The Normacog Brief Battery (NBB) provides a comprehensive overview of an individual’s cognitive functioning within a short amount of time. It was originally developed for the Spanish population in Spain. However, there is a considerable need for brief batteries in clinical neuropsychological assessment, especially in eastern European countries. Cultural background and other individual characteristics—such as age, level of education, and sex—are shown to influence both cognition and patients’ performance on neuropsychological tests. Therefore, it is important to develop understanding of how and why culture impacts on cognitive testing and determine which sociodemographic variables affect cognitive performance. The current study aims to translate, adapt, and standardize the NBB in Bulgaria, Croatia, and the Netherlands, and to analyze the effect of sex, age, and education level on cognitive performance between these three countries. This brief battery assesses eleven cognitive domains, including those most currently relevant in cognition such as premorbid intelligence, attention, executive function, processing speed, and memory. The translation and adaptation of the battery for different cultures will be done using the back-translation process. After exclusion criteria, the current study will include a total sample of 300 participants (≥18 years old). The samples of 100 participants per country will be balanced through the consideration of their age and level of education. Effects of the sociodemographic variables  on cognitive performance are expected. Furthermore, this relationship is expected to differ across countries. A multivariate hierarchical linear regression will be used and exploratory analysis will be carried out to investigate further effects. The results will be particularly valuable for future research and assessment in cognitive performance. The growing demand for accurate and fast neuropsychological assessment shows the importance of creating a universal brief assessment tool for wider cross-cultural application. Neuropsychological assessment is a performance-based method that is used to obtain information about a person’s cognitive functioning in different domains, such as memory, attention, processing speed, executive functions, spatial, and language functions . A neuroRegarding the influence of age on cognitive performance, Considering education level, it has been shown that people with higher education perform better on neuropsychological tests than those from lower educational groups. This effect is most prominent in verbal neuropsychological tests . AlthougAnother variable connected with cognitive performance is the participant’s sex. However, sex differences vary in magnitude across countries . For insin vivo than any other brain region, which makes them more susceptible to environmental factors that covary with socioeconomic status. Another influential factor is individual professional activity. The literature suggests that the longer the period an individual has not been professionally active, the greater their decline in cognitive functioning is . However, considering the state of current scientific literature, it is relevant to analyze not only cultural differences between the countries investigated, but also the effects of sex, age, and level of education on cognitive performance. More precisely, it is of interest whether there is an interaction between sex and age and if this interaction is influenced by the level of education of the participants, when controlled for sociodemographic covariates.Therefore, the goals of the study are specified as follows: Firstly, the current project aims to translate and adapt the NBB  in BulgaA structured interview was designed to collect the sociodemographic characteristics from participants and to make an informed decision regarding inclusion and exclusion criteria. The Hollingshead four-factor index of socioeconomic status (SES) will be used to measure each participant’s SES based on four domains: marital status, retirement/employment status, educational attainment, and occupational prestige .Table 1).Neuropsychological data will be obtained by administering the NBB  to parti–The Prospective Memory Test (PMT)  aims to –The Word Accentuation Test (WAT)  is the S–The Montreal Cognitive Assessment (MoCA)  is a 30-–The Animal naming fluency subtest from the Calibrated Ideational Fluency Assessment (CIFA) , was use–The Taylor Complex Figure (TCF)  was init–The University of Deusto Interference Test , based o–The Salthouse Perceptual Comparison Test (SPCT)  assesses–The Modified Wisconsin Card Sorting Test (M-WCST)  is a shoData will be collected from adults in three countries: Bulgaria, Croatia, and the Netherlands. The aim is to assess at least 300 participants in total, 100 from each country, after exclusion. Participants will be recruited by “word of mouth” from different geographical locations in each country. In addition, universities, companies, and retirement homes will be contacted to ensure a comprehensive sample of the population in each country.The sample size will be chosen taking into account the suggested sample size by the epidemiologic program “EPI INFO” and it will be based on realistic time and location constraints that authors will face when obtaining participants. According to the size of population older than 18 years for each country , the sample size will require at least 100 participants per country. The samples will be balanced considering two main demographic characteristics: age and education. Stratification will be done according to eight levels of age  and four levels of education  . The ageThe study was approved by the Ethics Committee at the University of Deusto, Bilbao, Spain, which is the coordinator of the study. The study has also been approved by Sofia University “St. Kliment Ohridski,” Bulgaria, Faculty of Humanities and Social Sciences, and the Centre for Croatian Studies, University of Zagreb, Croatia. The ethical approval has been submitted for Utrecht University, Netherlands, to which the evaluators in this study are affiliated. All subjects will be volunteers and will provide written informed consent prior to their participation in the study, in accordance with the Declaration of Helsinki.Table 2). The sampling plan was developed following the proportions of the recruitment plan by The main goal of the study is to translate and adapt the NBB into Dutch, Bulgarian, and Croatian. For this purpose, copyright for all subtests included in the original battery was obtained. The battery has been translated and back-translated from English into each language by proficient individuals (who have studied English at least the bachelor level and are native speakers of language for which the test is being adapted). In the Netherlands, most of the subtests were already available. Once the instructions and answer sheets for each subtest have been translated and back-translated, an instruction manual will be developed in each of the three languages. The examiners for each country were trained individually in neuropsychological assessment. The first author of the NBB, Rocio Del Pino, executed this training. There will be five assessing examiners who have been trained in the NBB; two examiners from the Netherlands, two examiners from Croatia, and one examiner from Bulgaria. A detailed plan was developed for sample recruitment, which will be closely followed in order to make the sample comparable across countries (see (1)The population older than 18 years old of each country  was reviewed in their corresponding National Institute of Statistics .(2)The sample size was calculated with “EPI INFO” program for a total sample of 23,210,911. According to this, at least 270 participants should be included in the study for a confidence level of 90% .n = sample sizeZ = Z-score value for the selected confidence levelN = Populationp = probability of successq = probability of failured = acceptable margin error(3)Adhering to previous studies , the sam(4)The final sample will consist of 100 participants per country; that is the adjusted sample after excluding participants that fail to meet the inclusion criteria.Figure 1. At the beginning of the study, all participants will have to read and sign the informed consent. They will then be interviewed by the examiner in order to gather demographic data and data related to their medical history, use of drugs, alcohol, etc. Once the interview is completed, the examiner will present all of the tests from the battery. The whole assessment procedure should last about 20 min with the majority of the participants, but with consideration of the fact that older participants are more likely take longer in completing the full battery. Neither the names nor any other information that may lead to the identification of the participants collaborating in the study will be published in any of the work resulting from this investigation. Therefore, each evaluator will have a code, as well as each participant. This code will be written in each sheet of the assessment in order to meet Data Protection Law and in accordance with the Declaration of Helsinki. To ensure that the coding scheme is comparable across examiners, the inter-rater reliability as well as the internal consistency will be checked. Finally, the complete data from the assessment will be codified and included in a data collection sheet. This data will then be processed and analyzed. A flowchart of the analysis plan is illustrated in Figure 2. All analyses will be performed using SPSS .The examiners will assess all participants in a quiet, neutral environment with minimum distractions . The assessment itself will consist of two parts: a structured interview and the guided completion of the NBB . A schemIn order to identify possible covariates, it will be checked whether sociodemographic variables, such as SES, are equally distributed across countries. To do this, a Chi-squared test of homogeneity will be conducted for each of the sociodemographic variables. If significant deviations across countries are found, the corresponding sociodemographic variables will be included as covariates in the model.A hierarchical model will be used for the analysis of participant and country effects because the participants are hierarchically nested within countries (participants within each country perform more similar than participant between countries). Cultural differences as well as the interaction between sex, age, and level of