Australian epidemiologists have recognised that lay readers have difficulty understanding statistical graphs in reports on population health. This study aimed to provide evidence for graph design improvements that increase comprehension by non-experts.This was a double-blind, randomised, controlled trial of graph-design interventions, conducted as a postal survey. Control and intervention participants were randomly selected from telephone directories of health system employees. Eligible participants were on duty at the listed location during the study period. Controls received a booklet of 12 graphs from original publications, and intervention participants received a booklet of the same graphs with design modifications. A questionnaire with 39 interpretation tasks was included with the booklet. Interventions were assessed using the ratio of the prevalence of correct responses given by the intervention group to those given by the control group for each task.The response rate from 543 eligible participants (261 intervention and 282 control) was 67%. The prevalence of correct answers in the control group ranged from 13% for a task requiring knowledge of an acronym to 97% for a task identifying the largest category in a pie chart. Interventions producing the greatest improvement in comprehension were: changing a pie chart to a bar graph (3.6-fold increase in correct point reading), changing the y axis of a graph so that the upward direction represented an increase (2.9-fold increase in correct judgement of trend direction), a footnote to explain an acronym (2.5-fold increase in knowledge of the acronym), and matching the y axis range of two adjacent graphs .Profound population health messages can be lost through use of overly technical language and unfamiliar statistical measures. In our study, most participants did not understand age standardisation and confidence intervals. Inventive approaches are required to address this problem. Local, regional, national and global public health authorities publish an ever-increasing number of reports that statistically describe the health of the populations they serve. Graphs form a large component of such reports, because they provide a visual means to summarise relationships between variables that influence health.The relationship between the design of graphs and the ability of people to comprehend them has been extensively studied in the fields of cognitive psychology, education, ergonomics and statistics. However, little information is available on comprehension of population health statistics.In 1999, the need to improve methods of communicating epidemiological and statistical concepts to lay audiences was recognised and incorporated into Australia's National Public Health Information Development Plan [This report describes the results of a component of the project aimed at testing specific interventions that were identified as being particularly relevant to the types of graphs appearing in Australian population health publications, but for which strong evidence could not be found in the literature. A secondary aim was to assess whether comprehension of the data and the success of interventions varied by educational attainment.This was a double-blind, randomised, controlled trial, with data collected through a self-completed questionnaire. Participants were randomly assigned to receive either a "control" or an "intervention" booklet of graphs. Both groups received an identical questionnaire that explored participants' understanding of the meaning of the graphs.Study participants were blinded to their control or intervention status. Study personnel and researchers were blinded to the status of respondents until after data analysis occurred. Each respondent group was assigned an arbitrary group identifier that did not reveal their status, even during analysis of the results. Data entry personnel were blinded to the respondent status, as any graph booklets returned with questionnaires were discarded prior to data entry. The status of each group was revealed only after analysis was complete.The "control" booklet contained 12 graphs reproduced from an original Australian population health publication. They covered a range of different graph styles and numerical measures, including population size, disease incidence rates, disease prevalence, incidence rate ratios, and risk of developing disease. Statistical and epidemiological concepts, such as age standardisation and confidence intervals, were included in some graphs.Graphs for the intervention booklet presented the same statistical information as those in the control booklet, but were subject to one or more changes. The changes were chosen in an effort to improve comprehension of the statistical information depicted in the graph. To limit the number of graphs and thus respondent workload, more than one change was made to some graphs. In some cases, changes were collectively intended to improve understanding, while in others, they were chosen to target specific aspects of comprehension within that graph. The control and intervention version of each graph is shown in Figures The questionnaire contained several questions relating to each graph, 39 questions in all. Each question was framed in relation to the population health interpretation of the information presented in the graph rather than to extraction of information in isolation from its population health meaning. Questions were also designed to specifically assess the effect of changes made to the graphs for the intervention booklet. Prior to finalising the questionnaire, it was piloted with a convenience sample of 20 people. All but one returned the pilot survey. Pilot respondents were asked to comment on difficulties they had, and consequent changes were made.The questionnaire also collected demographic details: education level, preferred language, age group, and sex. Respondents were also asked their work title, how frequently they used graphs, and to rate their visual ability to read the graphs presented.The control and intervention graph booklets and the questionnaire are available as appendices to the project report see .The study population included employees of the public sector health system of the State of New South Wales (NSW), Australia, regardless of the nature of their work. The state health authorities administer the delivery of, and policies for, public hospital and other public health services for the population of NSW. The workforce therefore includes people with a broad range of job types, not just in medical and health fields. Personnel conduct a broad range of activities, including clerical, financial, policy, scientific, information technology, engineering, maintenance, cleaning, and facilities management. Regional public hospital and community health services are administered by regional health authorities that are in turn administered by the NSW Department of Health. The sampling frame included employees whose contact details were listed on one of five telephone directory databases for employees of the main NSW Department of Health administration (1159 employees), an urban regional health authority (9629 employees), a mixed urban/rural health authority (1840 employees), and two rural health authorities (3560 employees). At the time, there were 17 regional health authorities in NSW, of which five were urban, four were semi-rural, and eight were rural.In total, 650 participants were randomly selected without stratification from the combined databases of 16188 employees, and these were randomly allocated into one of two groups of 325 participants each: the intervention and control groups. Each participant was posted a package containing a cover letter from the NSW Chief Health Officer inviting their participation, a questionnaire booklet, a control or intervention graph booklet, and a reply-paid envelope. Other than the letter from the Chief Health Officer, there was no incentive for participation. Up to six follow-up reminder calls were made to non-responders. These calls also allowed ineligible participants to be identified. Ineligible participants were those who no longer worked for the health service, who were unknown at the available contact address, or who were not on duty for the survey period.Unanswered questions were treated as incorrectly answered. The comprehension rate was defined as the prevalence of correct answers within a respondent group. We categorised the comprehension rate according to the following scale: 0% to <20%, very low; 20% to <40%, low; 40% to <60%, moderate; 60% to <80%, high; and 80–100%, very high.The effect of the interventions on each task was assessed by calculating the ratio of the comprehension rate in the intervention and control groups, with a 95% confidence interval (CI). To assess whether comprehension varied by educational attainment, separate analyses were conducted for subgroups of respondents categorised as university-qualified or non-university-qualified. Analysis was conducted using SPSS version 10.Of the 650 participants selected, 543 were eligible, and of these, 187 control and 176 intervention participants returned completed, usable questionnaires, giving an overall response rate of 67% .Sex, age, preferred language, education, and work position were similarly distributed between the control and intervention arms of the study. Intervention participants were somewhat more likely to rate themselves as frequent graph users than were control participants, and more likely to rate themselves as having good visual ability Table .The maximum proportion of missing answers for any comprehension task was 4% for the control group and 3% for the intervention group.In the control group, one of the 39 comprehension tasks had a very low comprehension rate and four tasks had low comprehension rates. Eight tasks had high and 17 had very high comprehension rates Table .The actual comprehension rates for each task for the 187 controls are shown in Table The tasks with the highest comprehension rates included: choosing the largest (97% comprehension rate) and smallest (91%) categories, and comparing the magnitude of two categories (95%) from a pie chart Figure ; determiFor all respondents, the interventions reduced the number of tasks with a very low comprehension rate from one to zero and those with a low comprehension rate from four to one. The number of tasks with a very high comprehension rate increased from 17 to 28  Figure . This ch• Changing the y axis of a graph so that the upward direction represented an increase rather than a decrease in the plotted quantity when judging the direction of a trend  Figure . This ch• Including a footnote to explain an acronym and perform a task that requires knowledge of the meaning of the acronym    Figure . The comSuccess at comprehending the graphs was lower for the group of 57 control participants without university qualifications than for the group of 124 control participants with university qualifications. Those without a university qualification had a low or very low comprehension