The working hours of junior doctors have been a focus of discussion in Australia since the mid-1990s. Several national organizations, including the Australian Medical Association (AMA), have been prominent in advancing this agenda and have collected data (most of which is self-reported) on the working hours of junior doctors over the last 15 years. Overall, the available data indicate that working hours have fallen in a step-wise fashion, and AMA data suggest that the proportion of doctors at high risk of fatigue may be declining. It is likely that these changes reflect significant growth in the number of medical graduates, more detailed specifications regarding working hours in industrial agreements, and a greater focus on achieving a healthy work–life balance. It is notable that reductions in junior doctors’ working hours have occurred despite the absence of a national regulatory framework for working hours. Informed by a growing international literature on working hours and their relation to patient and practitioner safety, accreditation bodies such as the Australian Commission on Safety and Quality in Health Care (ACSQHC) and the Australian Medical Council (AMC) are adjusting their standards to encourage improved work and training practices. As summarized elsewhere in this supplement, a significant body of evidence speaks to the negative impact of fatigue on both patient safety and the health and well-being of junior doctors. In Australia, the potential for harm was realized in the mid-1990s as a result of a growing body of international evidence. This resulted in a variety of campaigns and reforms that have sought to ameliorate the risks of unsafe working hours. Interventions have included workforce expansion, the development of more sophisticated rostering arrangements, greater emphasis on clinical handover procedures, and more detailed specifications regarding working hours in industrial agreements. Unlike countries in Europe and North America, Australia has not adopted a strict regulatory approach to working hours.This article briefly outlines how postgraduate medical training operates in Australia, and then summarizes the available data describing working hour patterns in Australia for the past decade. The factors likely to shape junior doctor working hours over the next decade are also discussed.In Australia, postgraduate medical education comprises both pre-vocational and vocational stages.The former begins with a one-year internship, which consists of several structured rotations through different specialties . After sPre-vocational training is usually completed by means of one to two years of generalist experience, predominantly in public hospitals. This stage of training is underpinned by the Australian Curriculum Framework for Junior Doctors, which outlines the knowledge, skills, and behaviours required to work safely in a variety of health care settings  and provVocational training programs are administered by colleges, and typically require three to six years of supervised practice. Fellowship is awarded upon completion of training requirements. Like medical schools, college training programs operate within an accreditation framework managed by the Australian Medical Council (AMC). Doctors undertaking vocational training programs are usually referred to as “registrars.”The hospital workforce also comprises those doctors who do not choose to specialize and who hold positions as career medical officers (CMOs). It is thought that up to 15% of graduates will not proceed to specialty training .In this article, the term “junior doctor” refers to doctors in both pre-vocational and vocational training.Very few articles in the literature address the issue of working hours for Australian junior doctors. The few published papers point to the important links between working hours and safe practice and highlight the important role the medical profession has played in advancing this agenda in Australia -8. Only The Australian Medical Association (AMA) launched its Safe Working Hours campaign in 1995. This included the commissioning and reviewing of research in topic areas that included the actual working hours of junior doctors, systems of work, and the effect of fatigue on learning and performance. These efforts culminated in the development of a national code of practice around working hours, shift work, and rostering for hospital doctors [Advocacy by the AMA has emphasized the need to avoid absolute, enforceable limits on single elements such as the maximum length of a safe shift or the break required between episodes of work. This is because there are multiple determinants of fatigue, including quantity of night work, shift length, on-call commitments, and access to breaks ,10.The AMA has audited the work patterns of hospital doctors every five years since 2001 -13. EachThe distribution of the three risk categories across the three AMA audits, stratified by training classification, is shown in Figure The audit results show a consistent, step-wise decline in the proportion of doctors in the higher-risk categories. Note should be made of the fact that the total number of respondents in the higher-risk category was halved between 2001 and 2011. The average weekly working hours of surveyed doctors in 2001 was 61.3; it fell to 55.1 in 2011.The complete data for the three studies also show improvements across “at-risk” disciplines, such as Surgery and Obstetrics and Gynaecology. The latter has improved markedly, with 74% of respondents falling into the lower-risk category (as compared to 28% in 2006 and 7% in 2001) . InternaWhile there has been some steady and consistent improvement in working practices, the 2011 audit demonstrates that this has not been universal. For instance, one in five doctors reported that they worked every day of the seven-day audit period. For one individual, this included a shift of 43 hours. It is also of concern that one respondent declared that he or she worked 120 hours in the audit week .The Australian Institute of Health and Welfare (AIHW) collects data on working practices and employment trends for the medical workforce at the time of medical registration renewal. These data are published annually as Medical Labour Force Surveys.The latest AIHW data on total working hours confirm the trend over the past decade of reduced working hours for doctors ,17. CompSome of this reduction has been attributed to the increasing number of women in the physician workforce, who tend to work fewer hours than their male counterparts . The aveFurther corroborating evidence of the reduction in working hours is found in MABEL, an ongoing national longitudinal survey of doctors funded by the National Health and Medical Research Council . The 200Over the last decade both the number of medical schools and the number of commencing medical students has increased significantly in response to an undersupply of medical graduates that dates to 1980. Since 2002, the number of medical schools has increased from 10 to 18. As well, the total number of commencing medical students has more than doubled, with the intake increasing by 2,110 (or 127.1%) between 2000 and 2011, for a total of 3,770 students. By 2016 it is projected that the number of graduates will increase to 3,970 – an increase of 65.9% from 2009 and 183% from 1999 .State and territory governments have responded by increasing the number of postgraduate training places and are actively changing the traditional patterns of employment, in part by reducing the number of overtime shifts. As evidence of this, the number of advanced training positions for specialty trainees has increased by nearly 50% in the last five years . AlthougAlthough significant anecdotal evidence suggests that flexible workplace arrangements are becoming more prevalent, specific data describing this trend are limited. MABEL  and the The Australian government is prosecuting a major health reform agenda, central to which are mechanisms to achieve a greater focus on transparency, quality, safety, and the minimization of risk. The Australian Commission for Safety and Quality in Health Care (ACSQHC) has recently produced its 2011 report to the Australian Government drawing “attention to the importance, and challenges, of implementing improvements to the safety and quality of health care.” Chapter four – "Developing a positive safety culture” – addresses the safety culture, (including stress recognition) of health care organizations. It explicitly considers working hours and the potential negative impact of prolonged working hours on the safety and quality of health care delivery .Despite the fact that the curricula are different in the 18 universities that offer primary medical education qualifications in Australia, all schools must be accredited against AMC standards. In an explanatory note for Standard 7.3, the AMC states “Attention should be paid to the time commitment required of medical students, especially during the clinical years. Students’ expected hours for course participation should be determined taking into account the effects of fatigue and sleep deprivation on learning, clinical activities, and student health and safety .”The AMC is currently reviewing its standards for implementation in 2013. From the consultative process, it is clear that the new standards will have an enhanced focus on quality and safety in health care. It is expected that this will result in a greater focus on working hours in undergraduate curricula.Like medical schools, college training programs are also accredited against AMC standards. Standard 1.4 – “Interaction with the health sector” – has an explanatory note that states “The duties, working hours and supervision of trainees should be consistent with the delivery of high quality, safe patient care. Ensuring trainees can meet their educational goals and service delivery requirements within safe hours of work is the responsibility of all parties .”The working hours of junior doctors have been the subject of discussion in Australia since the mid-1990s. As described above, surveys of working hours suggest a small but significant reduction in overall working hours from the early 2000s. In some disciplines, particularly Surgery, hours of work have shown relative improvement from a high baseline. However, the available data have significant limitations, which derive largely from their reliance on self-reporting.The complexity of improving safety in health care with regards to minimizing fatigue has been described in international literature for many years ,28. In ATraining and service delivery are inextricably linked. Although a 2010 systematic review failed to identify an overall negative impact from capped working hours during training, trainees in various settings have raised concerns that reduced working hours will have an impact on the quality of their clinical education ,30. In AIn the context of a more than doubling in the number of medical graduates, there is pressure on Australian governments to resource adequate postgraduate training positions. In addition to these workforce changes, the ACSQHC’s explicit emphasis on developing a safe workplace culture and the development of new safety and quality accreditation standards provide regulatory pressure. It is anticipated that enhanced national emphasis on safety and quality will be reflected in the revised AMC accreditation standards, which, in turn, will be more explicitly referenced in training curricula.The Australian experience suggests that working hour reform is assisted by the effective engagement of government agencies, health services, training providers, and the medical profession. It is possible that the parallel efforts of these groups have obviated the need for national working hour regulations. These reflections may be of relevance to other jurisdictions as they consider approaches to minimizing trainee and physician fatigue.Available data suggests that the working hours of Australian junior doctors may be decreasing, with a parallel reduction in the proportion of practitioners at significant risk of fatigue. These improvements, which can be attributed to a number of factors, have been possible in the absence of a strict regulatory framework for working hours. The impact on patient safety and postgraduate training has yet to be thoroughly evaluated.Looking forward, workforce expansion, increasing emphasis on work–life balance, curricular changes, and national safety and quality reforms will continue to drive change. To achieve the best possible outcomes for patients and physicians, ongoing reform of work practices will need to be supported by additional resources for the workforce, training, and handover.NJG proposed the content and wrote the first drafts of the paper after discussion with the other authors. MAB and RM collected the working hours data and edited the drafts.The authors declare that they have no competing interests.