education on cognitive performance will be tested with a hierarchical multivariate linear regression with multiple predictors. The proposed model includes the country of living as a predictor as well as the interaction between the predictors age, sex and education level. To control for their associated intraclass correlation country of living will be specified as a random factor. The predictors are expected to affect participants’ scores in the NBB. In addition, all sociodemographic variables that are unequally distributed across countries will be included as covariates in the model. A separate analysis will be carried out for the categorical variable resultant of the prospective memory test. A hierarchical logistic regression will be conducted to predict the probability that the participant needs help in the prospective memory task. For the logistic regression the same model is used as with the multivariate multiple regression. To validate the proposed model, a likelihood ratio test will be conducted to assess whether country of living and the proposed interaction contributes significantly to the model fit. The likelihood ratio test compares the model containing the proposed effect with the restricted model without the effect. This analysis will be executed both for the hierarchical multivariate regression as well as for the hierarchical logistic regression.post hoc tests are proposed for the main analysis. First, a post hoc power analysis will be conducted by analyzing the width of the confidence interval for the effect sizes. This analysis will indicate the likelihood of the real effect size being (non)zero. Second, the multivariate main analysis includes the standardized scores of all subscales in the NBB – except for the prospective memory – as an outcome variable. To obtain more detailed information about the location of the possible effect, separate univariate regressions will be conducted for each independent subscale score. The predictors will be the same as in the main analysis and corrections for multiple testing will be applied.Two In order to acknowledge the complexity of possible interactions, we will conduct additional exploratory analysis of the data, with the aim of gaining deeper insight into the interaction effects.Considering there is a serious lack of standardized neuropsychological instruments, we aim to ameliorate that situation by providing the interested parties with a readily translated and standardized brief battery. A review of currently available brief neuropsychological batteries has clearly demonstrated the demand for brief cognitive batteries in respective countries, especially in eastern European countries. A careful examination of the state of available neuropsychological instruments available for clinicians and other uses in Bulgaria and Croatia yielded no availability, while the Netherlands has limited availability of such resources. This will change once this battery is made available for use in the respective countries, providing professionals with a much-needed instrument.With the growing understanding of the importance of accurate and fast neuropsychological assessment, the making of a universal brief assessment tool for a wider cross-cultural application will be put to test. We expect to demonstrate that cultural aspects—such as language, education, and age—affect individual cognitive performance and each country should take into account its own characteristics to provide accurate interpretation guidelines crucial for making appropriate clinical decisions. We expect our project to have impact across Europe, making the issue of assessment instrument availability acknowledged by professionals, encouraging parties involved in such assessment to tackle the issue by joining the Normacog development initiative in respective countries that will be supported from the original authors. This Protocol should serve as a starting point for any researcher who is interested in adaptation for their language.Besides these strengths, some limitations have emerged so far. Obtaining an adapted test for premorbid functioning in both Bulgaria and Croatia has already proven to be problematic, which may complicate the comparability of results within the given domain. Further, the test for premorbid functioning used in the Netherlands consists of 50 words, while the same test used in Spain consists of 30 words, which might cause difficulties comparing the results. Secondly, another test, the Salthouse Perceptual Comparison Test, where a participant has to compare a series of letters with each other, may also be problematic in our study. This test has been made with the Latin alphabet while people in Bulgaria use the Cyrillic alphabet. For this reason, Latin letters were replaced with Cyrillic ones, potentially impeding comparison between people in Europe who did this test. Thirdly, the current study involves the minimum adequate sample size required for analyzing cross-cultural differences; therefore, if future studies aim to test a representative sample of each country, a larger sample size should be included.Previous research suggests that cultural differences exist in cognitive functioning between countries from Eastern and Western Europe . TherefoTo summarize, the translation, adaptation, and standardization of the NBB for the Netherlands, Croatia, and Bulgaria have been completed. Our next step is to gather participants and start the analysis of cross-cultural differences in cognitive performance.All authors contributed equally to and have approved the final manuscript. RDP originated the study design as part of the Normacog project and was the supervisor of the study. LJ, LB, DT, NdB, and AD were responsible for collection of data. AS was responsible for the proposed analysis EB was responsible for writing the introduction and leading the copyright process. LJ was the team’s communication officer.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.


Despite all the human efforts and monetary investment over the last few decades, cancer is still a devastating threat to our life expectancy and quality of life in many parts of the world. The etiology of cancer varies. The genetic and epigenetic causes of cancer are heterogeneous and multifaceted. Early detection is still a difficult issue in many types of malignancies, resulting in unsatisfying cure rates. Cancer not only is a disease of malignant cells but also involves the host physiology, especially the natural defensive immune systems. The task of overcoming cancer is so vast and overwhelming that it will no doubt require scientists worldwide to join efforts and resources to succeed.The year of 2016 is the 150th year Anniversary of Dr. Sun Yat-sen’s birth. Dr. Sun was a doctor who devoted his life to treating patients and to revolutionizing the history of mankind by putting China on a path for modernization. We seek to honor Dr. Sun Yat-sen’s legacy by soliciting 150 most important questions in the war against cancer from cancer researchers and clinicians worldwide. We hope that presenting the most important questions in cancer research and clinical oncology would be beneficial to stimulating all cancer fighters, especially young generation researchers to focus on the key issues that will generate the most significant impact on cancer cures.A significant number of questions have been submitted by clinicians and researchers covering the fields of cancer etiology, cancer immunology, metabolism, biology, prevention, early detection, treatment modality, drug development, novel experimental technology, and cancer-induced erythrogenesis.Chinese Journal of Cancer will publish a series of peer-reviewed top-ranked questions from several categories. We hope these questions will further trigger a brainstorm among cancer researchers and encourage continued submission of key questions and research articles in the coming months to the Chinese Journal of Cancer as a special tribute to Dr. Sun Yat-sen.Starting in the December issue of 2016, the


We investigate how mass shootings influence the stock price of firearms manufacturers. While it is well known that mass shootings lead to increased firearms sales, the response from financial markets is unclear. On one hand, given the observed short-term increase in demand, firearm stock prices may rise due to the unexpected financial windfall for the firm. On the other, mass shootings may result in calls for regulation of the industry, leading to divestment of firearms stocks in spite of short-term demand. We examine this tension using a market movement event study in the wake of 93 mass shootings in the U.S. between 2009 and 2013. Findings show that stock prices of firearm manufacturers decline after shootings; each event reducing prices between 22.4 and 49.5 basis points, per day. These losses are exacerbated by the presence of a handgun and the number of victims killed, but not affected by the presence of children or location of the event. Finally, we find that these effects are most prevalent in the period 2009–2010 but disappear in later events, indicating that markets appear to have accepted mass shootings as the “new normal.” Washington Post reported that among OECD countries, the US had the highest number of firearms per capita, as well as the highest gun-related deaths per capita  inclusive). To do so, we replace the dichotomous treatment indicator with a series of dummy variables to capture the average abnormal return for each day in the panel associated with the event. Intuitively, this model allows for the identification of pre-treatment trends, if any, as well as the average return by day post-event. Results from this relative time model are in While the results in Tables These results can also be displayed graphically–We conduct several additional robustness and falsification tests of the baseline regressions to establish that the results are unambiguous. In the interest of brevity, and owing to the weaker base correlation with the DGTW index, we estimate these and all subsequent models using the S&P market index. It should be noted, however that all results are consistent with the DGTW market index. These tests include providing individual results for the two firms, modifying the training period for each model, including other placebo effects (i.e. the deaths of celebrities), as well as estimating different regression specifications for the main effects. These tests are described in the Statistical Appendix and provide largely consistent results. In the following