rate for 10 of the 39 tasks, compared with 3 tasks for those with a university qualification. Those without a university qualification had a high or very high comprehension rate for 23 tasks compared with 29 for university-qualified participants , which was changed to a horizontal bar graph with confidence intervals and a footnote was included for interpreting the confidence intervals Figure . The preTo our knowledge, this is the first randomised, controlled trial assessing interventions to graph design aimed at increasing readers' ability to understand statistical information about population health. In fact, the evidence base for graph comprehension and related cognitive processes in general is largely limited to studies conducted in laboratory settings with small groups of participants, usually university students. We are aware of only one other study that randomly selected participants from a defined population, and it had a response rate of only 50% . FurtherOur findings are of benefit from two perspectives. Firstly, we were able to quantify the proportion of readers who could extract some typical statistical interpretations from a sample of graphs used in Australian official population health publications. Depending on the graph and the specific interpretation sought, the proportion of readers able to interpret the graphs correctly ranged from as few as 13% to as many as 97%. Secondly, we were able to quantify the impact on comprehension levels achieved through the simple changes we applied to the graphs. This resulted in a maximum 3–4-fold increase in the proportion of readers who correctly extracted specific information from the graphs.While recommendations have been made about graph titles or captions and labels -9, thereThe most dramatic result of the study related to a vertical bar graph showing that Aboriginal people in a region of Australia had an increased risk of mortality at every age compared with the general population; in some age groups, the increase in mortality was almost 10-fold. More than 40% of control participants  were unable to determine from the graph the simple fact that Aboriginal people had a higher risk of death. A combination of interventions that included a title plainly expressing the question that was answered by the graph and the addition of text labels on the vertical axis that directly related to the title, more than halved the proportion of participants who did not grasp this fact.People working in public health and epidemiology regard the concept of disease incidence as quite commonplace. However, we found that <60% of all participants and less than half of non-university-qualified participants could answer a question that required an understanding that disease incidence refers to the rate of new cases of disease in a period of time. Changing the label on the incidence rate series from "Incidence..." to "New cases (incidence)..." had a statistically significant benefit for both university and non-university-qualified participants.To our knowledge, there is no literature on whether graph readers understand statistical concepts used in graphs, despite some recommendations being available ,9. Two sReducing information in graphs should improve reader performance -12, but We investigated the relative value of line and bar graphs for displaying information that is plotted against a categorical x axis that represents a numerical quantity, such as year or age. A line graph and a grouped bar graph of multiple disease trends by year performed equally well for point-reading tasks, but the line graph produced a marginal improvement in trend judgement in participants without a university qualification. This is as expected; bar graphs encourage discrete rather than trend-based comparisons , althougThe "population pyramid" is a popular choice for representing the age distribution by sex of a population. It is in fact a vertically oriented side-by-side bar graph. It can, however, also be represented as a horizontal format line graph with two series, each series showing the population size by age for each sex. To a greater extent, surprisingly, in university-educated participants, the line graph improved a broad comparison of the size of the male and female populations over a range of age groups. Interpretation of the broad shape of the population distribution was unaffected by the intervention.Dot graphs have been proposed as an improvement on bar graphs . We founPie charts are often derided because their non-linear format inhibits precise estimation of statistical quantities ,18. HowePaired graphs showing a quantity separately for each sex, or for some other population characteristic, are common in population health publications. Several interpretation tasks explored the consequences of using differing scales in adjacent graphs. Many respondents, particularly those without university qualifications, appeared to answer questions based on visual relativities rather than from studying the labels on the axes. For tasks comparing the relative magnitude of quantities between the two graphs, a matching scale range on each graph greatly improved comprehension. If comparisons between adjacent graphs are important, then the same axis range should be used to avoid confusion. This is consistent with Kosslyn's recommendation , and shox", where x is the quantity graphed, because, for example, a 1 in 20 risk is larger than a 1 in 50 risk. Although this finding may be culturally specific, it would be reasonable to assume that for a horizontally oriented graph, the left-to-right direction should represent increasing values.We found strong evidence for ensuring that higher values of the quantity presented on the graph are shown in the upward direction, even if this means the numerical labels are decreasing in the upward direction. This situation can arise when the risk of experiencing a disease is expressed as "1 in Several issues need to be borne in mind when considering the findings of our study. Despite the randomised design, there were differences between the control and intervention groups in terms of self-rated visual ability and frequency of graph use. Intervention participants were somewhat more likely to rate themselves as frequent graph users than control participants and more likely to rate themselves as having good visual ability. However, the observed differences may reflect the fact that many of the intervention graphs were more easily understood than the control graphs. These questions were asked at the end of the questionnaire, and intervention participants may have felt more comfortable rating themselves more highly on these characteristics.Because in some cases we made more than one change to the intervention graph, we could not completely attribute the impact of a single change to a single outcome. However, we aimed to minimise this difficulty by making the interpretation tasks as specific as possible to a specific intervention. This approach balanced respondent burden with the need to test many interventions for many graph styles.The results we obtained are probably an overestimate of levels of comprehension that would be achieved in the general population. People working in public health and policy-related areas represented approximately one-fifth of respondents. These employees would be most likely to require information on population health statistics for their work. Many other people in the health system would have a professional understanding of health and medicine. Two-thirds of respondents in our study had university qualifications, compared with approximately one-fifth of the population aged 25–64 years in Australia .The graphs we used were taken out of the context of their original report, and we recognise that much of the explanatory information required to understand the graph might have been contained in the surrounding text. However, if readers unfamiliar with the subject are required to hunt for explanatory information, they may weary of obtaining knowledge about population health. Publishers of scientific journals often require graphs to be able to "stand alone", and we support this objective, but would add that for documents intended for a public audience, the graphs should stand alone for a broad sector of the target readership.Finally, in some cases we removed information contained in the control graph to test the effect of simplifying the graph. The information we removed may have been an important dimension that the original graph designers wanted to communicate. This study thus highlights the trade-off between detail and successful communication. An alternative to presenting multiple variables within one graph is to present a series of simpler graphs for each subgroup of an additional variable.Profound population health messages can be lost by the use of overly technical language and statistical measures that are unfamiliar to a general audience. This study provides new evidence to support a range of recommendations on how to improve the design of graphs. This represents a clear opportunity to improve delivery of public health messages through graphs to a wider sector of the population. However, it is clear that, regardless of graph design, concepts such as age standardisation and confidence intervals were not understood by the majority of participants, regardless of their level of education. This is a vexed problem, because these concepts are crucial to accurate interpretation of statistical information in population health and epidemiology. There remains, therefore, an opportunity for inventive solutions to deliver the messages implied by these manipulations without increasing the difficulty of interpreting the graph.The author(s) declare that they have no competing interests.DM managed the project, advised on the population health and epidemiological requirements of the project, and drafted the manuscript. AS and DM designed the survey. AS managed the survey and conducted the statistical analyses. RM and AS conducted the literature reviews for the project. LJ provided technical advice on the project and edited the manuscript. All authors agreed to the final version of the manuscript.The pre-publication history for this paper can be accessed here:Full report of the literature reviews conducted for the project.Click here for fileFull report of the experimental study. Contains the published report with complete results and appendices showing the control and intervention booklets and questionnaireClick here for file