Ruditapes philippinarum; Pacific oyster Crassostrea gigas; Chilean mussel Mytilus chilensis; Blood clam Anadara granosa and Chinese clam Sinonovacula constricta. To date, the genomes of 5 species of mollusks have been sequenced, only one of which, Crassostrea gigas, coincides with the species with the greatest production in aquaculture. Another important species whose genome has been sequenced is Mytilus galloprovincialis, which is the second most important mussel in aquaculture production, after M. chilensis. Few genetic improvement programs have been reported in comparison with the number reported in fish species. The most commonly investigated species are oysters, with at least 5 genetic improvement programs reported, followed by abalones with 2 programs and mussels with one. The results of this work will establish the current situation with respect to the genetics of mollusks which are of importance for aquaculture production, in order to assist future decisions to ensure the sustainability of these resources.In 2012, world mollusk production in aquaculture reached a volume of 15,171,000 tons, representing 23% of total aquaculture production and positioning mollusks as the second most important category of aquaculture products (fishes are the first). Clams and oysters are the mollusk species with the highest production levels, followed in descending order by mussels, scallops, and abalones. In view of the increasing importance attached to genetic information on aquaculture, which can help with good maintenance and thus the sustainability of production, the present work offers a review of the state of knowledge on genetic and genomic information about mollusks produced in aquaculture. The analysis was applied to mollusks which are of importance for aquaculture, with emphasis on the 5 species with the highest production levels. According to FAO, these are: Japanese clam  The aquaculture industry consists of 3 main groups with different production volumes: fish, with a volume of 44.15 million tons in 2012, representing 66.3% of production; mollusks with 15.17 million tons equivalent to 22.8%; crustaceans, 6.44 million tons, 9.7%. The production of other types was 0.86 million tons, or 1.3% FAO, . Based oBiotechnology and genetic research have developed strongly in the last decade, and a large database now exists for fish species of importance for aquaculture . These new research lines have permitted great progress and increased efficiency. Research of this kind has led to the development of many applications for fish production, with an increase in the number of techniques for genetic improvement, and a wider range of genetic variability of the resources for application in studies of traceability, pedigree, degrees of relatedness, and the search for molecular markers for Marked Assisted Selection. These developments are well reviewed in Hulata , Liu andThe present work therefore reviews the current state of scientific knowledge on the genetics of mollusk species which are important for aquaculture. The amount of existing information on the genomes of these resources will be assessed—this is needed to establish a baseline for knowledge-based decision-making. Such knowledge would also enable us to define brood-stock selection programs, generate the bases for genetic improvement programs and finally assess the state of a given resource in order to ensure its long term conservation and sustainability.World mollusk production in 2012 was 15.17 million tons. Total aquaculture production of mollusks is represented by 5 main groups: clams, oysters, mussels, scallops, and abalones. Clam production during 2011 was 4.929 million tons; this was followed by oysters with 4.519 million tons; mussels with 1.802 million tons; scallops with 1.52 million tons; and finally abalones with aquaculture production of 395,000 tons FAO, .Ruditapes philippinarum or Japanese clam; Anadara granosa (=Tegillarca granosa), Blood clam or Blood cockle from Malaysia; and Sinonovacula constricta or Chinese clam. The other two are the oyster Crassostrea gigas and the Chilean mussel Mytilus chilensis. The detailed production for each of these is shown in Table There are 5 principal mollusk species produced in aquaculture , all bivalves, which correspond to only 5 species of mollusks reported in the FAO statistics 2013). Three of these are clams: 3. Three Genetic resources is the name given to all the genetic or genome information for a group of representative individuals of a species which constitutes a natural resource  search engines and the PubMed database. This allowed us to establish the low level of existing genetic information, despite the fact that it is one of the 5 mollusk species with the highest production in the world and is located in the first place of economic value production. Only around 30 papers discussing the genetics of this species were found (see database at Annex 1), although we consider that the number is not always representative of the information available. The research focused mainly on the genetic characterization of populations and the evaluation of genetic differentiation and population structure (42%), followed by searches for molecular markers (17%), and evaluation of hybrids with congeneric species (13%). A few papers describe research related to the genetic response associated with the immune system, gene expression, and transcriptome , and finally 2 works address heritability and selection measurement (8%). However, although the main focus of research is on molecular genetics and molecular biology, a large number of works was found associated with study of gene expression as it relates to the immune response or to exposure to contaminants; there were at least 19 papers from 2012 to date, showing a strong focus on the transcriptome of this species. On searching for genome information in the National Center for Biotechnology Information (NCBI), data was found on 30,442 DNA, and RNA sequences, 6469 EST (Expressed Sequence Tag) sequences, and 1046 protein sequences . This suggests that total left-shell pigmentation in C. gigas is strongly influenced by additive genetic variation and therefore amenable to selection. Another characteristic which has been extensively studied in oysters is the genetic response to the high summer mortality observed in hatcheries  values have been estimated for high survival groups (above 0.88), higher than the values for low survival groups (0.55–0.68), and the authors conclude that these values would permit good results in a selective breeding program to improve survival  of the research published on M. chilensis is associated with the genetic characterization and population structure of this species distributed along the Chilean coast. Works can be found on the identification of molecular markers, heritability, selection response and gene expression, all in much the same proportions and small quantities. This is corroborated by a search for genome information in the NCBI databases, where only 119 data were found on sequenced genes, and only 7 EST were identified . No records were found of current genetic improvement programs in this species, nor any information on baseline data which would allow the development potential of such programs to be assessed. On the other hand, the natural population structure has been studied (Ni et al., In the case of the Blood cockle a Figure , at leasa Figure  than in Sinonovacula constricta (Figure Of the 5 top species in aquaculture production, the one with the least scientific information published on genetics is the Chinese clam a Figure . In the a Figure . Howevera Figure , with tha Figure  observedCrassotrea gigas. There is great interest in this species and it has been introduced into many parts of the world for cultivation. Among mussels, the small amount of genetic information available on Mytilus chilensis is striking. At the same time the congeneric species Mytilus galloprovincialis is the most studied mussel; furthermore the latter is one of only two mussel species whose genome has been sequenced, and there are 41,294 records of DNA sequences, 19,756 of EST and 2,424 coded proteins. The species with the largest number of DNA sequences is the oyster Crassostrea angulata, with 81,015 DNA sequences, even more than C. gigas. On the other hand, C. gigas is the bivalve mollusk with the most EST sequences identified.On the basis of this review it is apparent that there is no obvious relationship between the economic importance of a species, in terms of the volume produced in aquaculture, and the amount of scientific information available about genetics/genomic resources, when restricted to the five most produced species. In this case our analysis was carried out for research into genetics, which is the basis for genetic improvement programs in aquaculture species. The scientific information found is summarized, with the databases used, in Table C. gigas, for which adequate base information exists and genetic improvement programs are currently being implemented. Sufficient information exists on the mussel M. chilensis to provide a basis for more improvement programs, but the low use of reproduction in controlled environments is a limiting factor in this species. For the other three species there is virtually no basic genetic information on which predicting their response to selection programs. Further research is required to generate a sufficient basis for selective reproduction, and also to clarify information on natural populations, identifying possible cryptic species or strong differentiation between populations. The lack of knowledge about species with high production in aquaculture could lead to population bottlenecks, since if the brood-stock management plan is unsuitable, or seed extraction from local populations is not handled correctly, the populations which