Environmental Health is an electronic Open Access journal, the numbers of downloads at the journal website can be retrieved. The top-20 list of articles most frequently accessed shows that all of them have been downloaded over 10,000 times. Back in 2002, the first article published was accessed only 49 times during the following month. A year later, the server had over 1,000 downloads per month, and now the total number of monthly downloads approaches 50,000. These statistics complement the Impact Factor and confirm the viability of Open Access in our field of research. The advent of digital media and its decentralized mode of distribution - the internet - have dramatically changed the control and financing of scientific information dissemination, while facilitating peer review, accelerating editorial handling, and supporting much needed transparency. Both the meaning and means of "having an impact" are therefore changing, as will the degree and way in which scientific journals remain "factors" in that impact.Environmental Health has just received its first Impact Factor by Thomson ISI. At a level of 2.48, this achievement is quite satisfactory and places Environmental Health in the top 25% of environmental science journals. When the journal was launched in 2002, it was still unclear whether the Open Access publishing model could be made into a viable commercial enterprise within the biomedical field. During the past eight years, Open Access journals have become widely available, although still covering only about 15% of journal titles. Major funding agencies and institutions, including prominent US universities, now require that researchers publish in Open Access journals. Because of the profound role of scientific journals for the sharing of results and communication between researchers, the advent of Open Access may be of as much significance as the transition from handwriting to printing via moveable type. As  Environmental Health, the bestowal of an official Impact Factor (IF) by Thomson ISI. The idea of a quantitative measure of a journal's "impact" was suggested in 1955 by Eugene Garfield in Science [Environmental Health in 2008 and 2009 they would count for this journal's 2010 Impact Factor, thus slightly inflating it.We have just passed another milestone in the history of  Science . GarfielThis is not the only quirk in the calculation of IFs , leadingEnvironmental Health ranks in the top 25% of journals (44 out of 180) listed in the 'Environmental Sciences' category. Of additional importance, an impact factor above 2 is important in some countries, where the productivity of researchers is rated annually from their publications in journals with an IF above that level. We made this mark on our first attempt.With an IF of 2.48, The IF we received is also more than respectable for a journal, such as ours, because the time gap between publication and citation is longer in highly specialized disciplines. Thus, the excellent performance by this metric testifies to the quality of the articles and the value of increased accessibility and timeliness afforded by open access and online publication.A milestone is a marker along a road that shows the distance one has come, and, if the destination is known, the distance yet to be traveled. Science is open ended so there is no ultimate destination, yet we can still make some observations about the landscape we are traveling through in this transitional period in scientific publishing. But we first take a brief look back as we approach the end of our first decade as an established journal.Environmental Health was launched in 2002, among the very first of the independent specialty journals on the roster of a new Open Access (OA) publisher, BioMed Central (BMC), which began in 2000 with its own in-house BMC journals. Its philosophical roots can be traced further back in time, but as a practical matter it was the almost free and limitless publishing and distribution technology of the internet in the 1990 s that made the modern OA movement possible. Suddenly, the major expense of publishing a scientific article online was in actually doing the science and the time and effort of authoring. All peer review and most executive editing was also done without cost to the publisher. The value added of printing and distribution provided by conventional print publishers suddenly disappeared, but in 2002 it was not yet demonstrated that the OA publishing model could be made into a viable commercial enterprise.The realization that the printed journals from the big publishers were no longer an essential ingredient didn't happen overnight. The technology developed much more quickly than established scholars and scientists were able to accommodate, and recognition of the new conditions was unevenly distributed across disciplines. Except for a few isolated examples in the humanities and the social sciences, it was in physics and computer science that the first change occurred. In these fields a submitted paper could take 1 to 2 years to see print and when the papers finally appeared, their complex notation was usually produced by a standard open source and free typesetting program developed by Donald Knuth called TeX . Because LaTeX was widely available for desktop computers and could be obtained without cost, physicists and computer scientists could produce and share their own "camera ready" manuscripts, circulate them for comment and establish priority before publication, even before peer review. Moreover the TeX typesetting language was not only standard but used only plain text that was "marked up" with tags, much like HTML, so collaboration and sharing of work could be done easily by email. From there it was a short step to depositing the manuscript drafts essentially in publishable form in a central source, often called a preprint server. It was just such a server for physics papers that was established at Los Alamos National Laboratory in the early 1990 s. The arXiv.org server still exists and self-archiving among physicists has not only become the norm, but in some subfields like high energy-physics, it is said to be 100%. From physics the pre-print culture spread to related fields like mathematics. Computer scientists had already been doing this and today their preprint server is almost twice as big as arXiv.org, automatically harvesting preprints  from specialized websites and institutional repositories, Google-style.By comparison, open and free access to electronic versions of scientific papers has come late to biomedicine and still faces opposition from large publishers and a lack of understanding from many biomedical scientists. Paradoxically, biomedicine was an early adopter of digital referencing and searching. As long ago as 1879, when John Shaw Billings made a catalog of the US Surgeon General's Library which he had been developing as a repository of medical literature, the US Government had been supporting a monthly publication listing the medical periodical literature called Index Medicus. In 1964 the Library, now called the National Library of Medicine, produced the first computerized version of Index Medicus, the Medical Literature Analysis and Retrieval System (MEDLARS), rechristened Medline in 1971 when it went "online" (at the time this meant remote access from other authorized computer systems). Medline was accessible to libraries but not to the general public until 1997, when it was made freely available in a web-based version called PubMed . SuddenlBut "fingertips" didn't mean "in hand". Medline/PubMed addressed finding what was happening in the exploding world of biomedicine in the form of journal citations, but did not provide the articles themselves. That still required either a personal subscription or access to a medical library. The rapid development of biomedicine also produced a proliferation of scientific journals, many catering to highly specialized branches of basic biology or clinical subspecialty. In 1999, the NIH Director Harold Varmus suggested a combined pre-print/post-print server, but it did not find immediate acceptance, although a beginning was made in early 2000 with the establishment of a post-print  archive called PubMed Central. Starting with articles from only two journals, PNAS: Proceedings of the National Academy of Sciences, and Molecular Biology of the Cell, it now includes the full text of articles from thousands of journals, including this one . All BMThe importance of OA in our field is illustrated by some recent statistics collected by a project supported by the European Commission . The resst century's first decade, the biomedical science community was still hesitant about whether OA publishers would be viewed as "second tier" venues and there was a faint suggestion that paying a processing charge automatically made it into a form of vanity publishing. But the technology and OA publishing movement, which had social and ideological roots, came together with the changing economics of commercial publishing to provide a powerful ally: librarians. This common interest was produced by what has come to be known as "the serials crisis," a chronic escalation of institutional subscription fees far in excess of inflation. A research article is unique and cannot be exchanged for a less expensive version in another journal, making price competition ineffective. The publishing industry was undergoing consolidation, so that by the early years of the millennium, a few publishers were producing a large proportion of all scholarly output.In the early years of the 21Libraries were bearing the brunt of this cost escalation, as serials are their major expense. Any failure of budgets to keep up with rising subscription prices resulted in dropping journal subscriptions. The parent universities were also unhappy, complaining that they were paying twice for the research conducted under their auspices, once for the salaries and resources of their scientists and then again to buy back what they produced. Librarians, with either the full or tacit support of their university administrations, began to push back through active promotion of free access .These economic incentives reinforced a social movement among scientists and other scholars, loosely called the Open Access movement. In 2001, 34,000 international scientists signed an Open Letter to Scientific Publishers calling for "the establishment of an online public library that would provide the full contents of the published record of research and scholarly discourse in medicine and the life sciences in a freely accessible, fully searchable, interlinked form" , began operations in 2000 as one of the first for-profit open access publishers. It now publishes over 200 journals, including this one. Most, but not all, are 100% open access journals where the author retains copyright but a form of public licensing allows free and permissionless access to anyone, providing there is attribution of credit. BMC demonstrated the commercial viability of a publishing model for peer reviewed science where the cost burden was shifted from those seeking access to those producing the science. In essence, the processing charge now covered the much smaller cost of producing the article electronically, much like the "page charge" commonly incurred in publishing in many subscription journals. Whether processing or page charges, publication charges have became just another cost of doing and disseminating research, like making slides or maintaining a service contract for instruments. It is now being routinely included (as a minor item) in grant budgets and represents a much smaller amount than standard subscription costs. A concrete symbol of the established viability of the business model came in 2008 when Springer, the world's second largest commercial publisher of science, technical and medical literature, bought