To demonstrate the potential of Population Impact Measures in helping to prioritise alternative interventions for psychiatry, this paper estimates the number of relapses and hospital readmissions prevented for depression and schizophrenia by adopting best practice recommendations. The results are designed to relate to particular local populations.Literature-based estimates of disease prevalence, relapse and re-admission rates, current and best practice treatment rates, levels of adherence with interventions and relative risk reduction associated with different interventions were obtained and calculations made of the Number of Events Prevented in your Population (NEPP).In a notional population of 100,000 adults, going from current to 'best' practice for different interventions, the number of relapses prevented in the next year for schizophrenia were 6 (increasing adherence to medication), 23 (family intervention), 43 (relapse prevention), and 44 (early intervention); and for depression the number of relapses prevented in the next year were 100 (increasing care management), 227 (continuing treatment with antidepressants), 279 (increasing rate of diagnosis), and 325 (Cognitive Behaviour Therapy). Hospital re-admissions prevented in the next year for schizophrenia were 6 (increasing adherence to medication), 36 (relapse prevention) and 40 (early intervention).Population Impact measures provide the possibility for a policy-maker to see the impact of a new intervention on the population as a whole, and to compare alternative interventions to best improve psychiatric disease outcomes. The methods are much simpler than others, and have the advantage of being transparent. The problem is how to decide which ones to prioritise for service development. While measures, such as Number Needed to Treat (NNT) and Quality Adjusted Life Years (QALY), do provide estimates of the benefit of interventions, they do not allow impact at the population level to be quantified. We have developed a new set of Population Impact Measures (PIMs) to describe the population impact of risks and benefits -4. PopulThe measure NEPP is a population extension of the well-known Number Needed to Treat (NNT), and takes into account the frequency of the condition in the population and the proportion of those with the condition who are actually exposed to the intervention. The measure provides local context to previous measures, allowing policy-makers to identify and prioritise the potential benefits of interventions on their own population. This paper derives NEPP for interventions used in two important psychiatric conditions, and demonstrates the potential for helping to prioritise interventions to make maximal impact on the population.We have obtained literature-based estimates of the components required to calculate the NEPP for a number of different interventions for schizophrenia and for depression. There are a number of relevant practice guidelines in the UK -7, and iWe chose to examine the population impact of interventions recommended in the NICE guidelines for depression  and schiThe data on prevalence and evidence on effectiveness come from a review of a number of papers, some are systematic reviews and some are individual trials. Where differences in estimates are found between publications, an estimate that represents the consensus has been attempted, using median or mean values depending on the data.Since we have had to use estimates for many of the variables, we have performed a one-way sensitivity analysis ,13 to exThe prevalence of major depression was taken from Kessler  at 6.6%,For increasing the diagnosis of depression, we assumed that we would identify 84% of those previously undiagnosed , allows a population perspective to the estimate of risks and benefits. If an intervention is effective in individuals, but the condition it is used to treat is uncommon in your own population, its impact on the population will be more limited than an alternative intervention with lower effectiveness but which can be used on a larger proportion of the population. The findings here demonstrate the different population impact of interventions for conditions of different prevalence in the population. We see from Tables Another advantage of the use of Population Impact Measures, is that they provide an estimate in a particular population, of going from current to 'best' practice. The numbers are thus dependent on current practice levels and the treatment goals. Where the goals are close to current practice, the potential population gain will be lower than where the gap is large, due either to poor current practice or ambitious goals. The potential impact thus can be personalised to the individual population into which the intervention is to be introduced. The results we present will thus vary according to local settings. Although the data used here come from a UK setting, the method is fully transportable to other populations.Although cost data would be needed to make a final prioritisation decision, we have deliberately left the outcomes to be expressed in the terms in which the data were collected (rather than the generic measures used in cost-effectiveness estimates) so that benefit expressed as real outcomes can be identified. A subsequent step would be to prioritise between interventions, taking costs into account, once the population impact can be observed.continuing drug treatment for depression (82%), based on the non-withdrawal rate from trials in a systematic review[The estimates we have made depend on the availability of relevant data. We have used literature-based estimates, which in themselves vary considerably. For example, adherence to drug treatment for depression varied from 30 to 97% in a number of trials, with a median of 63% . We haveic review. The estic review. The accThe use of Population Impact Measures provides the possibility for a policy-maker to see the impact of a new intervention on the population as a whole and to compare alternative interventions. Our methods are much simpler than those which use generic outcomes such as the QALY ,44,45, aThe author(s) declare that they have no competing interests.RFH conceived the study, RFH and LP reviewed the literature, IG performed the calculations. All were involved in the drafting the manuscript, and have read and approved the final manuscript.


To explore what domains of work are important for job satisfaction among doctors, nurses and auxiliaries and to discuss differences between professional groups in the perspective of micro team culture.Cross-sectional survey data from hospital staff working clinically at inpatient hospital wards in Norway in 2000.Linear regression models predicting job satisfaction for the three professions were compared. First, five domains of hospital work were examined for general job satisfaction. Based on the result of the first regression, five items concerning local leadership were explored in a second regression.A total of 1814 doctors, nurses and auxiliaries working at 11 Norwegian hospitals responded . The only domain of work that significantly predicted high job satisfaction important for all groups was positive evaluation of local leadership. Both steps of analyses suggested that professional development is most important for doctors. For registered nurses, experiencing support and feedback from the nearest superior was the main explanatory variable for job satisfaction. Job satisfaction of auxiliaries was equally predicted by professional development and local leadership. The results are discussed and interpreted as reflections of cultural values, loyalties and motivation.The professional values of medicine, the organizational and holistic skills of nurses and the practical experience of auxiliaries should all be valued in the building of interdependent micro teams. In recent years the link between hospital quality, organizational culture and management has received increasing attention in the United States of America -4 as welJoint Commission Journal on Quality and Safety [Microsystems in health service, as defined by Nelson et al. , are smad Safety -16 discuHigh-performance units, measured by medical records as well as financial reviews, were assessed and nine success characteristics identified: leadership of microsystem, culture of microsystem, interdependence of care team, macro-organizational support of microsystem, patient focus, staff focus, information and information technology, process improvement and performance pattern . The posThe present study focuses on three of the identified success criteria: leadership of microsystem, interdependence of care team and differences in professional cultures in a setting of hospital wards. A strong relationship between quality of care and leadership has been suggested by several authors ,18. Otheartifacts. This is what we see: actions and representations, visible for observers and openly communicated. At the second level he places beliefs and values that include evaluations and judgements. These are not visible but mostly accessible by questionnaires or other methods. The third level of culture, which he calls assumptions, are the deep formatting of mind - values so internalized in individuals and organizations that they are taken for granted.The concept of culture has been disputed for years within social anthropology ,23. A veTo get knowledge about the different levels in Schein's model will require different research methods. Artifacts and values may be directly observed or may be registered as variables in surveys. Deep beliefs and assumptions, however, would ideally require longitudinal ethnographic observations to provide valid knowledge.Culture is produced through shared challenges, discussions and communication over a period of time .Interdependence of the care team is described by Nelson et al. as a culture characterized by trust, collaboration, willingness to help each other, appreciation of complementary roles and a recognition that all contribute to a shared purpose . DependiWe suggest that variables of artefacts, like evaluation of competence, organization of work and leadership, may provide a reflection of deeper assumptions. These reflections may differ between professions. In this article we use data on job satisfaction of three professional groups. The aim of the article is to interpret values and assumptions by analysing which domains of work predict job satisfaction of doctors, nurses and auxiliaries, and to discuss the relevance of these assumptions for micro level culture.A postal survey including all types of personnel at 11 acute somatic hospitals across Norway was performed in 2000. The participating hospitals represented two out of the five university hospitals in Norway, three out of the 12 county hospitals and six local hospitals in the country. All five health regions in Norway were represented.Nurses (three years' college education) and auxiliary nurses  are employed at ward level in Norway. Their immediate leader is a head nurse. Doctors are employed at department level but they usually have their daily work in one particular ward together with the nursing staff. The immediate leader for doctors is most often a specialist or consultant.For the purpose of this article we included in the sample all doctors, nurses and auxiliary nurses working more than 50 % of full time. Names and addresses were obtained through the personnel administration systems of the participating hospitals. Non-respondents received one reminder after two to three weeks. Due to the aims of anonymity of the study, our only information about non-respondents