sustain these resources could be drastically reduced. This could result in a severe diminution of genetic diversity, to the point where it might be difficult for the species to recover, meaning that in future these resources which are so important for aquaculture would not be sustainable.The quantity of genetic information available on bivalve mollusks varies widely, with great differences between species, even though their production volumes in aquaculture are similar. There is also still little information with which to generate solid bases for genetic improvement programs such as are observed in fish or other cultivated species. The only exception is The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Captorhinus supports the former hypothesis about a fusion of the intermedium, the tibiale, the proximal centrale (c4) and eventually c3, producing a purportedly multipartite structure of the amniotic astragalus, but the issue remained contentious. Very well preserved tarsi of the Early Permian aquatic amniote Mesosaurus tenuidensHovasaurus bouleiMesosaurus in an advanced stage of development shows that the tarsus is represented by a single bone, most probably the astragalus, which seems to be formed by the suturing of three bones, here interpreted as being the intermedium, the tibiale, probably already integrated to the c4 in an earlier stage of the development, and the c3. An amniote-like tarsal structure is observed in very basal Carboniferous and Permian tetrapods such as Proterogyrinus, Gephyrostegus, the diadectids Diadectes and Orobates, some microsaurs like Tuditanus and Pantylus and possibly Westlothiana, taxa that were all considered as true amniotes in their original descriptions. Therefore, the structure of the amniotic tarsus, including the configuration of the proximal series formed by the astragalus and the calcaneum, typically a pair of enlarged bones, could have been established well before the first recognized amniote walked on Earth. Accordingly, the tarsus of these taxa does not constitute specialized convergences that appeared in unrelated groups, they might be instead, part of a transformation series that involves taxa closely related to the early amniotes as some hypotheses have suggested.The hypotheses about the origin of the primitive amniotic tarsus are very speculative. Early studies argued that the origin of the astragalus, one of the largest proximal bones in the tarsus of basal amniotes, was produced by either the fusion of two, three, or even four of the original tarsal bones, the intermedium, the tibiale and the proximal centralia (c4 and c3), or that the intermedium alone transforms into the primitive astragalus. More recent studies have shown that the structure of the tarsus in  Gephyrostegus  with centralia 1 and 2, which are placed distally and often fuse to each other . The fusMesosaurus tenuidens and hatchling individuals, were also analyzed for completing an ontogenetic sequence previously unknown for any other Early Permian amniote. This amazing record provides useful data for characterizing the tarsal structure in early and late juvenile stages, and helps us to understand the transition towards the acquisition of the adult tarsal morphology. We present a synoptic view of the evidence we found for homologizing the primitive amniotic astragalus to the intermedium plus possibly the tibiale and proximal centralia, and propose that the suturing of these elements occurred during the embryonic stage, producing a very specialized single bone in the hatchlings. We also report the invariable presence of a navicular-like bone (fusion of c1 + c2?) in Mesosaurus tenuidens , it is often identified as the centrale e.g., , or as tHylonomus lyelli, Paleothyris acadiana and Petrolacosaurus kansensis  Mangrullo Formation Mesosaurus tenuidens from Uruguay, which is curled as if within an egg M. tenuidens. The silhouette of part of the body can be reconstructed due to the preservation of the skin. The interdigital membrane that unites the toes to the claws can be delimited as well as the robustness of the leg musculature, even in such a young individual. What could have been the plantar aponeurosis covers most of the tarsal bones SMF-R 4496 –3C. This(4)AMNH 23795 –3D is an(5)Mesosaurus specimens.MN 4741 and SMF-R 4934 –3 G from(6)At later stages, these bones develop a short contact through the lateral margin of the astragalus and the medial margin of the calcaneum –6 H to PIn summary, the mesosaur tarsus consists of two proximal bones identified as the astragalus and the calcaneum plus a single navicular-like element and five elements in the distal tarsal series , resultiMesosaurus tenuidens found in the Early Permian of Uruguay  seen on the ventral surface correspond to attachments of muscles and tendons, and the medial groove delimitates the passage of the perforating artery. Even though the mesosaur astragalus of post-hatching stages does not show the tripartite structure described in torhinus  where wetorhinus , 6 and 8The ‘navicular’ is a bone present in both synapsid and sauropsid amniotes. In the latter, it is observed at least in their basalmost representatives: a ‘navicular’ is found in captorhinids, basal diapsids, some Parareptilia and Mesosauridae and in all pelycosaurs  and 10. Mesosaurus is a novel characteristic, as all but one , while others show them fused    . Therefore, they must have fused to a different bone than the astragalus or disappeared during the evolution of modern sauropsids as they are not recognizable during the ontogeny of the most advanced taxa.Selective pressures to reduce the number of tarsal bones in the sense that they are an extension of the epipodials, favour stability by strengthening the feet to drive the body forward. Thus, the acquisition of unitary, stout structures instead of several separate, delicate bones was an improvement for sustained locomotion capabilities. Therefore, we have to be cautious regarding these findings, considering the high variability shown by the chameleons’ tarsal structure, and the lack of embryological evidence in the fossil taxa for use in comparison. Therefore, as we previously mentioned, the possibility that neomorphic elements are present in such derived groups cannot be ruled out with the available data.Gephyrostegus, Westlothiana, Tuditanus, Pantylus  because it included ten, rather than nine bones  and the fibulare  being the first tarsal bones to ossify see . They arOrobates pabsti, a diadectid  and three small distal tarsals. Indeed, despite the very good preservation of the individual, it was apparently subjected to severe diagenetic distortion; the bones were embedded in a crystalline calcite matrix and there was a significant chemical substitution around their margins  do not have a well-defined tarsus, but this can be masked by the not sufficiently good preservation of the specimen feet. Even though, in Tseajaia campi, three distinct bones seem to form the proximal line  to demonstrate the homology of the bones composing the tripartite astragalus and their presence in the earliest stages of development. Isolated astragali from the Lower Permian of Oklahoma were described by Captorhinus magnus, showing the putative tripartite structure visible only from the dorsal surface of the bones. However, this feature was discussed by Taking into account the previous evolutionary transition in favor of a composite origin of the amniotic astragalus, which of course may also include other taxa, the interpretation of us e.g.,  do not pCaptorhinus, most of the isolated astragali figured by Captorhinus as described by Captorhinus magnus , and develops a lateral expansion in the area of the heel, possibly for insertion of extensor tendons including the Achilles tendon  from interspecific variation in mesosaurs and provides more informative characters that can be used in comparative studies of amniote relationships.Mesosaurus where it fuses to the astragalus in mature individuals. The early amniote astragalus is a composite bone as can be evidenced by the presence of at most three sutured bones in the tarsus of a non-hatched mesosaurid in an advanced stage of development. These bones seem to be the intermedium and the tibiale, and the later fused to c4; and the c3. Thus, our study rejects the hypothesis that the amniotic astragalus is neomorphic.The mesosaur tarsus includes eight or nine bones: astragalus and calcaneum plus centralia 1 and 2 (fused to form the mesosaur ‘navicular’) and five distal tarsals. The ‘navicular’ is proved to be present in all subadult and adult mesosaurs, even in Regarding the analyzed ontogenetic series, we could determine that the attainment of maturity in mesosaurs can be related to a determinate tarsus structure, which can be a good age indicator to extrapolate to other groups of basal amniotes. Moreover, the morphological changes observed in the mesosaur ontogeny could have strong implications in the recognition of until now undocumented, ancestral developmental features of early amniotes.Westlothiana), but they still remain close to the earliest amniotes. This commonly shared morphology among apparently unrelated but very basal taxa reflects the primitive nature of mesosaurids, as already noted by Current morphological and comparative studies on the mesosaurid skeleton suggest other interesting similarities between mesosaurids and basal synapsids that will be properly described in a forthcoming paper. However, these features are also shared with other basal sauropsids and taxa that are not even amniotes. For instance, mesosaurs share characters with taxa previously known to be closer to Amniota  but thes10.7717/peerj.2036/supp-1Supplemental Information 1Mesosaurus tenuidens. Close view of the feet area. Arrows indicates the position of the sutured astragalus.Figures of non-hatched Click here for additional data file.