BMC. BMC/Springer is continuing a policy of waiving the processing charge in cases of financial hardship or for scientists in developing countries; this decision is completely divorced from the editorial decision to publish based on peer review for scientific interest and soundness and journal scope.With a range of OA journals available, academic institutions began to support OA publishing, and by June, 2010, 87 institutions, including prominent US universities, now require that researchers publish in OA journals. Funding institutions have a similar interest in having results from the projects they sponsor freely available to the public. Internationally, a total of 44 funding agencies require that results of their sponsored research be published in OA journals. US legislation currently allows a grace period of 12 months for the journals to charge for access, but a proposal to shorten this period has recently been put forward in the US Congress.Environmental Health are mentioned in the news , but even when our papers make news it is not science news, because in an important sense there is no such thing as science news.This rapid historical review brings us to the present and the current milestone: the Impact Factor. The arcane bookkeeping and algorithmic details aside, it is surely a goal of this and every other scientific journal for its research papers to have an impact on science. Frequently papers published in Research results have an impact only by taking their place in a vast body of mutually consistent and reinforcing results. As scientists we produce pieces of a jigsaw puzzle that must be assembled into a coherent picture. Often we don't know where a piece fits, mistake a peripheral piece of the puzzle for a central one, wrongly use the fragment of a pattern as a clue to the whole, miss the meaning of a pattern because we don't see its relation to other pieces, or mistake a piece of one picture for that of another. When we are lucky or inspired we identify adjacent pieces and make them fit together or if we are exceptionally lucky we find a piece that connects whole regions of the puzzle, thus allowing us to see some of the bigger picture. The main point, however, is that it takes time for results to have an impact, the time necessary for them to be carefully put in relation to other results. Scientific research can't be "news" any more than a puzzle piece is news. It must take its place among the other puzzle pieces and be used and appreciated by the other puzzle players. For a result to be science, it has not just to be understood by a scientist -- understanding is something an individual does -- but it has to be intersubjective, something that can be shared. Publication in scientific journals is the principal means by which this sharing takes place. At least it does at this moment in time.We have added this last qualification because this seems to be a particularly labile and uncertain period in the history of sharing and communication between people, indeed a period of profound transition, likely to be of as much historical significance as the transition from handwriting to printing via moveable type. Even before the internet, in 1962, Marshall McLuhan coined the phrase "global village" for the effect of the uniform perspective produced by printing, a format which produced multiple and identical copies of the same text that imposed the fixed view of an "author" upon a local ("village") mindset . While oEnvironmental Health, has an added advantage: We can follow the numbers of downloads occurring at our website. As articles can also be downloaded from the PubMed Central server at the US National Library of Medicine, we count only the approximately 50% that occur directly from our own website. This information is available with the top-20 list of most frequently accessed articles at our website, and authors can track the numbers of downloads their own articles have received. The all-time top-20 list shows highly popular articles, all of them downloaded over 10,000 times from our journal server.An electronic OA journal, such as From a modest start in 2002, the first article published was only accessed 49 times during the following month. Less than a year later, we had over 1,000 downloads per month, and the total number of downloads in May, 2010 was 48,988. However, there are now 289 published articles available OA. As most downloads usually occur within the first year after publication, we therefore generated a graph that shows the total number of downloads month by month divided by the total number of articles published during the previous year , both the control and financing of scientific and other information dissemination has shifted dramatically, leaving conventional print publishers of periodicals and newspapers floundering as they try to adapt to a new and difficult economic landscape.In this new land it is likely that both the meaning and means of "having an impact" will change and the degree and way in which scientific journals will be "factors" in that impact will change with them, perhaps in dramatic and unforeseen ways. While we have reached one kind of milestone in the life of what is now a mature and established scientific periodical that uses an emerging and increasingly dominant form, the journey is only begun. No one knows what the road ahead looks like, where it leads and what will be considered "milestones" as we move forward from here.So far, it's been an adventure.BMC: BioMed Central; IF: Impact Factor; ISI: Institute for Scientific Information; MEDLARS: Medical Literature Analysis and Retrieval System; OA: Open Access.DO and PG are founding editors-in-chief of Environmental Health.DO drafted the first version of the manuscript, and both authors contributed to and approved the final version.


The epidemiology of leprosy is characterized by heterogeneity in susceptibility and clustering of disease within households. We aim to assess the extent to which different mechanisms for heterogeneity in leprosy susceptibility can explain household clustering as observed in a large study among contacts of leprosy patients.We used a microsimulation model, parameterizing it with data from over 20,000 contacts of leprosy patients in Bangladesh. We simulated six mechanisms producing heterogeneity in susceptibility: (1) susceptibility was allocated at random to persons , (2) a household factor,  a genetic factor (dominant or recessive), or  half a household factor and half genetic. We further assumed that a fraction of 5%, 10%, and 20% of the population was susceptible, leading to a total of 18 scenarios to be fitted to the data. We obtained an acceptable fit for each of the six mechanisms, thereby excluding none of the possible underlying mechanisms for heterogeneity of susceptibility to leprosy. However, the distribution of leprosy among contacts did differ between mechanisms, and predicted trends in the declining leprosy case detection were dependent on the assumed mechanism, with genetic-based susceptibility showing the slowest decline. Clustering of leprosy within households is partially caused by an increased transmission within households independent of the leprosy susceptibility mechanism. Even a large and detailed data set on contacts of leprosy patients could not unequivocally reveal the mechanism most likely responsible for heterogeneity in leprosy susceptibility. Mycobacterium leprae, was detected in a quarter of a million people in 2008, and many more people are living with impairments caused by this disease.Leprosy, caused by infection with Clustering of leprosy patients within households, families, and neighborhoods has been reported many times.Association between genetic elements and leprosy susceptibility has been suggested previously.et al.e.g. household member, neighbor, or social contact. This result indicates that the risk of family members might be caused by a common (but yet unknown) risk factor in a household, such as poverty, which in Brazil has been shown to be a risk factor for leprosy.Another mechanism determining heterogeneity in leprosy susceptibility may explain the observed clustering of leprosy in household contacts. In Moet et al.M. leprae. Using a newly-developed microsimulation model, we attempt to distinguish between mechanisms causing heterogeneity in leprosy susceptibility. The study of Moet et al.In this study, we analyze the data from the study by Moet e.g. helminthic parasitesWe used microsimulation modeling, a technique in which life histories of fictitious individuals are simulated. Individual humans are the unit of modeling, and dynamics at the population level are obtained by aggregation of all individuals. Microsimulation has been employed for studies of infectious diseases with complex natural histories or complex patterns of individual contacts, M. leprae in a population divided into households, and the development of leprosy by infected individuals. The model is based on and parameterized for the populationCOLEP and details of parameterization, see the Supporting Information Our model, called SIMCOLEP, simulates the spread of Demography is described by birth, death, and movement between households. A life table determines the life span of an individual. At birth, individuals are placed in the household of their mother, and individuals can move from one household to another existing or newly-created household during their lifetime. Individuals move at marriage, or during adolescence.popc (contacts per year), multiplied by the probability of infection during a contact, i.e. the infectivity. Additionally, within a household containing one or more infectious individuals, each susceptible household member is infected at the minimum of times until an infectious contact from each infectious individual in the household. The timing of these infectious contacts is determined by the rate hhc (contacts per year), and the infectivity. These two contact rates, popc and hhc, are estimated by fitting the model to the data.Transmission occurs due to direct contact with infectious individuals. An infectious individual makes infectious contact with random individuals in the population at rate et al.popc and hhc, the infectivity determines the rate at which new infectious contacts are made. Self-healing infections are never infectious, and proceed to the recovered state at the end of the symptomatic period. Both chronic and self-healing leprosy can be detected while symptomatic, subsequently treated, and cured.The natural history of the infection, schematically shown in We mimic the leprosy situation in the Nilphamari and Rangpur districts and thus also the control programs. Treatment becomes available in 1970, after which the average detection delay decreases from 12 years to 2 years in 1990.i.e. six mechanisms multiplied by three fractions of susceptibles – are fitted to data.We model a population in which a small fraction  is susceptible to leprosy. The majority of the population is not susceptible; these individuals do not develop symptoms and are never infectious. Allocation of susceptibility and the type of leprosy  follows one of six mechanisms, which will be explained in more detail below, and is summarized in Random”. In the second mechanism, indicated by “Household”, the inhabitants can be susceptible in 25% of the householdsRandom and the Household mechanisms, 80% of susceptibles display self-healing leprosy (determined by chance), and the remaining 20% develop the chronic type. The third and fourth mechanisms are genetic: Mendelian inheritance of one gene determining leprosy susceptibility, and a second gene determining leprosy type . We consider two mechanisms where either both genes are dominant (“Dominant”), or both genes are recessive (“Recessive”). Finally we considered two mechanisms in which a household factor is combined with a dominant or