retained after the reminder procedure was profession and hospital.Work experiences were measured by the Work Research and Quality Improvement questionnaire (WORQUA). As one major interest was to investigate organizational issues of hospital work, which is more multifaceted and not easy to transfer from one health care system to another, this questionnaire combined items and scales from established job-satisfaction questionnaires -30 with After a large-scale study in 1998 that included 15 hospitals, the questionnaire was modified to strengthen the dimensions of work organization and professional development before the 2000 study . All iteAccording to our suggestion that cultural values and assumptions may be interpreted from what constitutes job satisfaction, we set up a multiple linear regression model to analyse the relative contributions of five indices on the dependent variable: "All things considered, how satisfied are you with your job?" The five indices were top management, local leadership, competence, work organization and professional development. The dependent variable was measured on a five-point Likert scale ranging from "very dissatisfied" to "very satisfied". Self-reported age was included in the regression as the controlling variable because earlier analyses show that increasing age significantly contributes to job satisfaction . Sex is We expected the three professions to differ in their explanatory models. If the three groups did not differ on the level of indices, we wanted to explore whether there were differences at a more detailed level, by doing a second regression using single items as explanatory variables. Again, separate analyses for each professional group were conducted. Explanatory models are summarized by Adjusted R-squares. Statistical analyses were performed using SPSS 11.5 software for Windows.Of the 2790 nurses, doctors and auxiliaries receiving a questionnaire, 1814 answered after one reminder. The response rates for nursing staff was 66% and for physicians 62%. The number in each profession in our study accounted for approximately 5% of the nurses, doctors and, auxiliaries employed in Norwegian acute somatic hospitals in 2000. The nurses in the study were fairly equally distributed among hospital types: university hospitals (33%), county hospitals (34%) and local hospitals (34%), while the other professions differed between 27% and 45% .For auxiliaries the prime predictor for job satisfaction was working in a culture of professional development. Competence and local leadership also were important for their job satisfaction .A positive evaluation of the local leader was shown to be a significant predictor of job satisfaction for all groups. By exploring this domain alone in more detail, we found that the different professions emphasized different aspects of local leadership. Experiencing support and encouragement from the immediate superior was most important for nurses' job satisfaction Table . ProfessA surgical ward of about 24 beds in a Norwegian hospital may have two to four consultants  permanently attached to the ward, in addition to five to eight doctors in educational positions working at the ward for six to 12 months. All doctors participate in a rota duty within their specialty that includes emergency calls and ambulatory duties. About 20 to 25 nurses and four to six auxiliaries working solely in the ward constitute the nursing staff.Within a ward, working teams consisting of doctors, nurses and auxiliaries care for smaller groups of patients. Shifts and different working hours make such teams unstable, with alternating individuals. Establishing good working teams within the micro unit is a vital challenge for local leaders; managing the cultural diversity of professions is a central part of that challenge ,33. To sThe predictors of job satisfaction of nurses included organizational and professional as well as personal dimensions. The first regression showed the importance of leadership and organization. Focusing on local leadership, the importance of being encouraged and supported by the nearest leader points to a need to be seen and appreciated. Aiken and others found that nurses experience frustration and burnout because of lack of control over work conditions that determine the job for which they are responsible ,35. OtheStudies of nurse burnout and magnet hospitals in the United States concluded that professional development, cooperation with medical staff and managerial support were highly important for nurses ,38. In tThe significant predictors of the nurses' explanatory model included the local leader's knowing the work situation and providing support and feedback. The head nurse is, among other things, responsible for the local education and fostering of new nurses. Professional development might therefore be seen as taken care of through the local leader.Work organization (Index 4) was also an important predictor for nurses' job satisfaction. This is in line with findings that cohesive working relationships, cooperation with medical staff and appropriateness of the system of nursing was important for nurse job satisfaction in the United Kingdom .Nurses seem to be concerned about the vertical as well as horizontal organizational coherence in their work, which may reflect the multidimensionality of nurses' work. Because nurses have a coordinating role, the responsibility for shuttling between professional, organizational and relational tasks makes them utterly aware of organizational gaps and inconsistencies. Nurses inhabit an organizational position from which they overlook the local system's impact on professional competence as well as cooperation and workflow. Their system competence is an important asset in the micro team and should be used by managers.Auxiliaries are the group with the least professional authority. In our data 98% of the auxiliaries were female, 75% were above 40 years of age and 60% worked part-time. They are however, also the most stable group in hospital wards, thus representing long experience and considerable informal knowledge about their local patient groups as well as the hospital organization.It may seem surprising that professional development and working in a unit with high competence is important for their job satisfaction, while professional feedback is not a significant predictor. One interpretation is that auxiliaries draw their professional identity and loyalty more from the collective of the micro unit than from their own professional group. Having little chance of formal promotion, their prospects for professional acknowledgement and respect lie in building informal competence and local reputation. For the micro team these kinds of local skills and loyalties are vital. In building an interdependent care team, a leader's task should be to develop the collective aspect of the ward's total situational competence and hence to make the other professional groups acknowledge this resource as an asset.The strongest predictor of doctors' job satisfaction was working in a culture of professional development. Having a leader who knows the work situation and gives feedback on the work was also seen as important. The nearest superior for doctors is usually a consultant or a clinical manager. Knowing the work situation means understanding the difficulty and variation of clinical assessment and having the capacity to give professional feedback. The most striking cultural trait emerging from the doctors' results is the noticeable importance of the profession. The doctors' explanatory model reflects loyalties, authorities and motivation linked to the medical profession only. Studying trends in the doctor – manager relationships in the United Kingdom, Davies et al. support the picture of doctors' loyalty: Doctors reproduce a professional and individualistic authority ,41. In tA common strategy for bridging the gap between managerial and clinical rationalities in hospitals has been to train doctors and nurses in managerial theory and methods. The undesirable side effects of losing clinical responsibility and caring morale integrated in the clinical cultures have hardly been discussed ,43. A suBy exploring what predicted their job satisfaction, we have shed some light upon the values and the underlying assumptions of the different clinical professions at ward level. Such insights may be used as motivation through differing strategies tailored for each profession. Some crucial points may be to strengthen confidence and motivation at the local level by rewarding doctors for their professionalism but also urging them to share their knowledge and to expand their professional responsibility to the micro team as a whole. Nurses and auxiliaries could be used systematically as informants on the functioning of the micro- and meso-level of the hospital infrastructure. The coordinating role of nurses and the stability and local experience of auxiliaries give them an organizational overview that obviously is useful in quality improvement strategies.The response rate of the study was < 70%, which may be considered low. As our sample was made unidentifiable after the inclusion procedure, our knowledge about non-respondents is limited. All regions and hospital sizes in Norway were represented, however, with all three professions in the study; there is no reason to believe that our sample was in any way biased.The use of identical questions to different professions risks differing interpretations from the various groups. This may represent a validity problem, but on the other hand it also allows direct comparison between professional groups. The fact that gender still is closely linked to professions represents some interpretation problems. We cannot say that the values described are rooted in professions and not in gender. On a group level, though, we may state that professionalism is a cultural trait of hospital physicians.Hospital staff work within hospitals, departments and wards and thus share experiences and attitudes with their local colleagues. This would lead to underestimated standard errors and thus an inflated type I error rate. As we were not able to link all professional groups to ward levels of work, which is the most likely level of clustering, multilevel analysis could not be performed.To interpret a survey of work experiences as cultural information requires caution. We do not recommend the method for cultural studies as such. We do, however, defend the possibility of searching such material to identify characteristics reflecting cultural main vectors at a relatively basic level. In a setting of managing quality improvement, this level might be sufficient for practical purposes.The author(s) declare that they have no competing interests.Unni Krogstad was responsible for the survey and the design of the paper; she wrote the first draft of the article. Dag Hofoss and Marijke Veenstra participated in the statistical analysis and revisions of the article. Per Hjortdahl contributed ideas throughout the process and to revisions of the paper.