sections, we explore possible mechanisms for why the observed negative effects of mass shootings on short-run stock prices manifest.The results reported above provide evidence that mass shootings negatively affect firearm stock prices. However, the characteristics of these events can be probed further to better understand the mechanism by which these events may be leading to negative pressure on firearm stock prices. In this section, we conduct a series of tests to evaluate the moderating influences of contextual variables associated with each event. These analyses allow us to test the robustness of the results, in terms of falsifiability and identifying boundary conditions, as well as establish which specific aspects of shootings may be more or less damaging to firearm manufacturers. In these tests, we use the traditional market movement model, as opposed to the relative time model, to ease with the comparison of coefficients across estimates.We consider the impact of the composition of the victims affected by the shooting. By definition, mass shootings lead to at least four fatalities. However, beyond this threshold, the number of victims directly influences the salience of the event within the broader environment , leadinResults in Tables It is surprising that the results reflect a strong effect of victim count but not the presence of children. Children are involved in fewer of the incidents but these events arguably carry tremendous salience. Though the data includes the Sandy Hook shooting in Newtown, CT, where 20 elementary school children were killed, this remains an aberration even within the context of mass shootings in the US. The presence of children as victims undoubtedly makes mass shootings salient , but theTo the extent that a wide variety of firearms have been employed in mass shootings, we investigate whether or not the involvement of a handgun exacerbates or attenuates the negative effect on stock prices. We use handguns for the following reasons. While assault weapons are largely regulated by the Violent Crime Control and Law Enforcement Act of 1994, handgun regulation has, to a greater or lesser degree, become the purview of state and municipal governments. Furthermore, regulations or bans on the use of shotguns and hunting rifles are rare given their use in recreational sport (viz. hunting) and their importance in agricultural and ranching work. In contrast, handguns have seen multiple, and often failed, attempts to increase regulation at the federal level; such as the 1993 Firearms Safety and Violence Prevention Act. Further, the Federal Consumer Product Safety Commission (CPSC) does not regulate handguns as it does almost all other consumer products. Finally, the debate on the right to carry concealed handguns , 31 and To determine the effect of handgun use, we split our sample into those events involving handguns, and those not involving handguns . We then replicate our estimations of the market movement model. Results in We next consider the influence of the location of the event on the market response. As discussed previously, legislation relating to the regulation of firearms is not only enacted at the federal level, but at the state and municipal level as well. Therefore, the level of current state and municipal regulation concerning firearms is likely reflected in the majority views of the voting population of the region, i.e., stronger local regulation in liberal regions in the US like New York, Massachusetts, and Washington DC but weaker regulation in conservative regions like Texas, Idaho, and Oklahoma. If state and local regulation is critical, then there are likely to be differences in the market’s response based on where the shooting occurs. Indeed, some emerging work attests to the fact that legislators in states that lean ideologically left vs. right tend to have a different response to mass shootings in terms of the gun-related bills that are introduced in those states . If suchttp://cookpolitical.com/) to account for the ideological leanings of the region, at the level of the state. Used extensively in prior research [We use the Cook Partisan Voting Index  that no such action will materialize.In this work, we investigate how mass shootings in the US influence the stock price of publicly traded firearms manufacturers. Drawing upon research from stakeholder theory , and buiTaken in sum, results strongly suggest that when mass shootings occur, investors appear to be reducing their valuations of these firms. We contend that these results are reflective of the systematic violations of the social contract existing between firms and society in the US as a result of gun violence. Economically, our results indicate a penalty between 49.5 and 22.4 basis points, per day, when shootings occur, resulting in lost market capitalization of $5mm ($6.7mm) for Smith & Wesson (Ruger) over a 2-day window. Interestingly, these negative effects are not extended to other firms or industries that are closely associated with the two firearm manufacturers we study.From a theoretical perspective, the context we study poses an interesting departure from much of the literature that addresses the economic costs of wrong-doing by organizations. On one hand, received research includes several studies that address the negative impact on a firm or on an industry when there is significant evidence of corporate wrong-doing or fraud , 79, 80.In the case of firearm manufacturers, especially in the US, there are significant differences in terms of corporate agency as well as responsibility. In many ways, the firearms industry is closer to the tobacco and alcohol industries in that they offer a product that is legal and regulated. However, the products marketed by tobacco and alcohol firms still require some level of individual agency on the part of the actual consumer. In contrast, with mass shootings, a disproportionate cost is borne by individuals who are neither customers nor directly related to the firearm industry. Moreover, there is rarely ever admittance of corporate wrong-doing nor are mass shootings considered to be industrial accidents. In spite of these events, firearms enjoy a level of prestige and acceptance that are rarely ever accorded to tobacco and alcohol firms, and operate in a context that is historically and economically unique to the US.per se, and, on the other, society has limited means by which it can sanction these firms. This is reflected in prior work that has addressed first, the limited attempts made by firearm firms to apply the right safeguards to their products so as to reduce misuse [While a historical analysis of firearms in the US is beyond the scope of our analysis, the economic implications of how these products are used can and should be studied . While me misuse , and sece misuse . Our wore misuse .In terms of practical implications, our work here helps inform policy makers about some of the costs associated with mass shootings, and therefore gun violence, in the US. Much of the gun violence in the US occurs outside such mass shootings and therefore, in real terms the economic costs we identify are relatively small. However, it is worth considering what the total cost of gun violence is likely to be if all such costs could be reflected through the market. With just two firms, we observe losses in market capitalization of over $70M. If more firearm firms were public or the true costs of gun violence in other contexts could be quantified, there would likely be a cleaner case made for reasonable gun regulation and control that would satisfy all parties. That said, our analysis also reflects an uncomfortable reality–that the market adjusts to non-action on the part of regulatory agencies by systematically under-responding to such events. The market’s response is rational, if distressing from a societal and ethical perspective, since the risk of regulation has been observed to be systematically reducing in the US during the period of our analysis .A further implication of our work, albeit in a different context, would be to inform political entities of the value of gun control legislation that may limit both the human and economic costs of mass shootings. Recent work has attempted to causally link mass shooting events to the introduction of bills in legislative bodies within the United States, seeking to address restrictions on firearms . In thisOur work here has certain limitations. First, we are limited by the fact that only two publicly traded firearms firms are available for analysis in the US. Given this small set, it may be reasonable to consider our work here as a case study rather than a generalizable large-sample event study. However, as we use public data sources for our analysis, all of our models can be independently verified. Second, given the randomness of mass shootings and the exogeneity it provides, we estimate relatively simple models of stock movements after the event. While more complicated econometric models may be devised, we believe that the basic results will not change, given the sharply diverging coefficients observed for the S&P 500 index and the firearms stocks. Finally, while we theorize about social contract breaches and the risk of regulation in our work, we cannot rule out alternative explanations that may drive stock prices. Perfect identification of the mechanism is difficult, but the set of results we observe across multiple specifications provides some evidence of mechanisms. Future work is needed to fully establish whether regulation is indeed the dominant force.In summary, in this paper we investigate how mass shootings in the US influence the stock price of publicly traded firearms manufacturers. Results strongly suggest that when mass shootings occur, investors appear to be reducing their valuations of these firms and it appears that this effect is driven by the threat of impending regulation. Economically, results indicate a penalty between 49.5 and 22.4 basis points, per day, when shootings occur, resulting in lost market capitalization of $11.3mm ($15.05mm) for Smith & Wesson (Ruger) over a 10-day window, particularly when handguns are used. Results also show that these effects appear to be increasingly attenuated in the later period of our dataset, indicating that investors do not expect any regulatory action with respect to gun control in the US in the later years of our analysis, implying possible acceptance of mass shootings as the “new normal”.S1 File(DOCX)Click here for additional data file.