a recessive genetic factor (“Household & dominant” and “Household & recessive”). Half of leprosy susceptibility is caused by a genetic mechanism, and the other half is due to living in a susceptible household.The simplest mechanism causing heterogeneity in leprosy susceptibility is random distribution of susceptibility over the population. We will indicate this mechanism with “popc and hhc contact rates within an 11 by 11 parameter grid, we calculate the log-likelihood of the outcomes of 100 simulation runs for the leprosy data in 2003.The data used for fitting the model are the new case detection rate , the prevalence of cases among contacts for different household sizes, and the prevalence of cases among different classes of relatives.popc. This rate plays the predominant role in fitting the new case detection data (data not shown). Two scenarios, the Random mechanism with 5% susceptibles in the population, and Dominant with 20% of the population susceptible, could not be fitted to the data. We observed that for Random, the within-household transmission rate, hhc, and the contact rate in the population, popc, are high compared to the other mechanisms in which susceptibility is clustered within households .Even though the mechanisms provide a comparable overall fit to the data, there are substantial differences in which aspects of the data are fitted best . For exasize six . The dissize six . The HouHousehold and Random. The mechanisms that combine Household and the genetic mechanisms take an intermediate position.For all six mechanisms, the current decrease in new case detection of leprosy is predicted to continue over the next decades . The decDifferent mechanisms for heterogeneity of leprosy susceptibility can explain the observed clustering in household contacts of leprosy patients. The fit to aspects of the data – new case detection rate, household size, or relationship – depends on the assumed mechanism for heterogeneity in leprosy susceptibility. The predicted future decline in the new case detection rate also depends on these mechanisms. For this study, we had access to a large and detailed data set on clustering of leprosy within households,The most comprehensive dataset available on the contacts of leprosy patients was used in our study and even with this data and a very elaborate model it was not possible to distinguish between different mechanisms of leprosy susceptibility. One reason could be, that even in this large dataset among contacts only 43 new previously undiagnosed patients were present. Furthermore, the household factor is a great unknown in our model. This factor can be a very complicated and maybe time dependent, such as episodes of food shortage and malnutrition. Our model results indicate that in-depth studies  or large-scale human genetic and heredity studies could elicit more precisely the nature of the heterogeneity in susceptibility for leprosy.Our model only takes into account ‘within-household’ and ‘population level’ transmission. Especially the population level contact structure is simplified extensively. Population contacts are modeled in such a way that every individual has the same chance to encounter every other individual in the population. Of course in reality due to social structures and physical boundaries the contacts will focus on smaller groups. The effect on the epidemiology of leprosy might be that certain households have a higher probability of coming into contact with an infected household. However, because of the low prevalence of infection, the contribution to the clustering of leprosy in households, which was the objective of this study, is very limited. Incorporating such social structures is a challenge.Although the awareness campaigns in Northwest Bangladesh have lifted much to the stigma surrounding leprosy, contact behavior of symptomatic patients might change. It would be expected that contact rates are smaller. This could be modeled in the given framework by introducing a reduction in transmission probabilities from the moment of becoming symptomatic. Nevertheless, the outcomes are not likely to change much, because the majority of infections occur in the asymptomatic phase.Random) requires that both transmission parameters are much higher than for the other mechanisms. A short time until infection within household, i.e. a high hhc, means that most susceptible household contacts will be infected relatively quickly after becoming a household contact. Without a mechanism that clusters susceptibility in a household, a high probability of infecting susceptible household contacts is needed to obtain the appropriate number of household contacts with leprosy. For the Random mechanism, popc is substantially higher than for the other mechanisms, which is in concordance with the existing theory that clustering of susceptible individuals in households increases the endemic level of infectious diseases with an equal transmission rate.Our model shows that not assuming an explicit mechanism for susceptibility . Combining the scenarios produces results between the Household and genetic mechanism outcomes, with overall good fit, perhaps suggesting that multiple factors determine susceptibility to leprosy. We have fixed the contributions of each mechanism to 50% Household and 50% geneticIn contrast, the disease prevalence peaks in moderately large households for the genetic mechanisms . In thesRandom and Household mechanisms. These mechanisms differ considerably; the Random susceptibility is not clustered in households by an explicit mechanism, while the Household mechanism strongly clusters susceptibility. These equally fast declines are explained by our choice of 20% susceptibles in the population for Random, whereas we chose 10% for the other mechanisms. The difference in speed of decline between the Household mechanism and the genetic mechanisms can be explained by the consequences for contact tracing, which, together with self-reporting, is the only way to detect leprosy in the model. For example, while the genetic mechanisms have a higher prevalence among siblings than the non-genetic mechanisms, these siblings will marry other people and form a household of their own, possibly escaping detection. Furthermore, the Household mechanism predicts a high prevalence among spouses, who are likely to be picked up by contact tracing.In the study areaIn conclusion, in this study we have demonstrated that analysis and modeling of a large and detailed data set on contacts of leprosy patients could not unequivocally reveal the mechanism for the heterogeneity in leprosy susceptibility that is responsible for the clustering of the disease in households.File S1SIMCOLEP, model description and parameterization. Full description of the SIMCOLEP microsimulation model and parameterization.(2.49 MB PDF)Click here for additional data file.


Proximity to parks and physical activity sites has been linked to an increase in active behaviors, and positive impacts on health outcomes such as lower rates of cardiovascular disease, diabetes, and obesity. Since populations with a low socio-economic status as well as racial and ethnic minorities tend to experience worse health outcomes in the USA, access to parks and physical activity sites may be an environmental justice issue. Geographic Information systems were used to conduct quantitative and qualitative analyses of park accessibility in New York City, which included kernel density estimation, ordinary least squares  regression, geographically weighted  regression, and longitudinal case studies, consisting of field work and archival research. Accessibility was measured by both density of park acreage and density of physical activity sites. Independent variables included percent non-Hispanic black, percent Hispanic, percent below poverty, percent of adults without high school diploma, percent with limited English-speaking ability, and population density.a2 = .11 and .23, respectively; AIC = 7162 and 3529, respectively). Geographically weighted regression, however, suggested spatial non-stationarity in both models, indicating disparities in accessibility that vary over space with respect to magnitude and directionality of the relationships . The qualitative analysis supported the findings of the local regression, confirming that although there is a geographically inequitable distribution of park space and physical activity sites, it is not globally predicted by race, ethnicity, or socio-economic status.The ordinary least squares linear regression found weak relationships in both the park acreage density and the physical activity site density models (RThe combination of quantitative and qualitative analyses demonstrated the complexity of the issues around racial and ethnic disparities in park access. They revealed trends that may not have been otherwise detectable, such as the spatially inconsistent relationship between physical activity site density and socio-demographics. In order to establish a more stable global model, a number of additional factors, variables, and methods might be used to quantify park accessibility, such as network analysis of proximity, perception of accessibility and usability, and additional park quality characteristics. Accurate measurement of park accessibility can therefore be important in showing the links between opportunities for active behavior and beneficial health outcomes. Environmental justice is the fair and equitable distribution of both the environmental "bads," such as hazardous waste sites, and the environmental "goods," such as parks, open space, and recreational opportunities. For more than a decade, Geographic Information Systems (GIS) have been used to examine the spatial realities of environmental justice -11. GIS Previous studies have documented that proximity to parks and open spaces has a positive influence on engaging in active behaviors, like walking and running for exercise -19. OtheIf environmental factors help us to understand the distribution of health outcomes in the population, then one might expect that active outdoor space would be less available to populations with overall worse health outcomes. Since low SES populations and racial/ethnic minorities experience worse health outcomes in the USA ,28, acceMany researchers have endeavored to evaluate access to parks and recreational facilities, and have used various methods and measures to do so. However, there are many pitfalls in developing a measure or index of accessibility, and even the more sophisticated analyses have some problems in matching their indices with reality. Research on this topic has grown more nuanced in recent years, but many of these analyses still present some methodological difficulties, which may call into question their findings, especially considering that many of these studies show significantly contradictory results , rather than using or developing an actual proximity measure such as Estabrooks, et al, 2003 . In thisThis may be problematic in arriving at an accurate depiction of park access. For example, a person may live directly next to a park, but if the park is located in a different unit of aggregation , it will not be counted as "accessible" for that person. Additionally, populations that are distributed heterogeneously across a large areal unit may also introduce error in estimating park access. A population that is distributed heterogeneously within the areal unit may also introduce error in estimating park access, since, especially with larger areal units, the population may be concentrated in portions of the geographic unit that are not in close proximity to the park, although still within the same unit  or 1/2 mile (~800 m) as a standard walking distance ,33. HoweThere have been a few studies using the street network to calculate distance to parks, for instance, Nicholls (2001) and Talen and Anselin (1998) each of whom compared straight-line distance with street network distance ,35. The Kernel density estimation, or kernel smoothing, is another method for measuring accessibility. "Kernel density estimation involves placing a symmetrical surface over each point, evaluating the distance from the point to a reference location based on a mathematical function, and summing the value of all the surfaces for that reference location. This procedure is repeated for all reference locations."  