Governments world wide are increasingly demanding outcome measures to evaluate research investment. Health and medical research outputs can be considered as gains in knowledge, wealth and health. Measurement of the impacts of research on health are difficult, particularly within the time frames of granting bodies. Thus evaluations often measure what can be measured, rather than what should be measured. Traditional academic metrics are insufficient to demonstrate societal benefit from public investment in health research. New approaches that consider all the benefits of research are needed. Health and medical research in Australia received a very welcome boost in investment in the latest Commonwealth budget. The use of the word investment reflects the growing literature on the substantial economic returns to society from health and medical research (HMR). This is sometimes described as research 'payback' . In 2003The Macroeconomic Commission on Health  concludeThis notwithstanding, governments world wide are increasingly asking for outcome measures in the evaluation of research investment and for the justification of increased investment. Doing it well is no longer enough, and funding bodies are increasingly looking to objective ways of assessing the value of research.In Australia, the Federal Government is planning to introduce a Research Quality Framework (RQF) to measure both the quality and impact of research undertaken within universities. As the detail of the RQF is worked through over coming months much attention will be paid to the measures of research impact .With this in mind, how should the value of health and medical research best be measured?The outputs of health and medical research are often considered under the headings of knowledge gain, wealth gain, and health gain -8, and iKnowledge gain is the least difficult to assess, albeit still problematic. This is because the outputs  are more proximate to the research performance and can be quantified, with variable precision, by a variety of markers, in particular by bibliometrics. Thus numbers of papers, impact factors of journals, and citations are all widely used. A number of scholarly analyses have addressed the strengths, weaknesses and pitfalls of such approaches but they are relatively robust, at least within field for aggregated data. More recently new tools have been developed for bibliometric assessment of individual researchers, specifically the Hirsch index and its derivatives. Often research income is used as a surrogate, as judgments on funding usually rely on some measure of outcome. These measures, and others, as peer recognition, prizes, named lectures etc, all correlate to a degree .th century, both in relief of suffering  and cost savings , was published locally [But the measures given most weight by granting bodies, particularly journal impact factor, may not relate well to the ultimate goal of health and medical research, that is, health outcomes. John Cade's discovery of lithium for treatment of manic-depressive illness, unquestionably one of the greatest findings of the 20 locally  as were  locally ,11.inter alia by citations, but in aggregate, so that they say little about individual research papers. Anecdotally some researchers claim that really original work, which overturns conventional paradigms, is not accepted by 'first rank' journals and finds its way into less prestigious publications. For example, Parish's first paper on heparanase which showed that sulfated sugars could be used to inhibit tumour metastasis by blocking heparanase action was published in 1987 in the International Journal of Cancer (impact factor ~ 3) [Journal impact factors are determined tor ~ 3) . The papJournal of Immunological Methods (impact factor ~ 1.9) in 1994 [Google search suggests that between 5,000 and 10,000 papers have been published in which the procedure is used.The use of CFSE, a fluorescent dye developed for studying cell migration and proliferation, was published in  in 1994 . The appThe European Journal of Hand Surgery doubled its impact factor through an editorial review on the vagaries of citations which quoted its own papers [n papers .Wealth generation is more difficult to measure. A range of methodologic approaches to economic assessment of health research outcome have been described both nationally and internationally. These include valuing the direct cost savings from application of research findings, the economic value of a healthy workforce from improved healthcare, economic gain from new products and technologies, and some measurement of social health gain by placing a monetary value on healthy life . This rePatents and formation of spin-off companies are widely used, but are only surrogates. Patents often come to nothing and many spin-offs fail. Given the significant lag time from development to commercialization for devices and diagnostics, and the very long time for therapeutic and preventive agents, wealth generation often occurs years or decades after the original research, and well outside the time frame of political decision making, or even of granting bodies.In the health context one might ask whether a more realistic measure of wealth would identify some metric for linking savings to the health system from particular discoveries. For example, Kirschner and colleagues reported that the discovery of lithium treatment for manic-depressive disorders had saved the USA over $145 billion in hospital costs alone . ExtrapoAnother legitimate financial measure would be qualification of the contribution to economic activity from disease prevention through effective public health programs. Countries which have managed to reduce rates of tobacco consumption have, among other benefits, improved workforce productivity through reduced absenteeism.At present there is no accepted way of including impacts of these measures in a research assessment framework.Even more vexed, but undeniably most important, is the question of measuring health outcomes as a product of research. Given that lag times for research into practice can be very long, how is it possible to measure outcomes of investments made in time frames which match those of policy makers or granting bodies?Measures that might be used include incorporation of research into clinical guidelines or systematic reviews of best practice, generation of such guidelines or systematic reviews, or contributions to reports  which inform health policy or practice. But it is hard to reduce these to metrics, and qualitative rather than quantitative approaches are needed.Similarly, the contribution to health gain of research in health services and health systems and policy are difficult to measure. For example, the improvement in health outcomes for stroke victims treated in dedicated stroke clinics is well documented but system take-up has been poor . In one A linear approach to measuring impact that attempts to take a particular research finding and identify its health impacts will not meet most situations. In most areas, such as health services and public health, the potential for system impact will only be obvious after analysis of several studies, such as a Cochrane review, and the impetus for policy or system change might only come after governments have been persuaded of the benefits of reallocating health resources in the appropriate way. In such a scenario which piece of research legitimately claims the impact: the original study/ies? The Cochrane review? The policy 'translational' work? If all can have some claim, how is the impact 'quotient' fairly divided among them?Traditional academic metrics of research output through peer-reviewed publications and citations are insufficient to satisfy society's expectation that public investment in research results in real benefit to the society. This is particularly the case for HMR. An approach that takes into account all the benefits of research outcomes, including the freeing up of resources from savings on treatment and other costs, needs to be taken. This approach will require new metrics which are understood and accepted by society and the governments which represent it. It is no longer enough to measure what we can – we need to measure what matters.The author(s) declare that they have no competing interests.Both authors contributed equally to the conception of this paper, drafting the manuscript, revising it critically for important intellectual content. Both authors have read and approved the final manuscript.


Scientists are making strides every day in breast cancer research through the use of mouse models in the lab. These investigations offer a better understanding of the cell processes that lead to development of the disease. Translating the results of this research into information that can be understood and used by a lay audience can be difficult, but the Community Outreach and Translation Core (COTC) of the NIEHS-funded Bay Area Breast Cancer and the Environment Research Center (BABCERC) has created a DVD for the public that offers simple, understandable descriptions and updates on various mouse models being used in breast cancer research.Of Mice and Women: Modeling Breast Cancer and the Environment is a 35-minute information session designed for a range of audience members from high school science students to breast cancer advocates. The DVD was originally created for distribution to the other three NIEHS-funded breast cancer centers to help teach communities about the research process. That way, when study results are published, the average community member can put them into an understandable context.Based on a presentation developed by Mary Helen Barcellos-Hoff, a senior scientist at the Lawrence Berkeley National Laboratory and a BABCERC co-investigator, and produced by Michael Hoff Productions, the DVD The video describes why mouse models are ideal for this particular field of research. For example, Barcellos-Hoff notes that mice and humans have similar mammary gland development and structure. This commonality allows investigators to manipulate rodent genes in search of answers to questions regarding the mechanisms behind breast cancer development, including analysis of the effects of environmental exposures. The video also presents information on models such as protein mapping. Study of specific functions at specific protein locations in mouse cells gives researchers insight into which genetic mutations lead to the development of breast cancer.http://www.zerobreastcancer.org/ or e-mailjaniceb@zerobreastcancer.org.The COTC has also produced an accompanying scientific glossary that summarizes the information discussed in the DVD and defines terms used in the field of breast cancer research. The free DVD has become a popular educational tool used for a variety of audiences around the country, says Janice Barlow, executive director of Zero Breast Cancer, the nonprofit organization that leads the COTC. Its message may soon be delivered to more. According to Barlow, a California television station has expressed interest in broadcasting it, and there are plans to produce more copies. Above all, Barlow states that the ultimate goal in supporting research such as that featured in the DVD is to help ensure that desired health benefits and outcomes are achieved—namely, prevention of breast cancer, a decrease in breast cancer incidence and mortality, and a reduction of environmental exposures in our communities. For more information on the DVD, visit


