Having shown previously that an electronic prescription writer and decision support system improved pediatric prescribing behavior for otitis media in an academic clinic setting, we assessed whether point-of-care delivery of evidence could demonstrate similar effects for a wide range of other common pediatric conditions.Cluster randomized controlled trial.A teaching clinic/clinical practice site and a primary care pediatric clinic serving a rural and semi-urban patient mix.A total of 36 providers at the teaching clinic/practice site and eight providers at the private primary pediatric clinic.An evidence-based message system that presented real-time evidence to providers based on prescribing practices for acute otitis media, allergic rhinitis, sinusitis, constipation, pharyngitis, croup, urticaria, and bronchiolitis.The proportion of prescriptions dispensed in accordance with evidence.The proportion of prescriptions dispensed in accordance with evidence improved four percentage points, from 38% at baseline to 42% following the intervention. The control group improved by one percentage point, from 39% at baseline to 40% at trial's conclusion. The adjusted difference between the intervention and control groups was 8% . Intervention effectiveness did not decrease with time.For common pediatric outpatient conditions, a point-of-care evidence-based prescription writer and decision support system was associated with significant improvements in prescribing practices. Background: Computerized systems for managing health-care information, such as medical records and prescriptions, have the potential to improve medical care. These improvements could come about as a result of embedding software within a medical record system that alerts clinicians to evidence that is relevant to the care they are providing. For example, such a system might deliver a pop-up reminder that informs a clinician about a potential prescribing error, or that the prescription ordered is not supported by recent evidence. Systematic reviews of randomized controlled trials evaluating the benefits of such systems have shown that computerized feedback and reminder systems can improve clinician behavior. However, much of this evidence comes from academic clinics caring for adults, and there is not very much evidence available on children or from community-based, nonacademic clinical practices. The researchers here wanted to evaluate whether a computerized system providing clinical decision support at the time of electronic prescribing could improve prescribing in pediatric primary care. In order to test this, the researchers carried out a cluster randomized trial. This means that individual health-care providers were randomized to receive evidence-based prompts via the computerized system or not, depending on which arm of the trial they were randomized to, but outcome data for the trial were collected at the level of the individual patient's prescription. Pop-up prompts were provided for eight medical conditions common in pediatric primary care, and alerted the provider to a summary of the evidence that supported or refuted the prescription that the provider was about to make. The primary outcome in the trial was the change in proportion of prescriptions dispensed in accordance with evidence, over the course of the trial.What the trial shows: In the trial, 36 pediatric health-care providers were randomized at one site, where the trial was carried out over 50 months, and eight at another, where the trial lasted for 18 months. At the start of the trial, 38% of prescriptions in the intervention group were in accordance with the evidence, and 39% of prescriptions in the control group. At the end of the trial, 42% of prescriptions in the intervention group were in accordance with evidence, as compared to 40% of prescriptions in the control group. The difference in prescribing behavior change over the course of the trial between intervention and control groups was statistically significant, once adjusted for the clustering of data by the individual providers.Strengths and limitations: In this trial, health-care providers were randomized, rather than patients. This method (cluster randomization) is probably the most appropriate method to carry out a trial such as this, because it reduces the chance of contamination . One limitation is the small number of providers that were recruited into the trial; another is that many of these in fact practiced or had recently practiced in academic medicine rather than community-based, nonacademic clinical practices. This limits the ability to generalize from these findings to a nonacademic setting. Finally, the trial was planned with the intention of evaluating the ability of computerized systems to achieve health-care provider behavior change. Therefore, data were analyzed by lumping together outcomes for many different medical conditions. This means that the computerized prompt system may not necessarily have been that successful in achieving improvements in prescribing for any individual condition.Contribution to the evidence: Systematic reviews of the effectiveness of computerized decision support systems of this kind have found some evidence that such systems can improve the behavior of health-care providers. This study adds data showing that the reminder system studied here resulted in moderate improvements in prescribing within a pediatric primary care setting. Health information technologies offer substantial promise to improve health care . ElectroIn one outpatient setting with a comprehensive EMR system, provider management decisions and choice of medications were influenced by alerts for routine screening, for abnormal physical examination parameters, and for potential medication side effects –22. ManyIn pediatrics, CPOE with DSS has reduced errors in the treatment of seriously ill, hospitalized children. In Tennessee, medication prescribing errors in a critical care unit fell from a rate of 30 per 100 orders to 0.2 per 100 orders after implementation of CPOE . In SaltWe previously demonstrated the ability of an electronic prescription writer and DSS to improve pediatric prescribing