences and whether it was the first, second or even third treatment for a particular episode of malaria were also important. In addition, although formal health care facilities were the most popular treatment sources, it was also found that traditional healers were a common choice. In turn, the reasons why participants chose a particular type of treatment differed according to the whether they were seeking an initial or subsequent treatments. Conclusions: Simply bringing health services closer to where people live may not always result in a greater use of formal health care facilities. Policy makers in PNG need to consider within-country variation in treatment-seeking behaviour, the important role of traditional healers and also ensure that the community fully understands the potential implications of not seeking treatment for illnesses such as malaria at a formal health care facility.Carol P Davy, Elisa Sicuri, Maria Ome, Ellie Lawrence-Wood, Peter Siba, Gordon Warvi, Ivo Mueller and Lesong Conte

Improving access to health care for malaria in Africa: a review of literature on what attracts patients

BACKGROUND: Increasing access to health care services is considered central to improving the health of populations. Existing reviews to understand factors affecting access to health care have focused on attributes of patients and their communities that act as 'barriers' to access, such as education level, financial and cultural factors. This review addresses the need to learn about provider characteristics that encourage patients to attend their health services. METHODS: This literature review aims to describe research that has identified characteristics that clients are looking for in the providers they approach for their health care needs, specifically for malaria in Africa. Keywords of 'malaria' and 'treatment seek*' or 'health seek*' and 'Africa' were searched for in the following databases: Web of Science, IBSS and Medline. Reviews of each paper were undertaken by two members of the team. Factors attracting patients according to each paper were listed and the strength of evidence was assessed by evaluating the methods used and the richness of descriptions of findings. RESULTS: A total of 97 papers fulfilled the inclusion criteria and were included in the review. The review of these papers identified several characteristics that were reported to attract patients to providers of all types, including lower cost of services, close proximity to patients, positive manner of providers, medicines that patients believe will cure them, and timeliness of services. Additional categories of factors were noted to attract patients to either higher or lower-level providers. The strength of evidence reviewed varied, with limitations observed in the use of methods utilizing pre-defined questions and the uncritical use of concepts such as 'quality', 'costs' and 'access'. Although most papers (90%) were published since the year 2000, most categories of attributes had been described in earlier papers. CONCLUSION: This paper argues that improving access to services requires attention to factors that will attract patients, and recommends that public services are improved in the specific aspects identified in this review. It also argues that research into access should expand its lens to consider provider characteristics more broadly, especially using methods that enable open responses. Access must be reconceptualized beyond the notion of barriers to consider attributes of attraction if patients are to receive quality care quickly

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 &lt; 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

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

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

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