Supplemental Digital Content is available in the text Healthcare providers are often evaluated by studying variability in their indicators. However, the usefulness of this analysis may be limited if we do not distinguish the variability attributable to health professionals and organizations from that associated with their patients.Our objectives are to describe the main process and outcome indicators of primary healthcare services, analyzing the contribution to variability in these indicators from different levels: individual, health professional, health center, and health district.This is a cross-sectional study that includes all.All the individuals covered by the public Basque Health Service  over a 12-month period.We calculated the number of visits to primary care doctors, number of referrals, prescription costs, and potentially avoidable hospitalizations for ambulatory care sensitive conditions (ACSCs). Using multilevel analysis, we determined the percentage of variance attributable to each level.After adjusting for the characteristics of patients , doctors (panel size), health center , and health district, the variance in the indicators was mainly attributable to differences between patients, independently of the attending health professional, the center, or the healthcare organization, both in children  and in adults .The limited contribution of health professionals and organizations to variability in indicators should be taken into account when performing evaluations and planning quality improvement strategies. Variation in medical practice is a topic of interest in primary as well as other levels of care. Unexplained differences in healthcare may influence equity, outcomes of care provided and efficiency in the use of resources. As a consequence, numerous initiatives have been developed to measure and analyze such differences and to understand their causes.1 and developing countries,2 it is common to compare indicators obtained in populations under the care of primary care professionals. The results of this profiling have been used as a training and motivational tool for doctors (telling them their position with respect to their colleagues), to give financial rewards to those achieving good outcomes, and to guide patients toward the best-performing providers.3Both in industrialized4In this context, since 2005, in the Basque Country (Spain) the Basque Health Service (Osakidetza) has used an annual evaluation system that assesses primary care doctors, health centers, and healthcare districts in terms of efficiency in the use of healthcare resources and quality of care. In order to compare apples to apples, this assessment is adjusted for age, sex, and morbidity in the populations seen, using the Adjusted Clinical Group (ACG) Case-Mix System developed at Johns Hopkins University.5 Patients are not isolated units, independent of one another; rather they are individuals grouped in doctors’ lists, and in turn, doctors are based in particular health centers and healthcare organizations. Although some studies have indicated the need to use hierarchical statistical models for making comparisons between hospitals,6 health centers,7 and doctors,8 very few publications to date have analyzed the contribution of different levels of care to variability in primary care indicators.The logic behind this is that, once we have adjusted for differences between patients, variations in outcomes are attributable to medical practice, and hence, said outcomes may be modified directly, by acting on the way health professionals work. However, the usefulness of this approach may be limited, unless we take into account the hierarchical nature of data.In this study, we used the data from all Osakidetza primary care physicians providing healthcare to more than 2 million people, almost the entire population of the geographical area under study . Our objective was to describe the main indicators for measuring resource use , healthcare outcomes  and their relationship with factors related to patients, doctors, and health centers, as well as to ascertain the contribution of each of these levels to the variability observed in these indicators.This was a cross-sectional study, analyzing healthcare process and outcome indicators in the public health service in the Basque Country over a period of 1 year. The protocol was approved by the Clinical Research Ethics Committee of the Basque Country (reference number: 11/2012).Osakidetza is a public organization funded by taxes. It provides near-universal coverage for the population living in the Basque Country. Care is free at the point of delivery except for prescriptions, for which there is copayment, this varying with the type of disease and the patient's social and employment status.At the time of this study, in 2008, primary care health services were organized into 7 health districts, corresponding to geographical areas. Each health district was composed of 9 to 22 health centers. Funded by annual contracts with the Department of Health of the Government of the Basque Country, health districts are organizationally independent and accountable in economic terms.Primary care physicians are public sector workers, receiving a salary and a capitation payment, and work in multidisciplinary care teams. Every inhabitant is included on the list of a doctor, who is a pediatrician or a family doctor depending on the patient's age (<14 vs ≥14 years old). These primary care doctors act as gatekeepers to other levels of care. Electronic health records (EHRs) started to be introduced in 1990 and are now used by all primary care doctors.The observation period was set at 1 year, from September 1, 2007 to August 31, 2008. In such period, the total population covered by the Basque public healthcare system was 2,265,058 individuals. We considered that the primary care doctor was responsible of the care of a patient if such person was registered in his/her list most part of the year of study , independently whether they had any type of contact. Due to several reasons  57,883 people (2.6%) did not complete such 6-month period with the same doctor and were excluded from our analyses. So, the study population included almost the entire population of the Basque Country.In this study, we analyzed the data from across the public primary care network: 130 health centers, 1193 family doctors, and 286 pediatricians. The total number of registered inhabitants was 2,207,175 of whom 247,493 (11.2%) were children (age<14 years).The extent of the relationship between providers and patients can influence the quality of care and its outcomes. Inclusion of individuals with very short enrollment periods can affect the results of some providers and, therefore, the Basque Health Service has established 185 days as the minimum period to consider that a provider  is accountable for the care provided to a patient in their annual evaluations. Accordingly, we kept such criteria in our study. However, there is not a unanimous opinion between researchers about the decision of include/exclude patients with partial enrollment. So that, we repeated all our analyses limiting the study population to the 1,901,927 people that remained the full 12-month period with the same physician. Such results were very similar to the ones presented in this article and are available on demand for interested readers (please contact the authors for further information).Primary care EHRs of the Basque public health system, containing demographic, administrative, and clinical data including diagnoses, prescriptions, ancillary examinations, and referrals generated in each patient visit.The minimum basic data set, gathering information regarding all the patient discharges from all hospitals across the Basque public health network, including data on patient characteristics, hospitalization episodes, diagnoses, and procedures.We use the following sources of data:The explanatory variables used at the patient level were demographic characteristics (age and sex), morbidity (primary and secondary diagnoses), and socioeconomic status (census-based deprivation index).4 In our study, we applied a “lenient” diagnostic certainty option: any single diagnosis included in an ADG was enough to accept the corresponding ADG for such patient.In order to include a manageable number of disease categories, all the patient diagnoses (ICD-9-CM codes used by the primary care doctors during the year of study) were classified into Aggregated Diagnosis Groups (ADGs). The ADG system assigns ICD-9-CM codes to 1 of 32 categories, as a function of clinical criteria, expected resource use and the type of care likely to be required for each health problem. It is part of the Johns Hopkins population-based ACG Case-Mix adjustment system.9 In Spain, census tracts are the smallest geographical units into which census data are divided; they are mainly defined by criteria related to population size but also reflect natural and man-made features. Although the number of people living in a tract is variable, the median population per census tract is around 1200 people.The census-based deprivation index, used as a proxy for the socioeconomic status, was developed for the MEDEA project .10 and prevalence of morbidity.12This index is constructed from variables related to employment  and education . In this study, deprivation index scores were categorized into quintiles . It is a measure of the ecological effect on individuals of dwelling in a census tract and provides an indicator of the level of access to economic and material resources in a community. It has been shown to be correlated with mortality ratesAt the doctor level, we considered the number of patients on a doctor's list to estimate his/her workload.13 size of the center (number of primary care doctors), and satisfaction of the staff with their working environment. For the last of these variables, we used the overall satisfaction scores for health centers , taken from internal surveys that Osakidetza regularly carries out in all its organizations.14 As for the doctors, the health centers were categorized into quintiles. Categorization of these variables was necessary because some of the statistical models did not achieved convergence when we used them as continuous.To analyze the characteristics of the primary care centers we included variables relative to demographic characteristics of the area ,15 for the pediatric population and that of Caminal et al16 for the rest of the population.The outcome variables were the number of visits to primary care doctors, number of forms for referrals to specialized care issued to users by these doctors, and cost of medications prescribed by primary care physicians and paid for by the Department of Health during the year of study. In addition, we identified patients with hospitalizations for ambulatory care sensitive conditions (ACSCs) that might have been avoided had they received appropriate and timely outpatient care. For ACSCs, we used the list developed by Casanova Matutano et alThe study population was divided into 2 groups that were analyzed separately: individuals on pediatricians’ lists and those on family doctors’ lists. The methodology for the analysis was identical for the 2 groups. All analyses were carried out using SAS .5 were used to assess the relationship between the various different