Between 2011 and 2013, the Lebanese population increased by 30% due to the influx of Syrian refugees. While a sudden increase of such magnitude represents a shock to the health system, threatening the continuity of service delivery and destabilizing governance, it also offers a unique opportunity to study resilience of a health system amidst ongoing crisis.We conceptualized resilience as the capacity of a health system to absorb internal or external shocks  while sustaining achievements. We explored factors contributing to the resilience of the Lebanese health system, including networking with stakeholders, diversification of the health system, adequate infrastructure and health human resources, a comprehensive communicable disease response and the integration of the refugees within the health system.In studying the case of Lebanon we used input–process–output–outcome approach to assess the resilience of the Lebanese health system. This approach provided us with a holistic view of the health system, as it captured not only the sustained and improved outcomes, but also the inputs and processes leading to them.Our study indicates that the Lebanese health system was resilient as its institutions sustained their performance during the crisis and even improved. Lebanon is a country in the Middle East with a population of 4.2 million people . Since tSince 2011, following the escalation of the devastating war in Syria, there has been a large and ongoing influx of Syrian refugees to Lebanon. By the end of 2015 the numbers of refugees had reached 1.5 million, in addition to 53 000 Palestinians returning from Syria . The newThe unprecedented influx of refugees has placed a considerable burden on the Lebanese government, society and economy, which are facing many other challenges. For example, while the Gross Domestic Product (GDP) of Lebanon grew by 8% in 2010, it fell sharply to 1.5% in 2013 , constraA refugee crisis of such a large magnitude is a severe shock to the health system, and threatens continuity of service delivery, destabilizing governance and limiting access to care . To dateBox 1The health system in Lebanon is a public–private partnership with multiple sources of funding and channels of delivery. Almost one half of the population is financially covered by the National Social Security Fund (NSSF), an autonomous public establishment or by other governmental (civil servants cooperative and military schemes) or private insurance. All those schemes provide financial coverage with variable patient copays. The non–adherents are entitled to the coverage of the Ministry of Public Health (MoPH) for secondary and tertiary care at both public and private institutions. Palestinian refugees are covered through the United Nations Relief and Work Agency for Palestinian refugees (UNRWA) for their health care services .Although the MOPH does not cover ambulatory care services, it provides in kind support to a national network of primary health care (PHC) centers all over Lebanon . The cenAround 68% of the primary health care centers in the national network are owned by NGOs while 80% of hospitals belong to the private sector . The strResilience is the ability of a health system to sustain or improve access to health care services while ensuring long–term sustainability ,13. A reLebanon is currently facing an acute crisis  due to aThe aim of this study is to assess the resilience of the Lebanese health system in the face of an acute and severe crisis and in the context of political instability.While many conceptual frameworks for resilience exist  there isWhen studying the resilience of the Lebanese health system we drew on insights from studies of health systems that have faced refugee crises – studies which have considered the ability of a health system to maintain service delivery, prevent major outbreaks and sustain improvements in population level outcome indicators including utilization, service coverage, morbidity and mortality rates, as measures of success –33. The The study employs a case study approach and draws on data from multiple sources. We use an input–process–output/outcome model of a health system , where iThe study, which took place from January 2014 to July 2015, consisted of two main components: a literature review on resilience and how to measure it, and analysis of secondary data to document the impact of the refugee crisis and the health system response in Lebanon.For the literature review, we undertook a search using the following databases: OVID Medline, the Cochrane Library, and Health Systems Evidence. In addition, we searched gray literature databases such as Reliefweb, MedNar, OAISTER, Open DOAR, PROSPERO and OpenGrey.Local data were obtained from multiple sources, namely the Lebanese Ministry of Public Health database which included data on service utilization, human resources, immunization coverage, and epidemiological surveillance. We also used national health accounts data  and maternal mortality observatory data. The MOPH information systems and the maternal mortality observatory data sets are designed to incorporate ongoing assessment and reporting related to displaced Syrians, including for immunization coverage, disease surveillance and utilization of health services in addition to maternal and child mortality. Other sources included statistics from the Lebanese Ministry of Finance, Bank of Lebanon and the Central Administration for Statistics (CAS), UN agency publications, World Bank assessments, and international and local NGOs publications.Human resources. The fluctuating pattern in the number of physicians started before the Syrian Refugee crisis as a result of a mismatch in supply and demand, with persistent oversupply [ersupply . By contersupply . The steersupply .Financing. In 2013–14, there was no substantial change in patterns of public spending on health, the budget of the MOPH, and all public funds rose at the same rate of yearly increase as in the preceding years [. The funds from international donors are managed by United Nations (UN) agencies and are channeled through different international and local NGOs. The MOPH was not a recipient of these funds but worked with international entities to influence effective application of the funds to priority areas and populations.ng years . Howeverng years , decliniThroughout the crisis, the Lebanese health system was able to sustain the level of financing of services at primary–, secondary– and tertiary–care levels. The MOPH contracts with primary health care centers were maintained. The MOPH was able to uphold and improve its contracting terms with private hospitals by including performance measures in the contracts to achieve required service volumes at specified quality levels. Additionally, all the public funds and private insurance companies continued to provide cover to their respective beneficiaries, notwithstanding delays in reimbursement.Despite financial constraints, the MOPH managed to increase its expenditure on drugs, which helped to effectively meet the higher demand that arouse in recent years . This exFor Syrian refugees, primary care has been partly subsidized by the United Nations High Commission for Refugees (UNHCR). However, for secondary care the financial assistance provided by UNHCR has been limited to vulnerable groups, and for life–threatening conditions with co–payments provided by refugees . The limGovernance. At the start of the crisis, there was no clear government policy regarding the displaced Syrian population. While the MOPH began to offer displaced Syrians the same immunization and schedule primary health care services offered to Lebanese citizens, UNHCR and other relief agencies sought to create their own delivery channels and their own mechanism financing coverage which operated in parallel to the existing health system. The parallel systems established by international agencies led to fragmentation and poor coordination of the health system response to the refugee crisis. In the absence of a clear government policy, the fragmentation of health system governance prompted the MOPH to call upon international agencies to consider a more integrated approach to planning, financing and service delivery by embedding refugee health care within the national health system. To develop an integrated approach, the MOPH established a steering committee that includes major international and local partners to guide the response. The steering committee, led by the MOPH, develops strategic plans and coordination mechanisms and monitors the response [response . All parBox 2The registration of Displaced Syrians began in January 2012 by the UNHCR. By 6th May 2015, UNHCR had temporarily suspended new registrations as per Government of Lebanon's instructions. The highest influx occurred in 2013, with the number of Displaced Syrians reaching around 1 200 000 by end of 2014. The number of Displaced Syrians had declined by 31st October 2015 to 1 075 637.In response to the rapid rise in demand for human health resources, the MOPH, in collaboration with UNHCR, WHO and UNICEF, recruited a limited number of health workers to strengthen its surveillance system and emergency response capability and to cater for the needs of Displaced Syrians living in the informal tented settlements, in addition to a limited number of administrative employees at central and peripheral levels. A total of 92 new staff was recruited in PHC, in dispensaries’ and in public hospitals. Due to financial constraints, however, this number diminished gradually to 42 by the end of 2015. Retention of the remaining staff will largely depend on the evolution of the crisis.Embedded in each program of work were a set of monitoring and evaluation tools that made the stretched activities run smoother. Each program approached the crisis as if they were dealing with an enlarged population of 30%. Immunization activities, PHC services, and secondary care provision were all maintained and effectively expanded while monitoring and maintaining quality standards.During the crisis, the participation of the private sector and civil society, and networking with different donors, international stakeholders and UN agencies was not only important for health system governance but also for the development of multi–sectoral health strategies. Examples of successful partnerships included the engagement of the primary health care national network and private hospitals in health care delivery to mount a unified and effective response .Service provision. Provision of health care has been sustained at all levels throughout the crisis. Primary health care centers and hospitals from both public and private sectors have remained operational. Health programmes, such as those for epidemiological surveillance, immunization, medication