Renewed political and commercial interest in the resources of the Arctic, the reduction in the extent and thickness of sea ice, and the recent failings that led to the Deepwater Horizon oil spill, have prompted industry and its regulatory agencies, governments, local communities and NGOs to look at all aspects of Arctic oil spill countermeasures with fresh eyes. This paper provides an overview of present oil spill response capabilities and technologies for ice-covered waters, as well as under potential future conditions driven by a changing climate. Though not an exhaustive review, we provide the key research results for oil spill response from knowledge accumulated over many decades, including significant review papers that have been prepared as well as results from recent laboratory tests, field programmes and modelling work. The three main areas covered by the review are as follows: oil weathering and modelling; oil detection and monitoring; and oil spill response techniques. Politics, economics and climate change are the driving forces behind the ‘industrialisation’ of the Arctic marine environment. Which of these three forces are more dominant is far from certain, but what is clear is that any increase in human activity in ice-covered waters will magnify the potential for an oil spill. Whether it be from a shipping accident, leak from a subsurface pipeline, subsurface well blowout or a cruise ship venturing further into shallow waters.Whilst we have seen a substantial increase in Arctic fisheries and tourism, the recent slump in world oil prices combined with the need to reduce our carbon footprint in line with the legally binding Paris Climate Agreement has potentially reduced the attractiveness of investment in the Arctic. For instance, a number of major oil companies have announced the abandonment or suspension of their drilling operations in the Arctic Ocean, and trans-Arctic shipping remains at low levels. However, operations do continue, for example the newly built offshore terminals in Kara Sea region of the Russian Arctic handled (by ship) a combined 230 000 barrels a day in the second quarter of 2016 Lee . Whilst In common with oil spill response in temperate, open-ocean conditions, the purpose of conducting any oil spill response in ice-covered waters is to reduce the damage that the spilled oil might cause, both ecological and socio-economic. Knowledge of which socio-economic, ecological or cultural resources are likely to be damaged by the spilled oil at any particular location is important, so that the appropriate response strategies and methods can be used to minimise the damage that could occur. For example, cultural resource protection (e.g. remaining “secret”) requires knowledgeable representatives to participate in Shoreline Cleanup Assessment Technique (SCAT) and response operations to prevent direct oiling, accidental disturbance, illicit collecting or intentional site disturbance ; andCoastal Response Research Center’s (University of New Hampshire) review of the state-of-science for dispersant use in Arctic waters  with 40 reports, focuses on different aspects of an oil spill in the Arctic marine environment. There have been notable experimental and unplanned oil releases in the Arctic: the Dome Petroleum Experiment (1979/1980)  into Arctic oil spills; these include the following:This paper brings together knowledge that has been amassed over many decades, including the significant review papers mentioned above, as well as more recent laboratory tests, field programmes and modelling work. The three main areas covered by the review are as follows: (1) Weathering and modelling, (2) Oil detection and monitoring and (3) Oil Spill Response Techniques. We understand that there are omissions that given space restrictions we could not include. These include amongst others: biodegradation, effects of oil on Arctic ecosystems, infrastructure needs, logistics, training and education, indigenous communities perspectives and representation, chain of command/coordination, and the ethics, regulatory and international framework encompassing a potential Arctic oil spill.Depending on the season, the sea ice conditions at the time of the event and type of accident , oil could be spilled on, under, or into the waters surrounding the sea ice. However, what makes an Arctic oil spill particularly challenging is the plethora of environmental scenarios that could play out and the speed in which ice conditions can change. Furthermore, the combination of natural variability and climate-forced changes in the Arctic marine system make it particularly challenging to predict the ice conditions from one year to the next. Even though a spill could happen at any time of the year, it is important to keep in mind that most Arctic marine activities, at present, are concentrated around the summer months, and generally avoid sea ice. This summer focus may change as operational experience is gained; infrastructure is enhanced, and the continued increase in the ice-free season, over the next 30 years and more, stretches into other seasons.Oil spilled on a calm ocean surface spreads into a slick due to the balance between the forces of gravity, viscosity and surface tension. In rougher water, this spreading is augmented significantly by the entrainment of oil droplets into the water column by breaking waves, and subsequent resurfacing. The trajectory or drift of the slick is governed by the forces associated with currents, winds and waves  showed that an oil pool oil, spilled under 160-cm-thick ice, took about a week for ice to begin to encapsulate the oil Lewis . The encAccidental oil spills in the marine environment, whilst rare, are not an unknown phenomenon, and as such the oil spill modelling community is well versed in the fate of oil in more temperate environments. Complex models have been developed that use oceanographic, atmospheric, and weathering data to determine both the trajectory and fate of an oil spill in the open ocean. It is generally accepted that these complex models are well established and do a reasonable job, however similar modelling scenarios in the presence of sea ice are much more uncertain  in the USA and the European Space Agency (ESA). This has made it easier to research into new methods of detecting particular surface types, making algorithms available for the operational community to start producing services covering areas of interest to the community in general. In Europe, the Global Monitoring for Environment and Security (GMES) programme, sponsored first by ESA and now taken up by the European Commission (EC) under the name “Copernicus”, has led to the development of new services with the Sentinel series of satellites.Remote sensing from satellites can play a role, both in the continuous monitoring of spills, and in aiding the response efforts. Whilst remote sensing of sea ice and the detection of oil spills using satellites are both widely studied, there has been little work to investigate the two in combination. A particularly tricky problem for the Arctic is how to detect an oil spill if it is from an unknown incident or illegal activity such as discharge.Both optical and active microwave sensors have been the focus of some preliminary studies on oil spill in sea ice detection. While optical sensors have shown some success in detecting oil spills on top of the sea ice, their use in routine monitoring is limited by the clouds and darkness (polar night) prevalent in Polar Regions. Active microwave in the form of imaging Synthetic Aperture Radar (SAR) is the preferred approach for the Polar Regions as it can see through clouds, and is not influenced by the presence or absence of sunlight. Although SAR is proven in detecting oil spills in open water, its use for detecting oil spills in ice-covered waters requires more research. An in depth investigation of polarimetric SAR techniques using the new generation of satellite sensors that are now becoming routinely available could be particularly valuable.SAR is capable of mapping objects down to a few metres size, and can be targeted on the site to provide monitoring of individual ice floes and possible spill mapping. In May 2009, a combination of industry and research partners undertook fieldwork off eastern Svalbard as part of a Joint Industry Program (JIP) project on oil spill contingency for Arctic and ice-covered waters  are beginning to become more widely available and their ease of deployment, once regulatory hurdles are overcome, may in future make it possible for them to take on more oil spill response work , are good at detecting ice features such as ice edges and ridges and have been proven in open water oil spill detection. In Norway, the Norwegian Clean Seas Association for Operating Companies (NOFO) has 14 ship-based radar systems, produced by MIROS2 GHz, arBuoys are not normally used to carry sensors that can detect oil, although systems are being developed through the EU funded GRACE programme.The remote sensing of oil using on or near ice technologies is limited to a handful of systems, the most positive being penetrating radar (GPR), and dogs. On-ice systems offer very limited area coverage and are time consuming. Safety aspects of having personnel on ice are also a consideration.GPR systems operate in the 500 MHz to 1 GHz frequency range. Whether that can penetrate all the way through the sea ice is dependent on ice thickness, the temperature of the ice, and the distribution of brine within the ice. Studies have shown that GPR can detect oil layers of about 1–3 cm thickness, on ice but buried beneath snow, and trapped in or under relatively smooth ice , Autonomous Underwater Vehicles (AUVs) and ocean gliders), in the Polar Regions has a long history. However, it is only in the past decade or so that technology has advanced to a state where ROVs, AUVs, and ocean gliders are a practical proposition for under-ice remote sensing  a sea ice cover also increases. The detection of oil under sea ice is a difficult task, but investment and research in this field have delivered a number of sensor technologies that have the potential to detect and map oil under or within sea ice  mechanical recovery with booms and skimmers, (ii) dispersant