outcome variables and the characteristics of patients, doctors, and health centers. Taking into account the hierarchical nature of the data, these models include each of the explanatory variables as fixed effects and random intercepts for each of the levels .Four-level mixed effect models17 As healthcare cost data are typically nonnormally distributed with a skew toward the right, Gamma regression is a better modeling approach to deal with this skewness than ordinary least square.20 In the case of visits to the doctor and referrals, although Poisson regression is often employed for this type of outcomes, we used GLMMs with negative binomial distribution as our data showed signs of over-dispersion.21 Using likelihood ratio tests we checked that the negative binomial regression models provided a better fit than did the Poisson models.21 Finally, the existence of an ACSC hospitalization is a binary variable (yes/no), and therefore was modeled using logistics GLMMs.22 We rescaled the fixed and random effects using the method developed by Hox,23 because in multilevel logistic models the variance of the residual variance  is fixed at a constant.For prescription costs, we built generalized linear mixed models (GLMMs) with gamma distribution and log link.P value was below 0.05.All the models were developed using PROC GLIMMIX with the Laplace method. Effect sizes of associations were exponentiated and reported as ratios. We consider an association as significant when the For each outcome variable we calculated the intraclass correlation coefficients (ICCs) and percentages of variance at each level, both for empty models  and for adjusted models (full set of explanatory variables).24 Using the variance components obtained with these models, the calculation of the percentages of variance and ICCs at each level is straightforward. In a multilevel model, the ICCs are estimated as the ratio between the variance at the level (which include the variance at its higher levels) and the total variance. The ICCs can be interpreted as the correlation among observations within the same cluster .23In the case of prescriptions cost, visits to the doctor and referrals we used the method of normal approximation treating the outcomes as if they were normally distributed variables and analyzing them employing linear mixed models (PROC MIXED).22 based on a latent continuous variable that considers that the variance attributable to individuals is approximately 3.29.For ACSC we used logistic mixed models, which provide the variance components for the nonindividual levels , and the formula of Snijders,Patient, physician, and health center level characteristics are presented for adult and pediatric patients in Table By applying multilevel analysis, the demographic characteristics of patients had an influence on resource use and hospitalization for ACSCs, although not homogeneously. In the adult population Table  , the nuAlthough the differences were small, the least disadvantaged socioeconomic groups presented less frequent visits to the primary care doctor and referrals; for prescriptions, there were not differences in adults and the rates of hospitalizations for ACSCs were smaller among individuals living in richer areas; in pediatric patients, although being statistically significant their differences, there was no clear gradient for prescriptions and ACSC hospitalizations.http://links.lww.com/MD/A371). This pattern was also observed for children, except 1 group in visits and 2 in referrals for which the differences were not statistically significant. Regarding prescriptions, we found a higher use of drugs in 30 of the ADGs in both populations. The risk of potentially avoidable hospitalization in adults was higher for 20 ADGs and lower for 5 ADGs, while in children, the risk was higher for 14 ADGs and lower for 3.In adults, diagnoses in any of the 32 diagnostic groups (ADGs) were associated with more visits to the primary care doctor and referrals  and center , we did not find any statistically significant trends Tables  and 4, wIn the empty models, the variance observed was almost exclusively due to differences between patients (children or adults). Specifically, the percentage of variance associated with health professionals, and hence, health centers and districts, was <3% for prescriptions, 5% for referrals, and 9% for visits to the primary care doctor, being around 7.4% and 3.8% for potentially avoidable hospitalizations in children and adults, respectively Tables . After aOur results demonstrate that only a small proportion of variability in costs and in healthcare outcomes can be attributed to doctors or health centers. Even using a sophisticated and widely validated population-adjustment system, a significant percentage of the overall variance remains to be explained; it is attributable to patients but cannot be explained by their morbidity or demographic characteristics.7 estimated that the effect attributable to doctors’ way of working accounted for only 4% of the variance in visits of patients with diabetes mellitus, 1% in hospitalizations and 3.3% in glycemic control. Comparing hospital indicators, other authors found that the percentage of the variance attributable to health centers did not even reach 1% in the rates of readmission within 30 days6 and was around 10% in terms of use of intensive care resources.25 In a systematic review, Fung et al8 analyzed 21 studies that provided estimates of the percentage of variance explained by different levels of care, using hierarchical models; although results were mixed , the total variability attributable to the levels higher than the patient  was low, in almost all cases, when analyzing process or outcome measures, and higher considering patient satisfaction. More recently, it has been reported that 85% of the variance in outcomes in terms of overall technical quality in primary care centers26 and 92% of differences in the adequate prescription of beta blockers after a heart attack27 are attributable to patient-level factors.Other authors have also observed that patient characteristics determine most of the variability observed both between doctors and between levels of care. Hofer et al28; this has not been confirmed in other studies.30 Further, there is no consensus on what is the most suitable size for a primary care health center to achieve the best healthcare outcomes.31In our analysis, we did not find any associations of family doctor's panel size or work satisfaction, or health center size with outcomes in terms of healthcare resource use or ACSC hospitalizations in pediatric patients; in adults, we only observed more prescriptions in doctors with large panels, as well as differences in referrals to the specialists among health centers according to the percentage of elderly people in the geographic area, but without a defined gradient. Although some researchers have found an association between the satisfaction of primary care doctors and the provision of better quality care32 Wennberg classified clinical care into 3 categories: effective, patient preference sensitive, and supply sensitive, largely due to the capacity of the local health services.33 He defined unwarranted variation as care that is not consistent with patient preferences or related to patient's underlying diseases.34 However, as other authors recognize, discriminating between warranted and unwarranted variation can be challenging and interventions designed to reduce the observed variability risk eliminating variations that exist for good reason.35In healthcare organizations, it is important to record indicators of healthcare providers and analyze their variability, as they can help monitor the quality of the healthcare provided. In particular, it is unquestionable that, in this way, we may be able to detect inefficiencies in medical practice, clinical errors, and healthcare gaps, but this task is not straightforward and often variability can be attributed to the adaptation of the healthcare provided to the individual needs of patients or to the participation of patients in decision making.36 On the other hand, publishing such reports does not, in itself, ensure improvements in quality or motivate doctors to put in place measures to solve problems related to quality; in fact, an excessive emphasis by managers on reducing variability may encourage physician to “de-select” complex patients, declining to see the most sick patients, those whose treatments have failed or who do not adhere to treatment plans.7 Consequently, we believe that our findings help to put these indicators in context, since we show that only a small proportion of the variability can be attributed to healthcare professionals or the organization of health centers.In relation to this, primary care practice reports providing data on the results individual doctors has been criticized, because, among other issues, they only take into account very specific aspects of healthcare and do not provide a real measure of the complexity of the work of a primary care doctor.8However, a small proportion does not equate to no effect at all. If we translate the differences observed into costs related to prescriptions or visits to the doctor, we find significant absolute values that justify managerial interventions. Moreover, even recognizing that the greater part of the variability is attributable to patient-level factors, it may be easier and more efficient for healthcare organizations to design actions focused on their staff rather than on patients. Further, it has been suggested that doctors may be able to influence some important factors at the patient level.37 the same being true of the other levels studied. Besides, the socioeconomic variable used (deprivation index) may reduce the individual contribution to socioeconomic characteristics, given its ecological nature. Finally, the variability in other process or outcome indicators may be different to that of those analyzed in this study.The present study has some limitations. First, the data analyzed are taken from the healthcare information systems of the Basque Country, and may contain some incomplete or inaccurate information, as is always the case when using data from administrative databases or notes in EHRs. On the other hand, factors other than those analyzed may contribute to patient variability, such as those related to the family environment, psychosocial factors, or health-related habits,38To conclude, our study found that doctors and health centers make only a modest contribution to variability in resource use and healthcare outcomes. This fact should be taken into account when evaluating healthcare professionals, trying to modify their behavior, and attempting to improve the efficiency and quality of organizations. Compared to traditional analysis, complex statistical methods are more costly, require more time, and are more difficult for clinicians and managers to understand. Nevertheless, a greater understanding of the factors involved in variability and a greater reliability in the estimates will help the evaluations of healthcare providers to become accepted. Given all this, strategies for improving clinical practice should include multilevel interventions that go beyond the health professionals and include individuals themselves and the organization of healthcare services.