Kernel dKernel density estimation is typically considered a more refined spatial statistical model than the container approach. It can give an estimation of accessibility for every point in the study area, not just a binary answer of "within walking distance" or "not within walking distance," as in both the fixed-distance proximity and the network analyses. There have been very few studies of park access using the kernel density method, although this method has been used extensively in other types of analyses ,38. MoorThis analysis uses the kernel density estimation approach to test whether access to park space is associated with neighborhood race/ethnic composition and SES in New York City. Densities of both park acreage and physical activity sites are mapped and correlated with SES measures. The two main categories of data used in this analysis were park information and socio-demographic information.The park extent data was created by the New York City Department of Parks and Recreation and represents all land owned by the Parks Department as polygons (see figure The park features data (elements within the parks) were created through a collaboration between the New York City Department of Parks and Recreation and Lehman College of the City University of New York. Researchers traveled to all of the New York City parks carrying portable GPS units and recorded the locations of many of the parks' features, including items such as drinking fountains, comfort stations (rest rooms), flag poles, stairways, historical markers, statues, beaches, courts, ball fields, and other recreational areas . Otherwise, that pixel would have been given a value of '0' ('no park'). This grid was then converted into a statistical surface using the kernel density technique. This method involves a kernel function, which uses a moving window to apply differential weights to objects based on proximity. Thus objects that are close are weighted more heavily than more distant objects. Based on a sensitivity analysis, the bandwidth parameter for the kernel function was assigned a 1.6 km (1 mile) radius. This sensitivity analysis tested three different kernel sizes . Empirically, the one mile kernel bandwidth explained more of the variance in the model than the other bandwidths. This distance was also determined to be an appropriate kernel bandwidth for defining a feasible walking distance for park accessibility based on other research . The kerTo prepare the data layer for physical activity sites, each discrete non-linear park feature that was identified as activity promoting  was extracted from the main parks database, converted to a point (if necessary) and given a value of '1' . The features that were coded are: basketball courts, handball courts, tennis courts, volleyball courts, multipurpose courts, soccer fields, baseball fields, football fields, swimming pools, hockey rinks, golf courses, and running tracks. Kernel density estimation was again performed, this time in order to estimate the density for physical activity sites in New York City. A 1.6 km radius was used as the bandwidth and a 50 meter resolution raster surface was created (see figure These two density surfaces were used as proxies for access to park space (acres) and active recreation , following the assumption that where there are higher densities of resources, access is greater.In order to evaluate the possibility of unequal access to these park measures based on socio-demographic characteristics of the population, information was gathered from summary file 1 (SF1) and summary file 3 (SF3) of the 2000 USA census at the block group level. The measures included in this analysis were percent non-Hispanic black, percent Hispanic, percent of adults aged 25 years and older with no high school diploma, percent below poverty, percent who do not speak English well or do not speak it at all, and population density  to the same geographic unit as the socio-demographic data.Two discrete statistical methods were used on the data: ordinary least squares linear regression (OLS) and geographically weighted regression (GWR). The datasets used in the analyses were identical. Census block groups with fewer than 256 residents were excluded (lowest 5%) in order to stabilize the model. Block groups that had missing data from any of the variables were also excluded (~ 0.1%). After the data were cleaned and prepared, 5,439 block groups out of the original 5,732 remained (94.9%). Log10 transformed park acreage density (ACRE) and log10 transformed physical activity site density (PAS) were used as the dependent variables. The independent variables included: percent non-Hispanic black, percent Hispanic, percent of adults with no high school diploma, percent below poverty, percent with limited English language ability, and population density.When analyzed globally (with OLS) it appears that PAS and ACRE behave differently with respect to the independent variables  by running many iterations of the model, was used to determine the optimal number of nearest neighbors for the regressions. This adaptive kernel, rather than a fixed bandwidth, was chosen in order to reduce edge effect since we do not have detailed park data for the areas outside New York City. Although this method ensures a sufficient local sample size for all regression points, it can result in unstandardized geographic sizes for individual regression points (particularly near study boundary edges) which can lead to overly smoothed results for those areas. Although this is certainly a limitation, it may not be critical in this study since we were interested in examining the potential environmental justice issues regarding New York City residents and New York City parks only, that by necessity must be confined to the boundaries of the city's jurisdiction. The adaptive kernel method resulted in the utilization of 271 nearest neighbors for the PAS model and 279 nearest neighbors for the ACRE model. Although these samples are certainly large enough for a stable model, they represent only approximately 5% of the original data, suggesting that the relationships are quite local . The local nature of these relationships is further confirmed by a Monte Carlo test for spatial variability, which was executed within the GWR3 software where the spatial variability of all the parameters, with the exception of percent of adults without a high school diploma in the PAS model, were shown to be significant  was lower for the GWR models when compared with the global (OLS) models, suggesting that the former perform better than the latter. Model parameter summaries are provided in tables Geographically Weighted Regression (GWR) is a technique developed by Fotheringham, Brunsdon, and Charlton designed to quantify locally varying relationships among data, rather than the more common global relationships . These local relationships may vary over space therefore accounting for any potential spatial non-stationarity. In other words, the measurement of the relationship may be partially dependent upon where the measurement is taken. Fotheringham, et al 2002) suggest several reasons for locally varying relationships, such as sampling variation, a misspecification of the model , or simply a relationship which intrinsically varies over space . GWR is 002 suggeSince GWR allows the relationships to fluctuate, it can be difficult to summarize or conceptualize concisely via tables or graphs. As such, maps have been created that illustrate the variability of the relationships between the independent and dependent variables for both models  to be used as a park. In 1905, the park was extended south, and by 1908 the park was extended west and set the boundaries that remain today. By 1908 the park already included football fields, baseball fields, tennis courts, several gardens, footpaths, and park structures. Situated among a chain of seventeen mid-19y Belt." Highland Park's location is unique. A large portion of the park sits high atop the glacial moraine that runs diagonally from the southwest to the northeast through New York City, while the remainder of the park includes a steep slope that leads to a narrow piece of the park at the bottom of the moraine  , racial th century (1910 census), as shown in figures From 1850 to 2000 both areas were transformed from rural farmland to dense urban areas. In the beginning of the 20entury 19 census, A comparison of socio-demographic characteristics between the two study areas based on the more nuanced categories available in the 2000 census shows some differences  analysis of accessibility may not be the appropriate analytic method for this data. A limitation of the qualitative analysis is that only two case study park areas were examined, which is not likely to be representative of all the parks in New York City.The variability of the quantitative and the qualitative results suggests a number of potential limitations and shortcomings of our analyses. These limitations are discussed in detail below.As noted earlier in this paper, there are many pitfalls and problems in developing a meaningful park accessibility measure, and our analyses have reinforced the need for a more comprehensive approach. A number of additional variables might be included when measuring park access, in order to potentially establish more definitive results. In addition, our analysis focused on park access in New York City, which may or may not conform to the realities of other geographies.Access based on proximity or distance often does not take into account the actual street network, as noted earlier, and there may be a major highway or other barrier between the residents and the park. This was shown in our qualitative analysis of Highland Park, which is essentially cut off from the residential neighborhood on the northern side by large stretches of cemeteries and highways. Utilizing a network analysis may prove to be more realistic than the kernel estimation that we used, that quantified park access as a function of density of park acres and/or physical activities sites.A measure of access also needs to consider actual points of entry to the park. For some parks, there are entry points that may be at a far remove from the residential neighborhoods, even though the park may border the neighborhood. This can also be addressed using network analysis provided that the entry points are known and mapped.Most accessibility measures do not take into account perceptual access. For example, racial/ethnic minority residents might not feel welcome in a park or recreational facility used predominantly by non-Hispanic white individuals. Similarly, female park users might not feel welcome in male-dominated physical activity sites such as basketball courts.Perceptual access can also be based on park cleanliness or perceived or actual crime within or near the park. A park may be in close proximity but unused due to the bad conditions within the park. These "incivilities," such as graffiti, broken glass, liter, or evidence of drug or alcohol use, or the presence of violent crime could be important factors in making the park unattractive for use. While it is difficult to include perceptual access into a measure or index, if data on park maintenance and crime rates are available, they could be included in an analysis. Additionally, other more qualitative methods, such as