use and (iii) in situ burning. Each of these methods has particular capabilities and limitations that make it more or less suitable for responding to specific oil spill situations. The methods that would be feasible or effective for spills of oil in ice-covered waters vary depending on the seasonal ice and other conditions. The behaviour of oil spilled in cold, ice-covered waters is governed largely by the ice concentration in the case of broken ice and the process of encapsulation and subsequent vertical migration in the case of solid ice. For example, if oil is spilled under ice in the spring (after May), the oil might not become encapsulated in the ice due to insufficient new ice growth before seasonal melting commenced  the Mechanical Oil Recovery in Ice-Infested Waters (MORICE) project . Dispersing the oil as very small droplets in the water column enables naturally occurring hydrocarbon-degrading microorganisms to substantially biodegrade the oil, leaving a small proportion of recalcitrant residue  (IPIECA-IOGP 2015) . This process assesses the relative impact mitigation potential of candidate response options, in order to choose those that will most effectively minimise the overall consequences of a spill.Dispersant use could be a response to oil on the sea surface amongst broken ice. The dispersant would need to be sprayed onto the oil to achieve the recommended treatment rate of around a DOR (Dispersant to Oil Ratio) of 1:25. The mixing energy to cause initial dispersion of the dispersant-treated oil is normally provided by breaking wave action in the open sea, but this will be limited in the presence of ice because ice dampens the waves. The mixing in the upper layer of the water column that dilutes the dispersed oil will be less rapid when ice is present. Additional mixing energy supplied using ship’s thrusters might be required  to medium time (1–10 years) time frame?Do we have the capability to compare with reasonable accuracy the potential risks and potential benefits of Arctic oil exploration/production to support evidence-based decision making?There are undoubtedly some gaps in our knowledge-base, which only can be filled through research, technological development, operational testing and refinement. We need to eliminate the cycle whereby intense periods of research are interspersed by long periods of much less activity. This approach can be detrimental as institutional memories begin to fade, built-up expertise can be lost, older experts retire and training of new experts is sporadic. This feast or famine approach should be replaced by long-term, strategic investment. Recently, this more sustainable approach to Arctic oils spills seems to have received some traction as we have seen focused investment in basic research by industry through the Joint Industry programme,the Arctic Environmental Test Basin (AETB) at the Hamburg Ship Model Basin (HSVA),the Cold Regions Research and Engineering Laboratory in the US,the Churchill Marine Observatory in Canada, which is a major facility for the study of detection, fate, effects and mitigation of oil spills in ice-covered waters,The SINTEF Ocean AS climate rooms with laboratory and meso-scale experimental facilities.Whilst structured laboratory experiments are vital in further developing our understandings of key processes, detection methods, and equipment testing, controlled oil release field trials are essential to achieve and maintain a credible state of readiness. At present, most government regulations prohibit the controlled release of oil, but occasionally permits are issued for research purposes.We have shown that understanding the impact, response, and potential consequences of an oil spill in the Arctic marine environment is both a research and operational challenge that requires expertise from a wide spectrum of individual specialties. It is also clear that the extreme range of environmental conditions that can be present in the Arctic marine environment poses a challenge to any oil spill response. The past 40 years of Arctic oil spill research have provided a solid baseline knowledge regarding the fate of oil, as well as an understanding of the operational solutions and techniques needed to detect and recover the oil. Whilst our knowledge-base is broad one can legitimately ask:Oil release experiments, particularly in the Arctic, need to be carefully planned and permitted so that the maximum amount of science can be performed, whilst minimising environmental effects. These well thought out controlled spills need to be designed for a wide range of ice conditions, oil types, and spill scenarios. Progress requires equal partnership with a diverse range of stakeholders, including local communities. In addition to these controlled ‘real world’ experiments other strategies need to be developed whereby we can semi-regularly perform quantifiable stress tests on different aspects of Arctic oil spill contingency planning, associated decision support systems, and the system as a whole.At the current prevailing oil price, oil exploration and production in Arctic waters remain at low levels, as does Arctic shipping; however, the consequences of an accidental spill of oil (crude or processed) into the Arctic marine environment could be severe. Given these low levels of oil and shipping activity, we have a ‘window of opportunity’ to develop more robust solutions and protocols to meet the above-mentioned challenges. This needed research, and planning is not inexpensive, and will need continuous evaluation and refinement as science, data access and technology improve. No one can guarantee an accidental spill cannot happen, and no response method is risk free or completely effective. Therefore, to reduce the likelihood of an accident and accompanying consequences, we must have a comprehensive understanding of the issues involved, ensure best practices are followed, have a robust risk management framework, and have a responsive decision-making structure is in place.There are significant differences between oil spill response capabilities in open water and in ice-covered waters. Significant challenges also exist for spills occurring within different sea ice types, concentrations and seasons, all of which are strongly impacted by climate change. There is no getting away from the fact that the ice-covered regions are complex. With the renewed interest in the Arctic and with the pace of activity in the marine environment increasing, we cannot be complacent regarding oil spills in the Arctic marine environment.Whilst it has long been recognised that the Arctic marine environment represents one of the most challenging areas in the world in which to work, a wealth of technical and operational expertise, experience, and know-how has been developed within industry, government, and academia. This knowledge also extends to oil spills in ice-covered seas. Nevertheless, there are deficiencies in our understanding that need to be addressed so that these gaps can be bridged and solutions found.In order to comprehend fully our level of understanding and readiness to deal with an Arctic oil spill, field exercises that encompass a broad spectrum of sea ice, ocean and meteorological conditions will be necessary. Whilst a handful of controlled oil spills experiments have occurred in sea ice the past, new developments and techniques suggest that further controlled field trials are needed to evaluate and improve oil spill response capabilities and technologies. These exercises, whilst very challenging, should be encased within the realities of the climate-driven changes within the region.While the long-term goal is to reduce our reliance on hydrocarbons, and thus reduce our global carbon footprint in line with the legally binding Paris Climate Agreement we must be ready to deal with an accidental spill in the Arctic now. There is urgency to this readiness evaluation as exploration, shipping, and in some instances production, in ice-infested waters are well advanced.A strong regulatory framework will provide the clarity industry needs, and ensure that best available practices are always followed right across the sector and the region. The recent Agreement on ‘Cooperation on Marine Oil Pollution Preparedness and Response in the Arctic’ by the Arctic Council


Insect-borne pathogens impose a substantial burden on health, the environment, and agricultural production, and rapid outbreaks of such pathogens are becoming more common e.g., ,2. PopulAn emerging technology that could form part of strategies for controlling insect-borne pathogens is to suppress or replace insect populations by releasing genetically modified (GM) insects. Trial releases of GM insects for disease control have now been conducted in the Caribbean , MalaysiOne reason behind geographical differences in the assessment process is probably that there are no truly global guidelines. The Cartagena Protocol  establisRegional differences also reflect public opinion. The history of field releases of sterilised disease vectors and GMOs illustrates the importance of effective dialogue with the public. In the 1970s, public concerns about releases conducted by the WHO Research Unit on Genetic Control of Mosquitoes in New Delhi were a major factor in the project’s cancellation . More rePart of the solution could be providing more information to the public. Low public awareness of the New Delhi project is thought to have contributed to opposition , and theIt is also important that communication is two-way. The “knowledge deficit” model of science communication is increasingly recognised as outdated , partly Given the lessons learned so far, we suggest that the following three areas represent the best compromise between proportionate risk assessment and maximising the potential benefits of GM.Wolbachia [Firstly, assessments should regulate product, not process. Currently, the EU regulates by process, meaning that mosquitoes rendered sterile by radiation, genetic modification, or the addition of symbiotic bacteria such as olbachia ,39 wouldSecondly, assessments should consider the balance of risk and benefit resulting from GM insect release, not risk alone. Currently, the EU only considers risk (as discussed above), preventing applicants from providing information on the benefits of the proposed technology . Risk asFinally, the process of assessment needs to be more transparent. This would allow both beneficiaries and stakeholders to take part in better-informed discussions of proposed products and the results of trial studies, encourage consistent assessment by authorities across multiple regions, and make it clear to the public that assessments receive expert scrutiny, building confidence in the process. At the same time, we recognise that requiring full disclosure of methods and data at the time of application would strongly discourage innovation. Possible compromises could be to only release data from successful assessments, to embargo these data for a period after the assessment is approved, and/or to consider some form of market exclusivity for a period in return for such disclosure.Issues relating to the optimal balance between risk and benefit for the use of GM insects for disease control are not going away. New technologies are making it easier and faster to develop GM insects . Althoug