With the increasing popularity and scale of peer teaching, it is imperative to develop methods that ensure the quality of teaching provided by undergraduate students. We used an established faculty development and quality assurance process in a novel context: peer observation of teaching for undergraduate peer tutors. We have developed a form to record observations and aid the facilitation of feedback. In addition, experienced peer tutors have been trained to observe peer-taught sessions and provide tutors with verbal and written feedback. We have found peer observation of teaching to be a feasible and acceptable process for improving quality of teaching provided by undergraduate medical students. However, feedback regarding the quality of peer observer’s feedback may help to develop students’ abilities further. Tomorrow’s Doctors in the United Kingdom and the Liaison Committee on Medical Education in the United States [Peer teaching is becoming an increasingly important part of undergraduate medical education . It is id States , 3.Given the recent rise in popularity of peer teaching , and thePOT is a process in which an observer watches a colleague’s teaching and provides descriptive feedback on his/her teaching practice . The proGosling describes three models of POT: an evaluation model, a developmental model, and a collaborative model . In the Though the use of POT is growing in popularity , there hThis paper aims to describe how we have introduced POT in peer teaching in undergraduate medical education at one school in the United Kingdom.Keele University Medical School is a small (approximately 130 students per year) modern school with a highly integrated, hybrid, spiral curriculum. The school has a strong culture of extracurricular peer teaching that is autonomous from staff input. The vast majority of peer teaching within the school is delivered under the auspices of Keele Medical Education Society (KMES), a student-run society with the primary aim of facilitating high-quality peer teaching. Students in each year group deliver teaching to students in the academic year below them covering a variety of topics. Sessions are typically between one and two hours long, and take place on average fortnightly. Frequent topics taught include clinical examination, interpretation of investigations, anatomy and physiology, pathology, management of acute scenarios, and exam revision.Over recent years, the scale of peer teaching has surged with most sessions attended by 10 to 40 students and the most popular events attracting up to 100 students per year group. Naturally, with extracurricular teaching of this magnitude, there were concerns about the quality of teaching provided by novice teachers.In order to ensure the quality of teaching delivered by students to their peers is maximized, a programme of development opportunities has been introduced for students interested in teaching, including: peer-tutor training workshops focusing on lesson design and planning, peer review of lesson plans, and most recently, peer observation of teaching. In addition to quality assurance, POT was adopted as a process for developing students’ teaching skills.KMES have adopted a model, incorporating elements of both the developmental model and the collaborative model. The aim of POT within KMES is to develop both the tutor’s and the observer’s teaching practice through feedback, discussion and reflection. A collaborative approach emphasizes the mutual benefit to both tutor and observer. It is recognized that POT may evoke anxiety or apprehension in some tutors  and it iPOT was introduced in the academic year 2013–2014 and consisted of three stages: pre-observation meeting, observation of teaching, and post-observation feedback.Before teaching is due to take place, the observer and tutor meet to discuss the learning objectives for the session and set ground rules for the observation. As the feedback is delivered using the agenda-led model for feedback, tutors are encouraged to identify any particular areas they would like the observer to focus on during the observation.Prior to the pre-observation meeting, the tutor is asked to forward a copy of the lesson plan and any resources to be used in order for observers to familiarize themselves.At the beginning of each session, the observer is introduced to the learners by the peer tutor. The observation then takes place for the entire duration of the session during which the observer makes descriptive notes to inform feedback. Observers were advised not to interrupt the sessions unless tutors sought their input.Following the session, the tutor and observer meet to discuss the session whereby feedback is delivered through use of Pendleton’s rules , focusinA form was developed so that observers could detail their comments and feedback. The form included sections for pre-observation, observation and post-observation. The pre-observation section consisted of areas for detailing intended learning outcomes for the session and preferred areas for feedback as identified by the tutor. The observation section included statements to be rated on a Likert scale and corresponding free-text areas for detailing strengths and areas for development. The post-observation section included a description of the overall quality of the session and an area identifying goals for future development.During the first year of implementation, 11 observations were performed, each with one observer. The observers were not specifically trained to observe teaching and provide feedback, many felt under-prepared to give feedback to peers on their teaching. Furthermore, sufficient time was not always dedicated for pre- and post-observation meetings.As such, a number of changes were incorporated into the process for the following academic year. These included: providing training for peer observers, amending the form, increasing the number of observations conducted, and encouraging adequate communication between observer and tutor prior to the observation, ensuring sufficient time allocation before and after the session.The most fundamental change was the introduction of training for peer observers. The training consisted of a three-hour workshop designed and delivered by ER and BD to students with at least 1 year’s experience of peer teaching. The workshop discussed the principles and process of POT, principles of feedback, models of feedback delivery, and common pitfalls in feedback. Participants produced written feedback on example lesson plans and discussed their feedback in small groups. Participants were then introduced to KMES’ POT form and discussed how to complete it during observations. Participants subsequently watched a 10-minute video clip of an example peer teaching session during which they each completed a form. Following the clip, the group discussed the feedback from their observations, and one volunteer provided oral feedback during role-play with the tutor. This process was then repeated with a second example peer-teaching session video.In addition to the provision of observer training, the form was also updated to include space for tutors to document their reflections on their teaching.In the first semester of this academic year (2014–2015), 26 extracurricular peer-taught lessons were observed with the tutors receiving feedback. This is evidence of the feasibility of this form of tutor development and quality assurance activity. When contacted in advance regarding POT, no tutors have yet to decline to participate, suggesting it is acceptable to undergraduate student tutors.Peer Review of Observation Feedback (PROF). PROF involves senior students evaluating the written and/or verbal feedback provided by observers. To date, the use of PROF has been limited as the peer-observation programme is still a relatively new process within the school and the majority of resources have been focused on developing this programme first. However, further implementation of PROF would aid the development of peer observers and will be a focus in future years.To further support peer-observer development, observers are offered the opportunity to receive feedback on their feedback in a process we have termed At present, there is no literature describing the effectiveness of peer observation of medical students’ teaching, exploring the quality of the feedback provided by peers, how student tutors use feedback on their teaching, and how this supports their development as teachers would enable us to better understand the value of peer observation of student teaching.This paper has described the use of an established faculty development and quality assurance process in a novel context: peer observation of teaching for undergraduate peer tutors.None.None.Not required.