for chronic illnesses, tuberculosis, HIV/AIDS and reproductive health, among others, are functioning effectively [ectively . Other pectively .Nationwide vaccination campaigns for polio and measles have been routinely conducted as needed, and services provided to all those in Lebanonirrespective of nationalities . These iIn addition to the primary care centers across Lebanon that were providing health services for Syrians refugees and the public health response, at the hospital level, UNHCR contracted with public and private hospitals to provide for registered Displaced Syrians selected secondary care services, covering 75% of the fees . The addHealth service utilization. Since 2013, the number of primary health care centers in the national network as well as the number of beneficiaries steadily rose [ily rose . In 2014ily rose . These bily rose .Private and public hospitals continue to deliver quality services. While the MOPH has sustained its coverage of hospital admissions for uninsured Lebanese, admissions for insured Lebanese have not been disrupted by the Syrian refugee crisis   are considered important indicators of health system performance . The vacHealth expenditures. To ensure uninterrupted financial coverage, the MOPH developed and implemented a reform strategy in 1998 to rationalize health expenditures targeting the high financial burden on households. This strategy resulted in lower out of pocket expenditures, which declined from 60% in 1998 to 34% in 2013, at the peak of the Syrian crisis [n crisis ,44.Morbidity and mortality. Despite political instability, Lebanon has achieved the health related Millennium Development Goals  [5 and 6) . Infant 5 and 6) . Similar5 and 6) –47. Chil5 and 6) ,45,46.Prevention of outbreaks of infections. The influx of Syrian refugees has increased the risk and exposure to communicable diseases, including those that previously did not exist in Lebanon [ Lebanon . Communi Lebanon . OutbreaLebanon effectively managed several outbreaks including for measles. In 2013, the number of reported measles cases was 1760, compared to 29 cases in 2015 . The sprCase notification rate of tuberculosis (TB) in Lebanon had been declining until 2011. In 2012, the case notification rate increased by 27%, however   networking with stakeholders (ii) diversification of the health system that provided for adequate infrastructure and health human resources (iii) a comprehensive communicable disease response and (iv) the integration of refugees into the health system. A question that remains unanswered is the longer–term sustainability of the current response. Although, thus far, Lebanon has sustained achievements in morbidity and mortality levels, the magnitude and the chronic nature of the crisis continues to pose a threat to the health system.The study has three main strengths. First, to our knowledge, this is the first study to investigate the resilience of a health system during an ongoing major refugee crisis. Second, the use of the input–process–output–outcome model to analyze the data and to categorize the health system resilience has helped to frame the system as a whole, and shed light on the possible contributing factors to achieving resilience. Third, the study used multiple sources of information, including the public, private, civil society and humanitarian sectors, to provide a comprehensive view of the Lebanese health system.Several limitations are also acknowledged. First, the literature lacks a rigorous and scientifically validated method for measuring and proving resilience in health systems. We used a model that included several dimensions of resilience identified from published and gray literature, in addition to health system performance indicators which we considered to be important measures relating to resilience. Second, the study was limited by the availability of data on the dispersed refugee population and the ongoing influx of refugees. Third, the dynamic nature of the refugee situation means data need to be regularly updated.Notwithstanding limitations, however, our study contributes to an area of global importance and helps empirically to illuminate effective response from a health system that has shown resilience in spite of the most severe refugee crisis of recent times experienced anywhere in the world.


Journal of Medical Case Reports in 2006, the number of journals that publish case reports has increased rapidly, and most of these journals are open access. Open access publishing usually requires authors to pay publication fees while offering the articles online, free of charge, and free of most copyright and licensing restrictions. The movement for open access has gained support in the research community, with the publishers BioMed Central and PLOS ONE becoming leaders in scientific publishing in their number of articles and citations. As the number of open access publishers has exploded, so too has the number of publishers that act in bad faith to profit from the open access model. Simple guidelines have been developed and resources are available to help authors choose a suitable journal for publication of their case reports.Since the establishment of the  Case reports offer unique value to the body of medical knowledge by describing new diseases, disease mechanisms, therapeutic approaches, and adverse or beneficial effects of drugs. The act of recording, discussing with colleagues, and publishing clinical observations as case reports remains essential to the art of medicine and patient care . These sThe merits of large randomized studies are well known, but many clinicians recognize the value of case reports as a complement to evidence-based medicine. The case-based nature of clinical practice often is at odds with the population-based nature of research studies, where the findings may have little relevance to an individual patient. Narrow inclusion criteria and the absence of co-morbidities in randomized trials often create a disconnection between typical patient populations and populations represented in research studies . Case reIn response to renewed interest and acknowledgment of their value, the number of peer-reviewed journals that publish case reports has increased in recent years to more than 160 . Most ofIn line with the growing demand for case report publishing opportunities, the number of new peer-reviewed journals that focus on case reports had increased to more than 160 journals produced by 78 publishers by mid-2015. Figure Open access publishing offers freely available and unrestricted use of research and scholarship, which many researchers see as vital to efficient dissemination of science in the digital world . The opeAs scientific publishing shifts from a business model of subscription revenue to open access, the number of open access journals has exploded. However, the proliferation of journals that will publish seemingly anything for a fee has caused alarm among many in the global research community. Alongside many respected open access publishers, others have entered the space acting in bad faith. Some see it as the “dark side” of open access, a growing collection of pseudo-academic, prestigiously titled journals, many of which have similar but not quite identical websites and names to those of well-known established journals. Many of the websites look sufficiently impressive that non-experts doing online research have trouble distinguishing credible research from junk. Experienced academics have been misled into submitting manuscripts and even serving on editorial boards for pseudo-academic journals, agreements that often are difficult to undo. Most of these journals do not post their publication fees, and often authors are not informed of fees until after submitting a manuscript. Withdrawal of a manuscript, which is necessary before submitting the same paper to a legitimate journal, may require payment of the high fees first . For somScholarly Open Access, although removed by Beall for unknown reasons in January 2017, closely monitored the increasing number of open access publishers and alerted readers to individuals, publishers, publications, meetings, and scholarly metrics that, in the view of Mr Beall, appeared to exploit the open access model [Code of Conduct for Journal Publishers from the Committee on Publication Ethics (COPE), and Principles of Transparency and Best Practice in Scholarly Publishing from COPE, the Open Access Scholarly Publishers Association (OASPA), and the World Association of Medical Editors [Jeffrey Beall, a librarian and associate professor at Auraria Library at the University of Colorado, Denver, coined the term “predatory open access publishing” to describe this situation. He is a critic of open access publishing, blaming the system for creating the problem of predatory publishers. His blog ss model . He main Editors –16. Simi Editors . The casThe challenge for watchdogs and authors alike is to decide when a publisher is untrustworthy or simply unprofessional. Some publishers may fall under suspicion due to poor copy editing or amateurish website design, but this may not reflect an outright neglect of scholarly standards. It is important not to blacklist startup publishers who lack experience. Another problem with maintaining lists of disreputable publishers is that because copycat journals are often short-lived, the blacklist will continue to grow but individual entries may quickly become obsolete.Have you read any articles in the journal before?Is it easy to discover the latest papers in the journal?Do you or your colleagues know the journal?Is the publisher name clearly displayed on the journal website?Can you contact the publisher by telephone, email, and post?Can you easily identify and contact the publisher?Does the journal site explain what these fees are for and when they will be charged?Is the journal clear about the type of peer review it uses?Have you heard of the editorial board members?Do members of the editorial board mention the journal on their own websites?Do you recognize the editorial board?Do they belong to the COPE?If the journal is open access, is it listed in the Directory of Open Access Journals (DOAJ)?If the journal is open access, does the publisher belong to the OASPA?Is the publisher a member of another trade association?Is the publisher a member of a recognized industry initiative?The “Think. Check. Submit.” campaign arose in response to concerns about publishing practices, and the effort is supported by a coalition of scholarly publishing organizations. “Think. Check. Submit.” takes a positive approach to help researchers identify credible journals, providing up-to-date guidance for choosing where to publish , 19. To In addition to consulting colleagues and academic librarians for journal