interviews and surveys of local residents, as well as cognitive  mapping and participatory GIS, can be used to discern attitudes and perceptions about access to parks and physical activity sites.Most measures of accessibility do not consider park characteristics, either, such as types and quantities of activities available, or park size. A tiny vest-pocket park will not have as much to offer in the way of physical activity potential as a large park. Although our study did incorporate park acres and number of activity sites, it could be improved by looking at variation of the types of physical activity sites, . Presumably, a park having a greater variety of types of physical activity sites would make the park more of a draw to residents of different ages, genders, and physical fitness status, and therefore that park would merit a higher rating.Another park characteristic that may be taken into account is the requirement of permits in some physical activity sites. For instance, permits are required to use the tennis courts in all parks. Since these permits have fees, access is limited based on financial ability.Resources available in parks and physical activity sites tend to be team sports-related, making it is less likely that older adults make use of these facilities. Therefore, the results of using physical activity sites as an important metric of accessibility may be misleading and skewed toward younger populations.Additionally, people living in suburban-like areas within the city may have access to private open space, like backyards, usually available in single-family home neighborhoods, but not in higher-density, inner-city communities. Therefore proximity to public parks may be less important in those suburban-type areas, making any direct comparison of park and physical activity site accessibility between various types of neighborhoods and populations inaccurate. Public parks may serve a more critical function and there may be a higher need for public open space in less affluent neighborhoods, so statistical measures of equity regarding park access may not tell the whole story.A valuable data source that was not explored in our qualitative analysis was comprehensive interviews with residents of the study areas to better understand actual and perceptual park accessibility. This could potentially provide important information necessary to address many of the limitations mentioned above.st century New York City, where a very high proportion of the population is foreign-born, and less likely to be counted in the census, especially in the case of illegal immigrants who may be mistrustful of government and wish to remain unknown to them. The temporarily or permanently homeless also comprise a significant population that is traditionally undercounted, as well as populations who may rotate their domicile and are therefore often overlooked in the official count because they are not thought of as being a permanent part of the respondent's household.A major limitation of population studies such as this one is the necessarily heavy reliance on data from the census. While census data is the most complete and current dataset we have at any given time, we need to acknowledge several underlying problems with its accuracy. One of the most serious sources of inaccuracy is the potential for undercounting populations in poor and immigrant communities. This has been an on-going drawback throughout the United States, but is even more pronounced in 21Additionally, each decennial census defines racial and ethnic categories differently, making cross-census comparisons difficult for longitudinal studies. The guidelines and standards for racial and ethnic classification were revised by the Office of Management and Budget in 1999, and the 2000 census uses a markedly different classification system from the previous censuses, making the findings of longitudinal studies somewhat unreliable.It is generally acknowledged that access to parks and physical activity sites has beneficial health ramifications, so a better understanding of which populations have good access to these areas will assist in identifying and targeting those areas that do not, and the potential for more fully explaining disparities in health outcomes. It is important to recognize the environmental justice implications of park and physical activity site location and spatial distribution, since ethnic and racial minorities and poor people tend to suffer disproportionately from diseases which are often preventable by proper exercise.While we did not find an overall environmental justice impact for New York City as a whole with regard to park access and socio-demographic indicators, we know that there are many sections of the city with poor access to parks, and therefore this needs to be examined on a very local level rather than globally.The level of need also has to be taken into account because even if parks were distributed evenly throughout the city, some neighborhoods warrant having additional resources. This may be due to the fact that these neighborhoods are more densely settled than other more suburban-like parts of the city, and their populations do not likely have additional open space resources such as backyards or options to leave the city for recreational opportunities. These are the very populations for whom parks assume an even more critical function than typically provided, and the parks and recreational facilities in these areas perhaps deserve extra resources and funding commitments in order to provide the equivalent level of support.Physical, cultural, and perceptual barriers should be taken into account also when measuring access to parks. Even though distances may appear short, true access cannot be gauged through Euclidean measures, and much more research has to be done on what constitutes true access and equity of resources.The combination of quantitative and the qualitative analyses revealed trends that may not have been otherwise detectable. While the OLS  regression showed a weak relationship between SES characteristics and park accessibility, the geographically weighted regression  found "unpatterned inequality." The qualitative analysis did not reveal anything that would refute the statistical findings of the GWR analysis, as both neighborhoods were confirmed to be different from one another in terms of SES characteristics, although the park conditions and useable park area were comparable. The qualitative analysis did, however, suggest that an approach which considers physical barriers and some of the other variables listed in the limitation section would improve the model and better reflect reality. The qualitative analysis also showed that although the demographics around the study areas were similar at the times of the parks' construction, they have changed considerably since then, allowing for the possibility of environmental justice impacts. These environmental justice impacts may introduce disparities by influencing health outcomes and behaviors.The complexity of the issues around racial and ethnic disparities in park access has been demonstrated further by this study. Looking at one factor at a time is likely to result in misleading findings. Therefore, a more complex model that accounts for as many different types of variables as possible  will be needed to develop a more accurate measurement of park accessibility, particularly as to how it might mediate environmental justice and mitigate negative health outcomes.The authors declare that they have no competing interests.AM designed and implemented the quantitative analysis, conducted exploratory data analysis and visualization of data, assembled and processed spatial datasets, created maps and diagrams, aided in the qualitative and longitudinal designs and analyses, and contributed to the writing and editing of the paper. JM conceived of the project concept, assisted in the development of the research design and analysis, supervised the quantitative and qualitative analyses, performed background research, and contributed to the writing and editing of the paper. NS helped with the data analysis and design, background research, and contributed to the writing of the paper. KG conducted the qualitative analysis, longitudinal demographic analysis, field work of case study areas, and archival research, and created map figures for the qualitative analysis. PA helped develop the project concept and contributed to writing and editing of paper. All of the authors have read and approved the final manuscript.


As impact of literature concerning this subject is scarce, the objectives of this study were to assess whether the Health Related Quality of Life (HRQoL) is decreased in patients with painful temporomandibular disorders as compared to the HRQoL in the general population, and to evaluate to what extent pain duration affects HRQoL.Data concerning physical and mental health were retrieved from patients with painful temporomandibular disorders. Assessment tools used were: the Mandibular Function Impairment Questionnaire (MFIQ), the Short-Form-36 (SF-36), the Hospital Anxiety and Depression Schedule (HADS), and the General Health Questionnaire (GHQ). In order to examine the influence of the duration of pain on HRQoL, the total sample was divided into three different subgroups. Subgroup 1 consisted of patients with complaints existing less than one year. Patients with complaints from 1 to 3 years were allocated to the second group. The 3rd subgroup included patients with complaints longer than 3 years.The total sample consisted of 95 patients . On most physical and social functioning items, groups 2 and 3 scored significantly worse than the general population. On the other hand, none of the groups differed from the general population when comparing the mental items. Duration of pain was significantly correlated with SF-36 subscale physical functioning and the mandibular impairment.Patients with TMD pain less than one year score better than compared to the population norm. With a longer duration of pain, mental health scores and role limitations due to emotional problems do not appear to be seriously affected by reduced physical health, while social functioning appears to be considerably affected. Temporomandibular disorders (TMDs) comprise a group of disorders that affect the temporomandibular joint (TMJ), the masticatory muscles, or both. TMDs involve musculoskeletal pain, disturbances in the mandibular movement patterns, and/or impairment in functional movement . Pain isIt is generally accepted that quality of life is negatively affected by chronic pain ,8. The iMurray et al.  describeLeResche et al.  studied It is not clear whether and, if so, how (chronic) pain related to TMDs influences HRQoL, and whether pain duration is of influence. It may be hypothesized that when pain has just begun, this will mainly affect physical factors such as physical functioning. When the pain lasts for a longer period, and treatment so far has failed to relieve the pain, it may start to have more impact on the emotional behaviour, social factors and HRQoL. However, whether this is the case is yet not clear. Information concerning the influence of pain and its persistence on HRQoL may guide (the emphasis of) treatment in these patients. Therefore, the aims of this study were to assess whether the HRQoL is decreased in orofacial pain patients as compared to the general population, and to study the effect of duration of pain on HRQoL.rd subgroup consisted of patients with complaints longer than 3 years. Secondly, the influence of the duration of pain was studied using results of the total sample in regression analysis. During their first visit to the clinic, patients were informed about the study and the content of the questionnaires. When patients were willing to participate, they were requested to fill