We examine investor behavior under interest and inflation risk in different scenarios. To that end, we analyze the relation between stock returns and unexpected changes in nominal and real interest rates and inflation for the US stock market. This relation is examined in detail by breaking the results down from the US stock market level to sector, sub-sector, and to individual industries as the ability of different industries to absorb unexpected changes in interest rates and inflation can vary by industry and by contraction and expansion sub-periods. While most significant relations are conventionally negative, some are consistently positive. This suggests some relevant implications on investor behavior. Thus, investments in industries with this positive relation can form a safe haven from unexpected changes in real and nominal interest rates. Gold has an insignificant beta during recessionary conditions hinting that Gold can be a safe haven during recessions. However, Gold also has a consistent negative relation to unexpected changes in inflation thereby damaging the claim that Gold is a hedge against inflation. A lot of previous financial research assumes that investors are rational agents, so they try to optimize wealth and minimize risk  and by sector. Specifically, some financial  sectors have insignificant relations and we even find some contrary results when examining the relations by sector, sub-sector, and industry. Some industries have a consistent significant positive relation between stock returns and unexpected changes in real and nominal interest rates. Interestingly, Gold, among others, has a negative relation to unanticipated inflation in the overall sample and in the contraction and expansion sub-periods suggesting that it is exposed to inflation risk.The rest of the paper is structured as follows. Section Materials and Methods present the main methodology used in this research. Section Data describes the data and variables included in our empirical analysis. Section Empirical Results comments on the results of our research, and finally, Section Discussion makes concluding remarks.In this section, we explain how we measure unexpected changes in the nominal rate of interest. Then we explain how we measure the expected rate of inflation that is used as an input to decompose the unexpected change in nominal interest rates into unexpected changes in inflation and unexpected changes in the real rate of interest. Finally, we describe how we classify the state of the economy into expansion and non-expansion (contraction) states.Sweeney and Warga , Kane anThe returns on Treasury securities for different maturities are usually used as risk-free interest rate proxies because Treasury securities are commonly assumed to have no default risk. Of the long term maturities, 10 years tends to be the most liquid and accurately estimated as the Fed has continuously auctioned 10 year Treasury notes throughout our sample period so there is always a recently issued 10-year note that the Fed can use to accurately estimate 10 year treasury yields. Therefore, we use changes in the 10-year US Treasury bond yields, as reported by the Federal Reserve Bank of New York, as our approximation for unexpected changes in the nominal interest rate. We repeat our empirical results using 3-month, 1-, 3-, and 5- year US Treasury bond yield changes and find the results are very similar. These are available from the corresponding author upon request.t) can be broken down into the sum of its expected  to choose the ARMA  process to predict the month-to-month annualized inflation rate. Therefore, we suppose short-sighted expectations as in Leiser and Drori . Unit roEt as estimated above from the nominal rate of interest it.As mentioned above, we use changes in the 10-year US Treasury bond yield as our approximation for unexpected changes in the nominal interest rate. To obtain unexpected changes in the real rate of interest we assume the Fisher approximation and subtract the expected rate of inflation Then, changes in the above relation form our approximation for unexpected changes in in the real rate of interest.The literature focuses mainly on the Stone  two-factrjt is the stock  j return in month t, βj shows the stock sensitivity to market movements, rmt is the return on the market portfolio, jt is the error term.Typically, studies of interest rate sensitivity of stock returns start from the Capital Asset Pricing Model CAPM augmented by unexpected changes in nominal interest rates Stone,  to betteit into real interest rt and expected inflation Et components. Taking the first difference in interest rates as unexpected changes in nominal interest rates at time t, we then have unexpected changes in the nominal interest rate rt and unexpected changes in the anticipated inflation rate ΔEt. Thus, the second model estimated in this paper is the following:rjt is the stock  j return in month t, βj shows the stock sensitivity to market movements, rmt is the return on the market portfolio, Δrt represents unexpected changes in real interest rates, jt is the error term.We extend the Stone  model byTo avoid possible high collinearity between the explanatory variables, the financial economics literature uses some orthogonalization procedure. In Table We choose this orthogonalization method because this is in line with the aim of this research, which is to estimate the response of stock  returns to unanticipated changes in nominal interest rates and its' decomposition, unexpected changes in real, and unexpected changes in inflation rates. Therefore, we analyze direct and indirect effects of interest rate shocks and obtain clear economic intuition. We find similar results to those obtained without orthogonalizing and also very similar results when we interchange the dependent and independent variables. Thus, our results seem to be robust, since this orthogonalization process evidently only eliminates the correlation between variables.The final correlations between explanatory variables included in our model are reported in Table Like Veronesi , Knif etTable Our data set includes monthly indices for the US sector, sub-sector, and industries from November 1989 to February 2014, 292 monthly observations in all. The US sector index is based on the “Global Industry Classification Standard” GICS as developed by Morgan Stanley Capital International and Standard &Poor's. This classification aims to enhance the investment research and asset management process for financial professionals worldwide. Also, GICS is the result of numerous discussions with asset owners, portfolio managers and investment analysts. Finally, this classification is designed to respond to the global financial community's need for an accurate, complete, and standard industry definition. The sub-sector and individual industry indices are refinements of the GICS compiled by and obtained from Bloomberg. We also use the monthly S&P500 market index from Bloomberg and the monthly 10-year US Treasury yields from the Federal Reserve. Finally, we use the monthly expected inflation rates as explained in Section Expected Inflation Rates.The Supplementary Material Table Table We examine the stationary of the variables in the second part of Table rjt, we compute the log relatives using the closing index of the last day of the current month Pjt relative to the closing index of last day of the previous month Pjt−1. That is,In order to obtain continuously compounded returns for industry sectors, sub-sectors, and industries To avoid income smoothing, we use index values net of dividends. We use the S&P500 index as a suitable representative of the US stock market and compute the log relative return in an analogous way as in (3) to obtain market log-returns.We estimate two models, (1) examines the relation between stock returns and unanticipated changes in nominal interest rates and (2) estimates the relation between stock returns and unanticipated changes in real interest and inflation rates. Both models are applied separately by sector, sub-sector, and industry and are estimated throughout the sample period and during expansion and contraction economic sub-periods from September 1989 to February 2014. We estimate models (1) and (2) separately using the “seemingly unrelated regression” SUR technique Zellner,  for eachWe regress models (1) and (2) at the sector level and we report the results in Table Looking at model 1 for the overall sample period, the results confirms a noteworthy relationship between sector stock returns and unexpected changes in nominal interest rates as 6 of the 10 sectors have a statistically significant coefficient. Interestingly, the sign of this relationship is not always negative. Consumer Staples, Health Care, and Utilities are conventionally negative but Energy and Materials are marginally positive and Information Technology is significantly positive. Clearly, the positive coefficient for Information Technology is not due to mere chance. Moreover, the relation between stock returns and unexpected changes in nominal rates for the Information Technology sector remains significantly positive for the contraction and expansion sub-periods. This suggests that investors who seek protection from unanticipated interest rate changes can view an investment in a portfolio of Information Technology stocks as a natural hedge against interest rate risk.Meanwhile, the conventionally negative relation between stock returns and unexpected changes in nominal interest rates for Consumer Staples, Health Care, and Utilities remain negative for the recession and expansion sub-periods but only the Consumer Staples coefficient remains highly significant in both sub-periods. Clearly, an investment in the Consumer Staples sector is subject to a significant amount of interest rate risk. Finally, there are two sectors without any significant relation between stock returns and unexpected changes in nominal interest rates for the entire sample but show significant coefficients, with opposite signs, for the contraction and expansion sub-periods. Specifically, Consumer Discretionary, and Industrials have the conventional inverse relation during contraction which turns positive during expansion suggesting that firms in these industries can pass on additional financing costs when economic conditions are robust.When decomposing unexpected changes in the nominal rate of interest into unexpected changes in the real rate of interest and unexpected changes in the inflation rate (model 2), we discover comparable results for unanticipated changes in the real rate of interest but in this case, there are just four rather than six sectors that are statistically significant. Consumer Staples and Utilities have a significant inverse relation between stock returns and unexpected changes in the real rate of interest whereas Energy and Materials have a significant positive relation. However, none of these relations remains consistently significant and of the same sign for the contraction and expansion sub-periods with the exception of Energy. Even then the positive coefficient in the expansion sub-period is only marginally significant.Similarly, the signs of the relation between stock returns and unanticipated inflation are not always negative. Specifically, we find significant negative coefficients for Consumer Staples, Health Care, and Utilities and one positive relation for Information Technology. However, only Consumer Staples has a consistent inverse relation for both economic sub-periods suggesting