suggestions, authors have available to them several online resources. BioMed Central previously collaborated with Edanz, a company that assists authors in navigating the publication process, to create the author academy . The freSeveral automated search tools help identify suitable journals as well. Authors insert keywords from their manuscript abstract into a search engine, which then compares the words to many online publications and Edanz Journal Selector covers a broad range of journals. The online tool is free, and Edanz also offers a journal selection service (US $300) in which experts use their publication experience to identify up to four of the best journals for a given paper . The JouJournal impact factors, calculated and published by Thomson Reuters, measure the average number of citations per published article for papers published over a 2-year period. Despite the fact that the simple metric can be misleading, the impact factor has become, over time, a marker of journal prestige and desirability. The judgment of a paper’s value is often based more on the journal in which it appears than on its content. Many researchers contend that reliance on impact factors undervalues disciplines or study designs, such as case reports, which have lower citation rates. Overall, the number of citations of an article is commensurate with hierarchies of evidence, with meta-analyses receiving more citations than any other study design. Case reports typically receive few citations, although there are notable exceptions . The numNeurocase, has received an impact factor (1.124), dating back to 1998.Efforts to embrace a broader view of value in scientific communication, and perhaps diminish the influence of impact factors, have emerged. Journals of the American Society for Microbiology (ASM) no longer advertise impact factors on their websites. Similarly, in recognizing that impact factors are just one of a number of metrics, Nature journals list a suite of citation-based metrics. Only one case report journal, Taylor & Francis’s Medicine/National Institutes of Health Indexed research databases are often curated to ensure the quality of included publications. Clarivate Analytics (formerly Thomson Reuters) offers The Web of Science™, as one such example, and recently introduced the “Emerging Sources Citation Index” to complement their more selective indexes. This collection reflects the growing number of peer-reviewed publications of regional importance and in emerging fields .Journal of Medical Case Reports from BioMed Central, offer high visibility, relatively rapid publication, and transparent publication policies. The reputation of the journal plays an increasingly important part of the decision, requiring thorough vetting of potential journals.In conclusion, the growth in number of case report journals has provided authors multiple avenues for publication but, at the same time, it has introduced a new level of uncertainty in the journal selection process. Factors to consider when choosing a journal are: the topics the journal covers, the target audience, length restrictions, and the time to publication. Open access publications, such as the


This paper investigates the difference in the health conditions and the health care consumption of uninsured individuals as compared to individuals with private insurance, using a nationally representative data set of inpatient hospital admissions from the US. In line with the previous literature, our results indicate that uninsured individuals are, on average, in worse health conditions. However, if we compare individuals within the same diagnosis category, the uninsured are actually healthier, with a lower number of chronic conditions and a lower risk of mortality. This indicates that the uninsured are admitted to the hospital only for more serious conditions. In addition, our results show that uninsured individuals consume less health care. In particular, conditional on being admitted to a hospital and controlling for health conditions, the uninsured have lower total charges, fewer procedures, and a higher mortality rate. The Affordable Care Act is the most comprehensive reform of the U.S. healthcare system in the last decades and, when fully implemented, it is expected to have a substantial impact on the healthcare system. Moreover, a key motivation for the reform was to provide coverage to the millions of Americans without health insurance. However, there is no agreement in the literature about the health conditions of the uninsured or about how the reform will affect health care costs. Therefore, the purpose of this paper is to provide a more comprehensive analysis of the health conditions and the health care consumption of uninsured individuals as compared to individuals with private insurance.As in any other insurance market, there are important information problems in the health insurance market. First, the adverse selection problem predicts that individuals with a higher probability of requiring medical care are more likely to buy health insurance. This prediction would imply that the uninsured are healthier compared to the insured. However, the health condition of an individual is not the only determinant of whether or not an individual buys health insurance. The other key factor is income, as individuals with low incomes may not be able to afford health insurance. Therefore, it is not clear if the individuals with no insurance are in better or worse health conditions than the individuals with health insurance.In addition, there may be a two-way interaction between health conditions and insurance status. The existing literature indicates many individuals without health insurance are not getting the health care required to treat existing conditions and to keep existing conditions from becoming more serious. For instance, uninsured individuals are less likely to get preventive care such as diagnostic tests and are more likely to be admitted to a hospital for conditions that do not require hospitalization if treated in a timely way . The exiSecond, the moral hazard problem implies that individuals with health insurance consume more health care. There are many studies that show health insurance increases health care consumption. For instance, in the classic RAND Health Insurance Experiment, individuals with more generous health insurance plans had higher health care expenditures . In a siSome researchers, on the other hand, argue that health insurance may alter the type of care individuals get by increasing primary and preventive care and decreasing the use of the Emergency Department (ED), which is quite costly. For instance, Kolstad and Kowalski  and MillIn this paper we use the National Inpatient Sample (NIS) database, which is hospital inpatient administrative data. There are some advantages and disadvantages associated with using this database. A disadvantage of using inpatient administrative data is that we only observe individuals who are admitted to the hospital. Therefore, we investigate the health conditions and health care consumption of the uninsured, conditional on being admitted to the hospital. Given the nature of the data, we will not be able to say much about adverse selection, although we present some indirect evidence. In particular, conditional on being admitted to a hospital, there are systematic differences in the distribution over health conditions between the insured and the uninsured.A key advantage of using inpatient administrative data is that an inpatient record provides comprehensive information about the health conditions and the health care consumption of an individual. Therefore, the question we will consider is whether individuals with no insurance consumes less health care than individuals with private insurance, conditional on being admitted to a hospital and after carefully controlling for the health conditions of the individuals. As has been noted in the literature, it can be challenging to identify the causal effect of having health insurance on health care consumption because insured and uninsured individuals may have different characteristics. Hence, our identification strategy is based on carefully controlling for health conditions, essentially comparing the health care consumption of individuals with the same set of diagnoses.on average, in worse health conditions. In particular, uninsured patients have a higher number of diagnoses and chronic conditions and a higher severity of illness and risk of mortality. However, once we control for the primary diagnosis, the uninsured are actually in better health conditions. This indicates a clear selection bias, where the uninsured are more likely to be admitted to the hospital for more serious health conditions. Moreover, uninsured individuals are considerably more likely to be admitted to the hospital through the Emergency Department, and they have higher mortality rates. Surprisingly, the higher mortality rates for the uninsured do not go away when we control for health conditions, including the full set of diagnoses. We conjecture that the quality and/or intensity of the health care they receive is potentially different from the insured. In fact, our results show that, conditional on being admitted to the hospital and after carefully controlling for health conditions, uninsured individuals have lower total charges, receive fewer procedures, and are less likely to get a major procedure or an operating room procedure.Our analysis reveals that uninsured individuals are, The related existing literature can be organized in terms of the health conditions and the health care consumption of the uninsured, as compared to individuals with insurance. As mentioned previously, there are important information problems in the market for health insurance, which make it challenging to identify the causal effect of health insurance on the consumption of health care. For instance, if the less healthy individuals are more likely to purchase health insurance, it may be difficult to identify the health insurance effect without carefully controlling for the health conditions of an individual. For this reason, several approaches have been used in the literature, including randomized experiments and natural experiments resulting from policy decisions.The best well-known randomized experiments are the RAND Health Insurance Experiment and the Oregon Health Insurance Experiment. The two experiments investigate the effect of health insurance on the consumption of health care, but there are some important differences. In the RAND experiment, every individual in the study was randomly given a health insurance plan, where the plans had various combinations of deductible and coinsurance rate (but the plans had a relatively low maximum for out-of-pocket expenditures). The