in an informed consent.Patients were recruited from the department of Oral and Maxillofacial Surgery of the University Medical Center Groningen. The group consisted of 95 patients consecutively consulting the TMD/Orofacial Pain section for their orofacial pain problems. The inclusion criteria were age over 16 years, no language barrier, and the presence of a painful temporomandibular disorder as classified according to the RDC/TMD ,16. FromQuality of life has been described by the World Health Organisation as " ... an individual's perception of their position in life in the context of the culture and value system of which they live with the relation to their goals, expectations, standards and concerns." This concept incorporates different aspects of individuals, including physical health, psychological state, level of independence, social relationships, personal beliefs and their relationship to salient features of the environment .During the second visit to the clinic, patients were instructed how to complete the questionnaires. Subsequently, patients were left alone to complete the questionnaires. When necessary, unclear test items could be clarified by the interviewer. Only Dutch versions of the questionnaires were used.physical functioning , role limitations due to physical health problems , bodily pain (BP), general health (GH) perceptions, vitality , social functioning (SF), role limitations due to personal or emotional problems (RE measuring the effect of the emotions on work and daily activities) and general mental health (MH including anxiety and depression). The scores on every subscale range from 0-100, with higher scores indicating better health states. Reference values were used to compare the results of the group in question. The reference values were taken from a Dutch study, which consisted of a random Dutch sample of 1742 persons, which is used as the reference group . To . To 6]. The first group did score significantly higher ("better") on the scale "physical functioning", compared to the population norm. This finding could be explained by possible underestimation of the physical situation, when patients visit a hospital and are allocated to a study group that answers questions about health. Patients with pain or function problems that have arisen within the last year, may tend to focus on these problems in an opposite manner than patients who have a longer experience with these problems. In addition, the scale bodily pain was not significantly different from the reference value. Because of the relatively short existence of pain, patients may underestimate or underrate the consequences of their disorders on the measured scales. They may be convinced that (pain) symptoms will be transient and, therefore, patients will not allow them to affect the physical items. Patients may also feel the need to convince the doctor that the symptoms are purely physical, and want to display that statement in the answers of the subscales.bodily pain", "general health" and "vitality") the second and third group scored significantly worse than reference values. In addition, in the third group the items physical functioning and role physical were also worse than reference values. This physical impairment was confirmed by the FIRS scores, indicating moderate function impairment. So the mandibular function was lowered in all three groups. The GHQA score, which represents the somatic general health, is significantly lower ("worse") in the third group, which is in accordance with the scores on the SF-36. With significantly lower scores on the physical scales of the SF-36, the second and third subgroup did not score significantly lower on the mental scales.By contrast, on SF-36 physical health items  in group 2 is not only higher compared to group 1, but also compared to group 3. Patients who experience complaints for a short time may not be seriously affected, but when pain persists, psychological distress will be more pronounced. Later, when patients are used to the pain or when they are sufficiently reassured about their health status, the psychological distress will return to lower values again. This is in accordance with the score on the mental health item of the SF-36, which is better in the third group compared to the second group.Between the different pain duration groups, statistically significant differences were found only on a few scales. Comparing "better" scores from the first group with "worse" scores from the third group, leads in the According to our findings, it may seem that patients with a shorter duration of pain seem to underrate their physical impairment or at least do not consider it to be relevantly impaired, as the scores are "better" compared with a healthy reference group. Patients with longer lasting pain at least longer than one year, have more pronounced physical problems. The role limitations due to emotional problems or the mental health seem to be hardly affected, however. It has been suggested that psychological functioning is merely related to patients' beliefs and coping strategies rather than to the physical impairment . On the physical functioning and the mandibular impairment (FIRS). So with a longer duration of pain, the somatic well-being is considered worse. It remains unclear whether the physical discomfort has worsened during its existence or whether the discomfort is only rated worse due to its longer existence. Other subscales and other scores did not show significant correlation with duration of pain, which may be explained by a large range of duration of pain in contrast to the smaller scale range of the scores on other subscales and the other questionnaires.In addition to the analysis with three subgroups, we calculated Pearson correlation coefficients with data from all patients. This revealed a significant correlation between the whole range duration of pain with the subscale One factor that may be of influence on the results is the age. In our sample, no difference was found between the three groups. Besides TMD pain, other pain condition could play a role in HRQoL. Of the studied sample totally five had an accompanying chronic condition. In the second group, two suffered also from rheumatoid arthritis. In the third group, one patient suffered from rheumatoid arthritis, one from hernia and one from low back pain. These conditions could have influenced the questionnaire outcomes, although the number of patients is a slight minority compared to the total sample size, therefore we argued this to be of negligible influence.A limitation of this study could be the large female predominance, which is than in the general population, which may hamper the generalizability of the results. However, a predominance of female gender in TMDs is also found in epidemiological research . In addiIn patients with chronic pain conditions, such as most TMD pains, it has been demonstrated that psychological factors are better predictors of treatment outcome on the long-term than physical findings are ,28. WhenThe authors declare that they have no competing interests.GHET conceptualized and designed the study, acquired the data and participated in data analysis, and drafted the manuscript;JJR, ETV, BS contributed in data analysis and participated in revising the manuscript critically for important intellectual content.All authors read and approved the final manuscript.


Mobile health clinics provide an alternative portal into the healthcare system for the medically disenfranchised, that is, people who are underinsured, uninsured or who are otherwise outside of mainstream healthcare due to issues of trust, language, immigration status or simply location. Mobile health clinics as providers of last resort are an essential component of the healthcare safety net providing prevention, screening, and appropriate triage into mainstream services. Despite the face value of providing services to underserved populations, a focused analysis of the relative value of the mobile health clinic model has not been elucidated. The question that the return on investment algorithm has been designed to answer is: can the value of the services provided by mobile health programs be quantified in terms of quality adjusted life years saved and estimated emergency department expenditures avoided?Using a sample mobile health clinic and published research that quantifies health outcomes, we developed and tested an algorithm to calculate the return on investment of a typical broad-service mobile health clinic: the relative value of mobile health clinic services = annual projected emergency department costs avoided + value of potential life years saved from the services provided. Return on investment ratio = the relative value of the mobile health clinic services/annual cost to run the mobile health clinic.Based on service data provided by The Family Van for 2008 we calculated the annual cost savings from preventing emergency room visits, $3,125,668 plus the relative value of providing 7 of the top 25 priority prevention services during the same period, US$17,780,000 for a total annual value of $20,339,968. Given that the annual cost to run the program was $567,700, the calculated return on investment of The Family Van was 36:1.By using published data that quantify the value of prevention practices and the value of preventing unnecessary use of emergency departments, an empirical method was developed to determine the value of a typical mobile health clinic. The Family Van, a mobile health clinic that has been serving the medically disenfranchised of Boston for 16 years, was evaluated accordingly and found to have return on investment of $36 for every $1 invested in the program. Since the 1980s, disparities in health outcomes among many US sub-populations have been well documented. The primary predictors of inadequate healthcare are racial and/or ethnic minority status, female gender, rural residence and extremes of age . Mobile The use of costly emergency departments (EDs) for non-urgent care by the under- and uninsured is well documented ,4. Less Despite the substantial contributions made by mobile healthcare programs to the US healthcare system, evaluating the efficacy of such programs is currently difficult. Traditional research methods do not fit well in the environment needed to develop successful relationships with the clients of van programs, clients who are often marginalized or homeless or undocumented and almost always untrusting. Successful methods van workers have developed to deal with their clients, for example, the shield of anonymity that is so important to patient engagement, precludes systematic tracking of important data such as ED diversion, follow-up care, and health outcomes.The MHC industry is at a crossroads of evolving financing strategies similar to that experienced by its healthcare predecessors: hospitals and community health centers. A driving force in this development has been the emergence of the dual imperatives of enhancing access and shifting care to more cost-effective settings. Paralleling this evolution has been a shift from a grant and/or charity-based funding structure to a negotiated exchange of payment-for-services through government and private health insurance and/or subsidies. However, to facilitate this shift in payment models, and thus an appropriate and sustainable funding stream, we must be able to quantify the value of the contributions of the MHCs and to demonstrate the potential benefit of increased investment in this system of healthcare delivery.We hypothesize that by using published data that quantify the value of prevention practices, an empirical method can be developed to determine the efficacy of mobile healthcare programs. Specifically, mobile healthcare programs annually provide a broad array of prevention services to as many as 4 million otherwise un-served