that unexpected changes in inflation are an important source of risk for investments in the Consumer Staples sector. Interestingly, stock returns in the Industrials sector are directly related to unanticipated inflation in expansion sub-period but are inversely related to unanticipated inflation in contraction sub-period suggesting that firms in this sector can pass on unexpected inflationary costs during robust economic conditions but are less able to do so during harder economic times.In summary, we find that when there are significant relations between stock returns and unanticipated changes in nominal interest rates and their components, unanticipated changes in the real rate of interest and inflation, these relations are most commonly negative. The Consumer Staples industry sector shows this tendency most strongly as the relation between stock returns and unanticipated changes in the nominal interest rate as well as unanticipated changes in the inflation rate are significantly negative overall and in the contraction and expansion sub-periods. Even the relation between stock returns and unexpected changes in the real rate is negative but significantly so only for the contraction sub-period. Meanwhile we observe the contrary positive relation more rarely. The clearest example is the Information Technology sector. Specifically, while all the significant relations between stock returns in the Information Technology sector and unanticipated changes in nominal interest rate, real rate and inflation rate are always positive, they are consistently and significantly positive overall and in the in the contraction and expansion sub-periods only for unexpected changes in the nominal rate of interest. The next step is to see if we can discover more instances of these significant relationships as we further refine our analysis by examining more refined sub sector portfolios.In the second step of our analysis, we estimate model 1 and 2 at the sub-sector level as defined in Supplementary Material Table For both models, we find a positive and significant market beta for the total sample and for the expansion and contraction sub-periods for all sub-sectors with just one exception. There are <100% sub-sectors with a statistically significant positive market beta during the expansion sub-period because the beta for Construction Materials, while positive, is statistically insignificant. The average beta is close to the theoretical beta of 1, being a little higher in the contraction sub-period and a little lower in the expansion sub-period. For the overall period, betas range between about 0.4 for Electric Utilities and 1.5 for Semiconductors and Semiconductor Equipment. For the sake of brevity, we do not report the coefficients for each of the 33 sub-sectors. They are available from the corresponding author upon request.The average significant sub-sector coefficients, along with the average of the significant positive and negative coefficients are shown in column 3 of Table Clearly, the overall results are consistent with most of the prior literature as the relations between stock returns and unexpected changes in the rate of inflation are most often negative. Specifically, column 2 shows that around 42, 33, and 27% of the sub-sectors for the total sample, contraction, and expansion sub-periods, respectively, have stock returns that are significantly and negatively related to unexpected changes in the inflation rate.Nevertheless, there are some exceptions to the conventionally inverse relations. For instance, Table In summary, we find that on average, the relation between stock returns and unanticipated changes in the nominal rates of interest (model 1) and unanticipated changes in the real rate of interest and the inflation rate (model 2) are negative. This result is consistent with the literature. However, as we saw at the more aggregate sector level, we continue to find contrary positive relations at the sub-sector level. This motivates us to examine individual industries to see if we can find exceptional industries where investments in these industries can form a natural hedge against sources interest rate and inflation risk.As a last step, we regress models 1 and 2 at the industry level. We again examine the relations for the total sample, contraction, and expansion sub-periods and obtain some remarkable results. Tables For both models in all six panels from A1 to C2, all industries exhibit positive and significant market betas for the overall sample and for the expansion and contraction sub-periods with just one interesting exception. While all of the industry betas during the contraction sub-period in the Materials sub-sector are positive for both models, Tables Model 1 in Table  reports In Table In the expansion sub-period reported in Table There is an interest phenomenon contained within these results. Stock returns for the Diversified Metals and Mining industry  have a positive and significant relation between stock returns and unexpected changes in nominal interest rates for the overall, contraction and expansion sub-periods. This suggests that an investment in these industries can form a natural safe haven against unexpected changes in the nominal interest rate.Meanwhile, Model 2 Tables However, within these general results we find three industries, one each in the Energy, Industrials and Materials sectors, have a consistently significant, and positive, relation between stock returns and unexpected changes in the real rate of interest. Specifically, we find that stock returns in the Integrated Oil and Gas, Commercial Services, and Supplies and Diversified Metals and Mining industries have a consistently positive relation with unexpected changes in the real rate of interest for the overall, contraction, and expansion sub-periods. This suggests that investors can find that an investment in these industries can provide some insulation from unexpected changes in the real rate of interest.We find that the stock returns of many industries respond to unexpected changes in the inflation rate. Overall, 23 of 82 industries respond significantly to unexpected changes in inflation, while during the contraction sub-period the number of significant relations rises to 30 and during expansion the number of significant relations falls only slightly to 21. The stock returns for industries in the Energy sector exhibit the highest average response to unexpected changes in the inflation rate for the total sample (7.19) and contraction sub-period (12.58) whereas firms in the Financial sectors have the highest average response in the expansion sub-period (4.65). In contrast, industries in the Consumer Discretionary sector have the lowest average response to unexpected changes in the inflation rate for the total sample (−0.29) while industries in the Materials sector have the lowest response in the contraction and the expansion sub-periods, −2.46 and 0.37, respectively.On average, the majority of sectors, most notably Consumer Discretionary, Consumer Staples, Financials, Industrials, and Materials, have an industry weighted negative significant relation between stock returns and unexpected inflation. While overall, and in some of the sub-periods, we can find industries with a significant positive relation between stock returns and unexpected inflation, we are unable to find an industry that has a consistently positive relation with unexpected inflation. However, we do find that stock returns in the Household Durables, Pharmaceuticals, and Gold industries have a negative relation to unanticipated inflation in the overall sample and in the contraction and expansion sub-periods suggesting that stocks in these industries are exposed to significant inflation risk.As mentioned previously, according to most of literature, the response of stock returns to changes in nominal and real interest rates is usually negative. Our results generally agree with these previous findings. Also, like Booth and Officer , Bae 19, Jareño Many studies have analyzed the sensitivity of stock returns to changes in nominal interest rates (Sweeney and Warga, At the sector level, we find that the most vulnerable sector to fluctuations in 10-year government bond yields are Utilities, so regulated and seriously indebted sectors seem to be the most interest rate sensitive, particularly in the expansion sub-period. Also, we note that Consumer Discretionary and Industrials have the conventional inverse relation between stock returns and unanticipated changes in the nominal rate of interest during the contraction sub-period that turns positive during the expansion sub-period so that for the overall period, there is no significant relation. This suggests that firms in these industries can pass on additional financing costs when economic conditions are robust.In order to deepen in our analysis, we decompose unexpected changes in the nominal interest rate into unexpected changes in the real interest and inflation rates. In general, the stock returns by sector, sub-sector and industry are inversely related to unexpected changes in the real interest rate movements, and unexpected changes in the inflation rate overall and more so in the contraction than in expansion sub-period. However, it is unusual to find industries with a consistent negative relation between stock returns and unanticipated changes in the real interest rate and the inflation rate. There are three exceptions however. Evidently, inflation is an important source of risk for investments in Household Durables, Pharmaceuticals and Gold industries as they have a negative relation to unanticipated inflation in the overall sample and in the contraction and expansion sub-periods.It is remarkable that stock returns are inversely related to unexpected inflation for the Gold industry, thereby damaging the image of Gold as a hedge against inflation. Another interesting result is that the stock returns in the Gold industry are not significantly related to the market return during contraction economic sub-periods thereby bolstering Gold's reputation as a safe haven during recessionary conditions.Interestingly, we find that investments in three industries, specifically Integrated Oil and Gas, Commercial Services and Supplies, and Diversified Metals and Mining can provide a safe haven against unexpected changes in the real rate of interest. Specifically, we find that the stock returns in these industries have a consistently positive relation with unexpected changes in the real rate of interest for the overall, contraction and expansion sub-periods. This suggests that investments in these industries will tend to increase if real rates of interest unexpectedly rise, thereby offsetting extra costs associated with a rise in the real rate of interest.Our empirical results support the state-dependent nature of the investor behavior in the interest rate sensitivity analysis. Also, this study may find a herding behavior of investors in some scenarios, because in certain times of market stress, investors disregard their own information, and exhibit herding behavior, which is often extremely optimistic or pessimistic and may lead to an unreasonable reaction to movements in interest rates. Finally, we confirm the null hypothesis that investor behavior may depend on different factors that affect the investment or trading decision. Therefore, aspects such as the sector that traded stock belongs to and the business cycle definitely impact on investment behavior.All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.