study investigated how the generosity of the plan, in terms of its cost sharing structure, affected health care expenditures . A partiThe Oregon experiment involved the use of a lottery to expand Medicaid access to many uninsured low-income individuals. In contrast to the RAND experiment, in which individuals were randomly assigned to multiple insurance plans with marginal variations in cost sharing, in the Oregon experiment individuals were essentially randomly assigned to one health insurance plan (Medicaid) or no health insurance plan. Therefore, the Oregon experiment should provide a better estimate of the effect of health insurance on the consumption of health care, for the particular insurance plan and population in the study. The key results of the study indicate that individuals who were selected by the lottery had substantially higher health care utilization and better self-reported health . In termA number of studies have used natural experiments resulting from policy decisions. For instance, Antwi et al.  use a diIn addition, Miller  investigMore closely related to our work, Doyle  investigFinally, a number of studies have used survey data to investigate the health conditions of the uninsured. For instance, Kronick  uses datThe data used in this study are obtained from the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) database for the year 2011. The data approximates a 20% sample of U.S. community hospitalsTable The key independent variables in the analysis include insurance status and variables describing the health condition of an individual (DIAGNOSIS variables). Each inpatient record includes a “primary” diagnosis and up to 24 “secondary” diagnoses. We use the Clinical Classification System (CCS) to describe the different diagnoses. In this classification system, there are a total of 264 different diagnoses. In our analysis, we will control for the primary diagnosis (PRIMARY DIAGNOSIS variable) and for the full diagnosis vector (DIAGNOSIS VECTOR variable), which includes the primary diagnosis and the secondary diagnoses. Table In addition, we control for individual characteristics  and hospital characteristics . Moreover, in our preferred specifications we include hospital dummy variables in place of hospital characteristics.Table Table Number of Diagnoses gives the number of diagnoses in the patient’s record. Each diagnosis is categorized as “chronic” or “non-chronic”. A chronic condition is a condition lasting for a year or longer and which meets at least one of the following conditions: (a) it places limitations on self-care, independent living, and social interactions and (b) it results in the need for ongoing medical care. Chronic conditions include conditions such as diabetes, hypertension, and many forms of mental illness. Non-chronic conditions include conditions such as infections, pregnancy, and physical injury. The variable Number of Chronic Conditions gives the number of diagnoses categorized as chronic. As illustrated in the table, compared to individuals with private insurance, uninsured individuals have a higher average number of diagnoses and a higher average number of chronic conditions.Table Severity of Illness expresses the extent of physiologic decomposition or organ system loss of function and has four subclasses: (1) Minor loss of function, (2) Moderate loss of function, (3) Major loss of function, (4) Extreme loss of function. The presence of multiple conditions in combination with the primary diagnosis determines the severity of illness. An increase in severity of illness reflects increased difficulty and costs involved in treating the patient. On average, uninsured patients have a higher score for severity of illness. Similarly, the variable Risk of Mortality indicates the assessed likelihood of dying based on the diagnosis information and includes the following classes: (1) Minor likelihood of dying, (2) Moderate likelihood of dying, (3) Major likelihood of dying, and (4) Extreme likelihood of dying. On the other hand, the binary variable Died in Hospital indicates whether a patient actually died in the hospital. Uninsured individuals have a higher assessed risk of mortality and a higher mortality rate.The variable Emergency Department is a binary variable equal to one if the admission originated in the Emergency Department of the hospital. This variable is based on the “Emergency Department Service” indicator, which is equal to one when the admission record indicates (1) the Emergency Department as the admission source, or (2) an Emergency Department revenue code, or (3) a positive Emergency Department charge, or (4) an Emergency Department procedure code. As illustrated in the table, compared to individuals with insurance, uninsured individuals are considerably more likely to go through the Emergency Department of the hospital.Finally, the variable The key dependent variables used in the analysis of health care consumption include total charges and length of stay, as well as other outcome measures such as the number of medical procedures performed, the number of major procedures performed, and an indicator variable for a major operating room procedure.Total Charges shows the hospital total charges in dollars for the admission and the variable Length of Stay gives the length of stay in days. The total charges consist of “hospital” charges and “professional” charges. The hospital charges include charges for the room and nursing care and charges for services like laboratory tests, medications, and operating room charges. The professional charges are the charges of the attending and consulting physicians. For our purpose of measuring health care consumption, total charges are a reasonable measure because the total charges are based on the procedures performed  and the hospital list prices .The variable Number of Procedures sums all types of procedures performed. The variable Number of Major Procedures is constructed to include major diagnostic and major therapeutic procedures only. Finally, the variable Operating Room Procedure is a binary variable equal to one if a major operating room procedure is performed.A characterization of the number and types of procedures performed is contained in the variable PRCLASS, which contains the number of procedures in each of the following classes: (1) minor diagnostic (non-operating room procedures such as CT Scan of Head or Diagnostic Cardiac Catheterization), (2) minor therapeutic (non-operating room procedure such as Respiratory Intubation or Circumcision), (3) major diagnostic (operating room procedure such as Laparoscopy or Biopsy of Liver), and (4) major therapeutic (operating room procedure such as Coronary Artery Bypass or Cesarean Section). The variable Table In this section, we present the main results of the paper. First, we provide some results related to the health conditions of the uninsured. Then, we report the results related to the health care consumption of the uninsured as compared to individuals with private insurance.The measures we use for health conditions include the number of diagnoses, number of chronic conditions, severity of illness, and risk of mortality. For each health condition variable, we estimated an equation of the following formd errors .Number of Diagnoses. The results are reported for various sets of controls to check the robustness of the results and to illustrate how the results change depending on the set of controls. The estimate in column (1) is simply the difference in the average number of diagnoses between the uninsured and the insured. The uninsured have approximately 0.4 more diagnoses on average . The higher number of diagnoses may be an indication of worse health conditions. The estimates in column (2) control for individual characteristics and the estimates in column (3) additionally control for hospital characteristics and include state fixed effects. The estimates are considerably lower and no longer statistically significant, with individual characteristics explaining most of the difference. In particular, individuals with lower incomes have a higher number of diagnoses.primary diagnosis. Here, the estimated effect for the uninsured is negative and statistically significant but small (about 3% lower for the uninsured). In other words, after controlling for the primary diagnosis, the uninsured do not appear to be in worse health conditions. Finally, the results in column (5) control for individual characteristics, primary diagnosis, and hospital fixed effects. Including hospital fixed effects allows us to control for unobservable hospital characteristics. The estimates are similar to the previous estimates. Overall, the results indicate that on average uninsured individuals have a higher number of diagnoses, but this result is reversed when controlling for the primary diagnosis. The results are very stable across the different specifications, which highlights the robustness of the results.Table Number of Chronic Conditions. The results are very similar to the previous results. The uninsured have approximately 0.34 more chronic conditions on average . The estimated effect becomes smaller but stays positive when we control for individual characteristics and hospital characteristics. However, if we control for the primary diagnosis, the estimated effect for the uninsured is negative and statistically significant (about 7% lower for the uninsured). That is, if we control for the primary diagnosis, the uninsured actually have a lower number of chronic conditions.Table Severity of Illness and Risk of Mortality, respectively. The results parallel the results from the previous health conditions measures. The uninsured have a higher severity of illness and a higher risk of mortality on average, but the results are reversed when we control for the primary diagnosis. For robustness, we performed a similar analysis using the binary variables High Severity of Illness  Major loss of function or (4) Extreme loss of function) and High Risk of Mortality  Major likelihood of dying or (4) Extreme likelihood of dying) and we obtained the same pattern of results.Tables The previous results indicate that the uninsured are in worse health conditions on average but are in better health conditions when controlling for the primary diagnosis. The results are not inconsistent with each other and have an intuitive interpretation. In particular, uninsured individuals are more likely to be admitted to the hospital for more serious conditions, which are associated with a higher number of diagnoses and chronic conditions and a higher severity of illness and risk of mortality. For instance, compared to individuals with private insurance, the uninsured are more likely to be admitted to the hospital for Endocrine, Nutritional, and