Despite the close link between tuberculosis (TB) and poverty, most mathematical models of TB have not addressed underlying social and structural determinants.To review studies employing mathematical modelling to evaluate the epidemiological impact of the structural determinants of TB.We systematically searched PubMed and personal libraries to identify eligible articles. We extracted data on the modelling techniques employed, research question, types of structural determinants modelled and setting.From 232 records identified, we included eight articles published between 2008 and 2015; six employed population-based dynamic TB transmission models and two non-dynamic analytic models. Seven studies focused on proximal TB determinants , and one focused on macro-economic influences.Few modelling studies have attempted to evaluate structural determinants of TB, resulting in key knowledge gaps. Despite the challenges of modelling such a complex system, models must broaden their scope to remain useful for policy making. Given the intersectoral nature of the interrelations between structural determinants and TB outcomes, this work will require multidisciplinary collaborations. A useful starting point would be to focus on developing relatively simple models that can strengthen our knowledge regarding the potential effect of the structural determinants on TB outcomes. TUBERCULOSIS (TB) is widely recognised as a disease of poverty,5The targets and indicators of this new TB action framework are anchored in the 17 Sustainable Development Goals (SDGs) adopted by the United Nations and which mark the global development agenda that began on 1 January 2016. By placing their emphasis on the interdependence and synergies between socio-economic development and health,In the present article, we follow the definition of SDs of health of the WHO Commission on Social Determinants of Health:Quantitative analytical tools such as mathematical modelling can play an important role in informing the End TB Strategy, evaluating the impact of novel poverty-reduction interventions nested in its vision , and exploring the contribution of socioeconomic drivers to the epidemic. However, to do so, TB models will inevitably need to expand their focus beyond diagnosis and treatment to incorporate SDs, but the potential of modelling as well as its main limitations in supporting this research agenda remain unclear.In the present paper, we report findings from a systematic review of the literature carried out with the aim to provide an overview of the current state of knowledge in the mathematical modelling of SDs of TB. We then go on to discuss key methodological challenges and gaps in empirical evidence that existing mathematical models need to overcome to be able to incorporate SDs to remain relevant to policy-making.For the purposes of this review, ‘mathematical model’ was defined based on that envisaged by Garnett et al. as a mechanistic representation of how disease burden is established. This includes both dynamic transmission and decision (non-dynamic) analytic models. We searched PubMed for any relevant article on modelling and the socio-economic determinants of TB .We excluded systematic reviews, epidemiological studies that did not use mathematical modelling techniques and ecological analyses looking at the SDs of TB. The search focused on socio-economic factors , and excluded studies focusing only on diabetes mellitus (DM), the human immunodeficiency virus (HIV) and behavioural risk factors such as alcohol consumption and smoking unless their association with socio-economic factors were also considered. We applied no restrictions as to the year.Additional relevant articles were identified in the authors' personal libraries and are included in the review. DP selected the articles with support from RMGJH, DB and KL; data were extracted by DP and RMGJH.A total of 229 unique records were found in the literature search, and four additional articles were added from the authors' personal libraries. Of these, 54 underwent full-text evaluation. After full-text screening, we included eight articles published between 2008 and 2015, with four articles published in 2015 only. 12–19The study by Reeves et al. was the only article that looked at the impact of distal determinants, i.e., government expenditure per capita on public health services, gross domestic product (GDP) and cumulative decline in GDP, as a measure of the severity of an economic recession on TB control.Compartmental population-based dynamic TB transmission models were the most common simulation approach employed in the selected articles ; two studies used non-dynamic analytical models and both investigated the effect of both DM and nutritional status on TB epidemics. Most studies included a conceptual framework to illustrate the mechanics of the models and the hypotheses behind their research questions.Transmission models employed standard SLIR (susceptible-latent-infectious-recovered) models that were adapted to explore the research question set in each study: the model by Oxlade et al., for example, was stratified by levels of undernutrition by wealth quartile.13*With regard to model parameters, Ackley et al. explored changes in differences in susceptibility to infection and progression to disease in hypothetical scenarios.17Data on the different exposures were mainly drawn from the literature,The studies in our review support the notion that TB control is linked to and would benefit from action on TB social determinants. Reeves et al. found that a decrease in funding to control TB due to an economic recession  can lead to a decline in TB case detection, and consequently to higher TB rates.19This review has highlighted the paucity of mathematical modelling studies looking at the effects of socio-economic factors on TB pathogenesis and epidemiology, but has also shown that, although fairly recent, work in this field seems to be growing as the number of articles published has increased from 2011 onwards. This is possibly a reflection of changing policy priorities that are now part of the End TB Strategy.Our findings point to the need, at this stage, to develop relatively simple models that improve and expand the current body of work to incorporate available evidence and strengthen our knowledge of the potential effect of SDs on TB outcomes. For instance, most models focused on one or two factors only, and those that considered two factors did not account for possible interactions between them. It is to be noted that most mathematical modelling studies focussed on assessing the effect of nutritional status and changes in BMI on TB epidemiology. This is not surprising, as undernutrition has long been acknowledged as a key socially determined TB risk factor. We found no modelling work looking at the impact of improved socio-economic macro-indicators on TB outcomes, or of social protection interventions targeting TB patients and their households. With respect to proximal risk factors, only one model assessed the effect of crowding on TB epidemiology, possibly a reflection of the fact that data on crowding and TB are not rich enough to unpick causality for a model.The narrow focus of past global health and development policies and TB control strategies only partly explains why TB modelling has so far shown some reluctance to include SDs. This has also been due to the real and perceived weaknesses in the empirical evidence needed to populate models and quantify the pathways from socio-economic factors to changes in the natural history of TB in a population. While there are some data to inform parts of, for example, the pathway from macro-economic policies  to TB incidence,24When translating the effect of changing a socioeconomic determinant into a mechanistic model, it does not suffice to have an estimate of the magnitude of the effect  are affected at different stages of the pathway. In addition, any interventions that reduce barriers to care and treatment completion will change model parameters capturing the time to diagnosis as well as retention in care .In addition to effects on incidence, SDs may alter the natural history of disease  or disease outcomes . Clustering of these risk factors for behavioural or biological reasons requires an understanding of their interactions, and further increases the level of knowledge required. Finally, separating out composite phenomenological quantities into their mechanistic components may also improve transferability between settings if the data needed to quantify how these components differ are available.Mathematical modelling is a powerful and flexible tool to inform policy discussions and estimate the potential impact of various interventions relative to one another.As shown in this article, there exists a scarcity of TB models that include SDs, but also a small but increasing body of work that has explored initial ideas. Some modelling of proximal risk factors and related public health interventions has been done but, for example, this has never moved upstream. TB models can leverage the existing data, and highlight the value of collecting those that are missing, such as the exact link between changes in nutritional status and changes in progression to disease, or the relationship between transmission intensity and living environments .To further our knowledge, projects are urgently needed that advance the field while avoiding the pitfall of developing overly complex models that include population or pathway structures not adequately supported by empirical evidence or fully understood. In addition, the complexity of the pathways involved and the multisectoral nature of new approaches to end TB evidently require collaborations from different disciplines, including social scientists, epidemiologists, economists, policy makers as well as mathematical modellers.In this article, we highlighted that the literature on mathematical modelling of social determinants of TB remains limited. We argue that to maintain its key role in policy discussions in the era of the SDGs and End TB Strategy, the TB modelling community needs to embrace the technical challenges to adequately represent the interplay between TB and its socioeconomic drivers. While some work is underway, more funding, data and capacity are urgently needed to ensure TB modelling remains a useful tool for the ultimate goal of TB elimination.