individuals. Recent, ground-breaking research from the National Commission on Prevention Priorities (NCPP) assigned a relative value to various prevention practices in terms of quality adjusted life years saved (QALYS). When combined with research that estimates the value of a statistical life year saved, and applied to mobile healthcare data, the result projects a return on investment (ROI) ratio of at least 30:1, a value both significant and compelling. We believe that this methodology can be enhanced, and applied to mobile healthcare programs across the USA to quantify the value of their current services as well as support the process of prioritization of target populations and interventions that promise the greatest return for healthcare dollars invested. We believe that such a ROI calculator will be an innovative and effective method of quantifying the value of mobile healthcare programs within the US healthcare system.In order to test this hypothesis, we developed a 'prototype' or model. Using a sample mobile healthcare program, and published research to quantify health outcomes, we developed an algorithm to calculate the ROI of a typical broad service MHC.To develop the ROI prototype, the following elements formed the calculator algorithm:1. Using sample cost and service data from a selected MHC we developed a cost baseline for a sample service population.2. Using published data we projected QALYS for selected interventions provided within the sample MHC .3. By identifying interventions within the service sample with high correlation to ED visits, we estimated the number of ED visits prevented by mobile healthcare interventions.4. Using published data we applied a value of statistical life years saved (VSLYS) and avoidable cost of ED visits prevented to quantify the ROI of our sample mobile healthcare program.Analysis of 2005 Preventable Emergency Department Visits was used as the source for the cost of preventable ED visits [Sample cost and service data for the Harvard Medical School-sponsored 'The Family Van' for the academic year 2008 were used to provide clinic specific cost data to generate a per visit cost to compare with ED per visit costs to calculate 'cost avoided' as a measure of cost effectiveness. Data from the 2007 Massachusetts Division of Health Care Finance and Policy publication D visits , Actual It should be noted that in addition to medical screenings, education and counseling, individuals served by The Family Van access a broad array of services directly through the van, and/or its formal linkages with Neighborhood Health Centers .et al. [The NCPP research provided the pivotal estimates of outcome utilities in terms of QALYS. However, to be adapted for use in calculating a financial return on investment, the QALYS metric needed to be converted into financial terms. In order to address this issue, we used the 1990 research of Tolley et al.  to proviAs a result of applying the methods outlined, we calculated the ROI of the sample mobile healthcare program to be 36:1.In academic year 2008 The Family Van expenses were $565,700. There were 4,848 visits to the Van. These visits are discounted by 20% to allow for interventions within the service sample not highly correlated to ED visits. The resulting 3,878 visits would presumably have resulted in a preventable ED visit had the Van not been available , the remIn addition to preventing unnecessary ED visits, this mobile clinic also provided several of the NCPP recommended prevention services. The NCPP recommended prevention services provided include: hypertension screening, vision screening, cholesterol screening, obesity screening, depression screening, diabetes screening and diet counseling. Only first-time visits were counted and the NCPP estimations assume a 30% non-compliance with treatment . Cost-beIndividuals in need of follow-up were served either directly by the MHC, or through its formal linkages with Neighborhood Health Centers.Annual funds invested for The Family Van services totaled $565,700. ED costs avoided totaled $3,125,668. The value of potential life years saved by the Van services totaled $17,780,000. With these inputs, the resulting annual ROI of The Family Van is US$20,339,968 for a ROI ratio of 36:1.By using a sample mobile healthcare program and published research we were able to calculate a ROI value that indicates that for every dollar invested in funding for that mobile healthcare program $36 were returned in combined value of life years saved, and ED costs avoided. In that we have used an ROI-based metric to determine the effectiveness of preventive services, while proposing to decrease both the incidence and economic burden of preventable diseases as a result of rendering such services, our results echo the findings of a recent study conducted by Trust for America's Health in partnership with the New York Academy of Medicine, the Robert Wood Johnson Foundation, and The California Endowment and Prevention. This study calculated the ROI to state healthcare systems of small local prevention programs. Reassuringly, data from this collaborative effort detailed healthcare savings of up to $16 billion annually with only marginal investments in preventive services per person per year .There are several limitations to our study. It should be stated at the outset that adapting the 'population-based' research of the NCPP series to derive individual estimates of QALYS is specifically discouraged in the NCPP research. However, as acknowledged in the NCPP research, the disproportionate vulnerability of the mobile healthcare target population makes the adaptation of these population values reasonable for the 'prototype' purposes. In addition, we used the lower estimate of life years saved in all cases . Nonethe1. The assumption that MHC preventive services interventions are no less effective in preventing more expensive uses of healthcare than those reported in other studies .2. The distribution of healthcare status among the MHC population provides at least equivalent opportunities for effective prevention.Further limitations include the fact that the ROI calculation applies cost data from 2005 to 2008 service data, as well as using research values for QALYS from a 2004, and for VSLYS dollar values from 1990. Although the result of this calculation, assuming annual growth in healthcare costs, arguably results in a more conservative estimate of the return on investment, the data used would optimally be from the same year. This limitation should be addressed in future iterations of this project.In addition, it should be noted that it is assumed that the ED would be the only alternative source of care for MHC clients. This assumption needs to be based on more verifiable data in future iterations of this project.et al. model [The conversion of quality outcome data to a dollar value is hardly unique to this effort and has a long pedigree. Many public health projects must estimate the cost of inaction in terms of life years lost, and it was the review of research supporting the national Clean Air Act that yielded the most applicable research on determining the value of a statistical life year. Due to its use of quality adjusted life years, and because it calculates the lowest value for a statistical life year, we selected the Tolley l. model .A priority of future efforts will be to revisit the estimates that we have borrowed from other studies for QALYS and VSLYS related to MHC interventions and provide more robust lower bounds for the populations specifically served by the MHCs.The generalization of this approach will require the adaptation of the calculator algorithm to several different MHC programs. This in turn will require the development and adoption of a uniform data collection tool, service taxonomy, and reporting channel to provide the evidence with which to drive further resource allocation and decision making for the underserved communities reached by MHCs.By using a sample mobile healthcare program and published research we were able to calculate a ROI value that suggests that, for every dollar invested in funding for that mobile healthcare program, $36 may be returned in combined ED costs avoided, and the value of life years saved.The implications of this ROI should be used to promote the effectiveness of the program model among healthcare policy-makers, as well as to seek funding to create a continuous self-assessment tool for use by mobile healthcare providers to promote healthcare practices that provide the greatest healthcare benefit for every healthcare dollar invested.Ultimately the calculator, when paired with services data rendered by each of the MHCs across the country, should serve as a powerful communication tool, whereby individual providers will be able to enter their own data, calculate ROI, compare their results to industry counterparts, establish benchmarks, and learn and change accordingly.ED: emergency department; MHC: mobile health clinics; NCPP: National Commission on Prevention Priorities; QALYS: quality adjusted life years saved; ROI: return on investment; VSLYS: value of statistical life years savedAll the authors except PB and IK work with MHCs. Proving the value of mobile healthcare will be of benefit as it will prove the usefulness of this form of service delivery. All the authors have applied for funding to further develop this project and build a web-based calculator that will be accessible to all mobile programs.NEO initially conceived the idea of a ROI calculator for mobile healthcare and prevention services. She is founder of The Family Van, and was first author in drafting the manuscript and had final approval. PJC, the former Massachusetts Commissioner of Public Health, designed the calculator algorithm, performed the statistical analysis and background investigation to develop the calculator, made significant contributions to the manuscript and had final approval. APV was the co-creator of the idea of calculating a ROI for mobile healthcare and prevention services, helped to draft the manuscript and had final approval for the manuscript. JB, Executive Director of The Family Van, collated and provided the cost and service data to test the algorithm and helped to draft the manuscript. DD provided expertise related to the national mobile healthcare service sector and helped to draft the manuscript. PB provided significant help in reviewing the literature and drafting the manuscript. IK provided expertise on health informatics and the use of, and assumptions about QALYS and VSLY, helped to draft the manuscript and had final approval.The pre-publication history for this paper can be accessed here:


The metabolic changes in trauma patients with shock contribute directly to the survival of the patient. To understand these changes better, we made a rigorous analysis of the variations in the main examinations requested for seriously polytraumatized patients.Prospective analysis of patients with blunt or penetrating trauma with hypovolemic shock, with systolic arterial pressure (SAP) equal to or lower than 90 mmHg at any time during initial treatment in the emergency room and aged between 14 and 60 years old. The following exams were analyzed: sodium, potassium, blood test, glycemia and arterial gasometry. The tests were carried out at intervals: T0  and followed by T24 (24 hours after admission), T48 (48 hours after admission), T72 (72 hours after admission).2 and PCO2 showing only slight variations, which meant an acidotic state during the hemorrhagic shock followed by a response from the organism to reestablish the equilibrium, retaining bicarbonate. The red blood count, shown by the GB (globular volume) and HB (hemoglobin) was normal upon entry but later it dropped steadily until it fell below normal; the white blood count  remained high from the first moment of evaluation.The test evaluations showed that there was a tendency towards hyperglycemia, which was more evident upon admission to hospital. The sodium in all the patients was found to be normal upon admission, with a later decline. However, no patient had significant hyponatremia; there was no significant variation in the potassium variable; the gasometry, low pH, BE (base excess) and bicarbonate levels when the first sample was collected and increased later with POIn this study we demonstrated the main alterations that took place in patients with serious trauma, emphasizing that even commonly requested laboratory tests can help to estimate metabolic alterations. Suitable treatment for polytraumatized patients with hypovolemic shock is a challenge for the surgeon, who must be alert to endocrinal and metabolic changes in his patients. Based on these alterations, the surgeon can intervene earlier and make every effort to achieve a successful clinical result. The endocrinal and metabolic alterations in the trauma maintain vascular homeostatis in addition to hydroelectrolitical, nutritional and hormonal integrity. Generally, this complex response is harmonic and ordered, resulting in the restoration of homeostasis. When this response is excessive, there will be profound imbalance in the homeostasis, resulting in these metabolic changes continuing into shock and the metabolic blockage of several organs, delaying the improvement of the patient or resulting in death.The best knowledge of definitive treatment for traumatic lesions and endocrinal and metabolic alterations to trauma can be useful in conducting planning and adaptation, improving treatment of patients, reducing their recovery time and improving prognosis.In this study, we aimed to identify early metabolic and hydroelectrolitical alterations resulting from serious trauma through simple, routine laboratory tests which are available even at small hospitals.This study was conducted at the Cajuru University Hospital (CUH), run by the PUCPR University in Curitiba, the capital city of Paraná State in the south of Brazil. The CUH is a respected center for traumas and emergencies in the south of the country and has 300 beds, of which twenty are in the intensive care unit.Following approval by the committee in charge of ethics in research at the institution where the study was to take place, the results of the laboratory exams of the patients who met the criteria for inclusion in the study were consecutively analyzed. The collection of data was begun in the second half of 2007 and finalized in the first semester of 2008.Fifty-one patients of both sexes, aged 14–60, suffering from moderate to serious trauma with systolic arterial pressure ≤90 mmHg at any time during admission were included in the study. Following analysis of the laboratory tests, the metabolic responses at the different assessment times were compared.For each of the variables that were normal, the null hypothesis that the averages were the same at the four evaluations was tested, versus the alternative hypothesis that in at least one instance there was a different average from the rest. In the tables below, the values of averages and standard deviations are given and the p values of the statistical tests. We highlight only the results that showed significant variations throughout the study.The results of the study were given by averages and standard deviations or medians, minimum values and maximum values. To compare the evaluations with the variables that met the condition of normalcy, the variance analysis with repeated measurements was used and the LSD multiple comparison test. For variables that did not meet the conditions of normalcy, this comparison was made by using Friedman's non-parametric test and the Wilcoxon non-parametric test for multiple comparisons. Values of p < 0.05 indicated statistical significance. For multiple comparisons using the Wilcoxon test, the level of significance was corrected by Bonferroni. In this case, values of p < 0.008 indicated statistical significance.The prospective analysis was carried out in patients with blunt or penetrating trauma with hypovolemic shock, evaluated by SAP equal to or lower than 90 mmHg at any time during initial treatment in the emergency room and aged between 14 and 60. The following exams were analyzed: sodium, potassium, blood test, glucose, glycemia and arterial gasometry. The test were carried out at intervals: T0  and followed by T24 (24 hours after admission), T48 (48 hours after admission), and T72 (72 hours after admission).Our study showed that relevant alterations were observed in the laboratory tests that were requested when compared with the values considered normal by our laboratory.Hyperglycemia was found in most of the patients upon admission to the hospital, with a later tendency to normalize, although the glycemic levels remained high even during fasting.  and Hemoglobin (HB), was found to be normal upon entry but steadily reduced until it fell below normal. , PO2 (oxygen pressure), PCO2 (carbon dioxide pressure), BE (base excess) and pH, as these are more important when evaluating patients in serious condition.  and later the Na-K-ATPase pump. InitialThe increased sodium in the cell stimulates enzymatic activity of the Na+, K+-ATPase, which pumps the sodium out of the cell in order to restore the potential of the cellular membrane. Depolarization is a vital and essential activity for the functioning of the neurons and myocardial cells. In shock patients, the activity of the Na+, K+-ATPase is hindered because the anaerobic aggression of the shock impedes the arrival of sufficient energy to the cells which is necessary to maintain the active transport of the sodium ions by the Na+, K+-ATPase. Initially, there could be increased release of this ion by the muscular lesion and then a reduction would be expected due to the release of aldosterone.+. As the plasmatic level of adosterone increases, a great deal of Na+ is reabsorbed and more K+ is excreted[Potassium is the main intracellular cation. In cases of metabolic injury, the organism responds with the diffusion of potassium out of the cell. What is responsible for this phenomenon is aldosterone, released by angiotesin II and the increase of potassium in the serum, resulting from the hypovolemia in these patients. The role of the aldosterone here is to act in the exchange of intraluminar sodium for intracellular K excreted. Due to  excreted,9The magnitude of the physiological aggression suffered by the organism is directly proportional to the volume of lost blood. Two responses may be seen in these patients: if blood loss is low (<15% of blood volume), the organism responds with a vasoconstriction; if blood loss is high (15–40% of blood volume), the response is hypotension, with a hemorrhage and imminent risk of life.Hypovolemic shock, a type of hypodynamic shock, is related to the low cardiac output and increased systemic vascular resistance (vasoconstriction). The massive drop in red blood cells, hemoglobin and hematrocrit are the main laboratory markers of hypovolemia, excluding the first period after hemorrhage has begun. Clinically, compromised cardiac output is by primary reduction of the venous return. If cardiac output falls, the arterial pressure will follow suit, affecting organic perfusion. HoweverOther factors related to shock stimulate macrophages to produce and release a cytokine, tumor necrosis factor alpha , one of the important inflammatory mediators of shock. This cytokine stimulates the invasion of leukocytes to the tissues by increasing the formation of neutrophils in the bone marrow (leukocytosis) and, at the same time promoting the endothelial expression of leukocyte adhesion molecules. The neutrophils may be responsible for the tissue lesion which, when activated, release oxygen free radicals, N-chloramines and proteolytic enzymes.,5,9. In The basic acid alteration predominant in shock and metabolic acidosis resulting from insufficient supply of tissues with sufficient blood flow and oxygen, which are highly necessary to the mitochondrial aerobic metabolism. Hemorrhagic shock mainly consists of hypovolemia, with insufficient cardiac filling pressure, hemodilution, of the hemoglobin and reduced cardiac output, which in fusion, reduce the capacity to carry oxygen per unit of blood volume,13.+ released when the ATP is oxidized ADP. The ADP is not converted into ATP because of mitochondrial insufficiency to carry out oxidative phosphorylation[2, and beginning to produce pyruvate via glycolysis, which accumulates in the cytoplasm until it is converted into lactate and then it is released[There is no shock without alterations to the cellular metabolism. The explanation for acidosis in shock patients is the incapacity to recapture the Horylation. With a  released,9. Thus, released.3/CO2 relationship, (buffering system) as close as possible to normal. Thus, the organism reduces the PCO2 through compensatory hyperventilation. We then find the answer to why the HCO3 and PCO2 are low. Even with these mechanisms, we can never say that this response will be complete because there is no pH correction to a normal level. Another interesting piece of information in the arterial gasometry that leads us to consolidate the initial idea of metabolic acidosis and later compensatory response is the base excess values. Negative values in an acute injury mean that there has been a reduction in the total number of bases, i.e., that the organism has lost bases resulting from a primary metabolic disturbance, in this case, metabolic acidosis.[But the fall in tissue perfusion caused by the hypovolemia and cellular hypoxia is also seen through other parameters evaluated in the arterial gasometry. Not only do patients with metabolic injury develop metabolic acidosis, but they also react with other compensatory mechanisms to this cellular stress in order to maintain the HCOacidosis.,9,14,15.2 to the cells and the purification of components derived from the metabolism such as CO2 and toxins. The accumulation of lactate (anaerobic metabolism), CO2 and other substances, leads to a progressive reduction in the pH with the consumption of bicarbonate. Almost immediately, chemo receptors found in the aortic arch modulate impulses afferent to the central bulbar, leading to hyperventilation (Kussmaul breathing) in their attempt to eliminate CO2 and reduce the acidemia levels. Nevertheless, these mechanisms cannot keep the pH normal and we see that initially this reaches close to 7.5. It is important to point out that a large number of the patients analyzed were submitted to invasive mechanical ventilation in order to carry out their surgical procedures. Based on the data given above, we see the development of mixed acidosis, with a predominantly metabolic component, mainly at the cost of the bicarbonate ion.In serious trauma, there is a considerable fall in tissue perfusion, which impedes the arrival of nutrients and OAlthough gasometry has good parameters for identifying criteria of the gravity and clinical condition of patients, it should not be used as a substitute for other tests such as lactate testing, considered as a more sensitive marker for the diagnosis of hypoperfusion and prognosis in patients suffering from hypovolemic shock..In this study we demonstrated the main alterations in serious trauma patients, emphasizing that even commonly requested laboratory tests can estimate metabolic alterations. Suitable treatment for polytraumatized patients with hypovolemic shock is a challenge for the surgeon, who must be alert to endocrinal and metabolic changes in his patients. Based on these alterations, the surgeon can intervene earlier and make every effort to achieving a successful clinical result.The authors declare that they have no competing interests.The authors contributed equally to this work.