Metabolic diseases (which includes diabetes) and for (ii) Circulatory System diseases . These results are consistent with the existing literature and the general perceptions about the health conditions of the uninsured. However, once we compare individuals with the same primary diagnosis, the uninsured are actually in better health condition, a result that does not appear in the literature.Emergency Department variable, which is a binary variable equal to one if the admission originated in the Emergency Department of the hospital. Unlike the study above, our data includes emergency room visits that result in hospitalization. Hence, we only observe real emergencies. In that sense our estimate provides a lower bound on the difference of emergency department utilization between the insured and the uninsured. Table In addition, the existing literature indicates the uninsured are more likely to use the Emergency Department to access the healthcare system . We estiDied in Hospital, which is a binary variable equal to one if the individual died in the hospital. Table Finally, another indicator of the health conditions of the uninsured frequently mentioned in the literature is the actual mortality rate. Therefore, we estimated our basic model using the variable The following results relate to the health care consumption variables, which include total charges, number of procedures, number of major procedures, an indicator of an operating room procedure, and length of stay. We estimate the same basic model as in the previous section, but now the dependent variable refers to the health consumption variables.Total Charges in dollars. Column (1) indicates that the total charges for the uninsured are about $2900 less on average (about 9% less compared with the insured). The estimates in column (2) control for individual characteristics and the estimates in column (3) additionally control for hospital characteristics and state fixed effects. This increases the gap between the insured and the uninsured, the latter paying about $5000 less on average (about 15% less compared to the insured). The estimates in column (4) additionally controls for the full diagnosis vector. This allows us to compare individuals with similar health conditions based on their insurance status. The estimates in column (5) control for individual characteristics, the full diagnosis vector, and hospital fixed effects, which is our preferred set of controls. The estimated effect of being uninsured is consistently negative and statistically significant. The estimate in column (5) indicates the uninsured are charged about $1900 less on average . Finally, the estimates in column (5) indicate that Blacks and Hispanics have lower total charges (compared to Whites), females have lower total charges , and the total charges increase with age. The estimates in column (3) or column (4), which control for hospital characteristics, indicate that total charges are higher in for-profit hospitals, in urban hospitals, and in teaching hospitals.Table full procedure vector in place of the full diagnosis vector. Once we control for the particular set of procedures that patients get, the difference between the insured and the uninsured disappears . This supports the idea that the uninsured have lower total charges because they consume less health care.Our previous results on health condition variables show that controlling for primary diagnosis, the uninsured are not in worse health conditions compared to the insured. However, the current estimates include the full set of diagnoses, not just the primary diagnosis. Hence, we conclude that, after controlling for the health conditions of a patient, the uninsured consume less healthcare. This suggests that the uninsured receive fewer procedures and/or less expensive procedures. As a test of this hypothesis, the estimates in Column (6) control for the Number of Procedures are reported in Table The results for In addition, these results are consistent with, and help explain, our previous findings regarding total charges. In particular, Black and Hispanic patients are getting a smaller number of procedures compared to Whites, females get fewer procedures compared to males, and the number of procedures performed increases with age. Finally, the results in column (4) indicate that there is no significant difference between for-profit and non-profit hospitals in terms of the number of procedures , but as Number of Major Procedures and Operating Room Procedure. Table For robustness, we performed a similar analysis using the variables Length of Stay are displayed in Table The regression results for As a summary, our results from the analysis of the health care consumption variables indicate that uninsured individuals consume less health care. In particular, if we compare insured and uninsured individuals with the same diagnosis vector, who are treated at the same hospital, the uninsured have lower total charges, a smaller number of procedures, and a smaller probability of having an operating room procedure. The difference in the health care consumption of the insured and the uninsured may help explain why the uninsured have a higher mortality rate.This paper attempts to take a closer look at the health conditions and the health care consumption patterns of the uninsured compared to the insured using a nationally representative inpatient dataset from the US. A key advantage of using inpatient administrative data is that an inpatient record includes comprehensive information about the health conditions and the health care consumption of an individual. As has been noted in the literature, it can be challenging to identify the causal effect of having health insurance because insured and uninsured individuals may have different characteristics. Therefore, our identification strategy is based on carefully controlling for health conditions, essentially comparing the health care consumption of individuals with the same set of diagnoses treated at the same hospital.First, we find that conditional on being admitted to the hospital the uninsured are, on average, in worse health conditions. This is a result frequently cited in the literature. However, if we control for the primary diagnoses, the uninsured are actually in better health conditions than the insured. Surprisingly, this is not a well-known finding in the literature. These results together imply that uninsured individuals go to the hospital for more serious reasons that are associated with a higher number of chronic conditions and a higher severity of illness and risk of mortality. For instance, compared to individuals with private insurance, the uninsured are more likely to be admitted to the hospital for conditions related to the Circulatory System  but are less likely to be admitted to the hospital for conditions related to the Musculoskeletal System (such as Rheumatoid Arthritis). Therefore, studies that compare health conditions of the uninsured and the insured based on inpatient data can be misleading. They are comparing very sick uninsured individuals, who have no choice but to go the hospital, to the insured, which may include patients with less severe or non-urgent conditions. We do not observe the uninsured with less severe or non-urgent conditions in the data simply because they do not go the hospital.Second, the uninsured are more likely to be admitted to the hospital through the Emergency Department, and are more likely to die at the hospital. These findings are consistent with the existing literature. These results persist even if we control for the full diagnosis vector. Some researchers argue that the uninsured may choose to seek care in the emergency room because, unlike private physicians, emergency rooms are legally obligated to treat people, even if they cannot pay for services. Our data includes emergency room visits that result in hospitalization. Hence, we only observe real emergencies. In that sense our estimate provides a lower bound on the difference of Emergency Department utilization between the insured and the uninsured. The probability of dying in the hospital is about 35% higher for the uninsured as compared to the insured. If uninsured individuals are more likely to die compared to insured individuals with similar health conditions, this may indicate that the quality or intensity of the health care they receive is potentially different.Third, again conditional on being admitted to the hospital, the uninsured consume less health care. They have smaller total charges, receive a smaller number of procedures, and are less likely to get an operating room procedure. In our analysis, to control for possible differences in health conditions, we include the full diagnosis vector. In addition, we control for individual characteristics and hospital fixed effects. This implies that when we compare insured and uninsured individuals with the same set of diagnoses, who are treated at the same hospital, the uninsured have lower total charges. The difference disappears when we control for the set of procedures in place of the set of diagnoses. This reveals that the uninsured have lower total charges because they are getting fewer and/or less costly procedures. In this study we only look at the number of procedures. The question of whether the uninsured get alternative and less expensive procedures compared to the insured is left as future research.The results of this study suggest that providing health insurance coverage to the millions of Americans without health insurance is likely to improve the health conditions of the uninsured but may result in higher healthcare costs. First, the results imply that uninsured individuals go to the hospital for more serious conditions and are considerably more likely to be admitted through the Emergency Department. Therefore, it is reasonable to conjecture that the uninsured individuals are not getting the necessary care in a timely manner and frequently seek care only when their condition deteriorates to the point of requiring hospitalization. In this situation, providing coverage to the uninsured is likely to improve health conditions and may actually lower healthcare costs by providing treatment at an early stage and decreasing the use of emergency care. On the other hand, our results indicate that the uninsured consume less health care. This suggests that providing coverage to the uninsured may increase healthcare cost by increasing the intensity of treatment and by allowing previously uninsured individuals to get care for non-emergency or elective procedures. Finally, the results related to in-hospital mortality rates, combined with our previous results, suggest that providing coverage to the uninsured is likely to lower the in-hospital mortality rates of the uninsured.