behavior for otitis media in an academic clinic setting . Using aThis was a cluster randomized clinical trial of provider behavior change. We measured prescribing behavior in both the intervention and control groups before and after the introduction in the intervention group of a DSS providing evidence at the time of electronic prescribing. This approach enabled us to measure the independent effects of the intervention, while controlling for baseline differences in prescribing behavior and other temporal trends unrelated to our intervention.In this study the unit of intervention was the provider. We chose this design because a cluster randomized clinical trial is the strongest study design available to assess the effect of a DSS upon provider behavior, as it directly compares providers receiving the intervention to those not receiving the intervention. Direct contamination between providers was likely to be minimal because, as detailed below, the DSS provided privately viewed messages that only briefly engaged the practitioners and their patients. Had we randomized patients instead, it would have resulted in providers taking care of both patients receiving the intervention and others not receiving the intervention, with the strong possibility of diluting the intervention effect.This study was conducted at two clinical sites. One was the Pediatric Care Center at the University of Washington (PCC), an outpatient teaching clinic for pediatric residents and a clinical practice site staffed by full-time pediatric providers. The other site was Skagit Pediatrics (SP), a primary care pediatric clinic serving a rural and semi-urban patient mix approximately 60 mi north of the Seattle metropolitan area.At the start of the study period at PCC, care was provided by 29 resident physicians, two nurse practitioners, and seven attending physicians, each with their own patient panels. At SP, there were eight physicians and two nurse practitioners, also each with their own patient panels. Both clinics adopted a computerized patient flow manager developed by one of us (JAW); this system was described in detail in our earlier publication . At PCC,An electronic prescription writer was developed to interface with the computerized patient flow manager. To prescribe a medication, a provider first selected the patient's name and then the patient's medication, indication, dosage, and, finally, the duration. The patient's weight was entered by the nurse during check-in. A paper copy of the prescription was printed for the patient and was also attached to each medical record.Providers were trained on the network, and then a 6-mo period prior to randomization was used at each site to collect baseline prescribing behavior for all providers. When the intervention started, paper prescriptions were either removed (at PCC) or actively discouraged (at SP).The participants in the study were the 36 HCPs at PCC and the eight HCPs at SP . Study iAt each site, the unit of randomization was the HCP. We used a stratified randomization process to randomly assign providers to either the intervention arm or the control arm. Specifically, for each condition , providers were first stratified by the number of prescriptions they wrote in the baseline period, in order to roughly equalize the number of patients seen by providers and prescriptions written in the intervention and control arms. Then, within strata of high or low number of prescriptions written, HCPs were randomly assigned to receive evidence-based medicine prompts or not. In both clinics, providers could have been randomly assigned to receive anywhere from none to all of the evidence-based prompts; after randomization it turned out that all providers received at least one evidence-based prompt.Random numbers for allocation were generated by computer, and were concealed until interventions were assigned. This process was overseen by the research coordinator (L. L.) in conjunction with the investigator in charge of the data structure (J. W.). L. L. enrolled participants and assigned participants to their groups based on the randomization. Participants were not informed in advance of the study as to which conditions were being investigated, and hence were blinded to the intervention. However, based on the nature of the intervention, HCPs were  able to determine to which evidence screens they were randomized by discussions with other HCPs.In 2000 and 2001, additional residents were randomized as they joined PCC as interns. After baseline data collection for these residents, they were randomized as the others to either the intervention or control groups, and then followed in the same manner as the other providers until study conclusion.For each condition studied, providers in the treatment arm were shown pop-up “alert” screens, based on their selection of medication, indication, or duration. The first screen contained a short summary of the evidence either supporting or refuting the current choice of medication, indication, or duration. The provider could then choose to (i) view more information about this evidence, (ii) view the abstract of the article from which the evidence was derived, (iii) view a scanned PDF version of the article, or (iv) have the reference E-mailed to them for later viewing. The conditions included in the intervention were acute otitis media, allergic rhinitis, sinusitis, constipation, pharyngitis, croup, urticaria, and bronchiolitis. For the outcome of otitis media, a small percentage of the data includes data previously published in the earlier report by Christakis et al. Excluding these data did not affect our results or conclusions, and we have elected to include these data so that we are able to address the consistency of effect among the two intervention sites. The full details of the information provided in the evidence screens are available from the authors.Our primary outcome was changed physician behavior in accordance with the intervention message screens. Our primary measure assessed all of the interventions considered together, in order to answer the question, “Can we influence provider prescribing behavior by providing ‘just-in-time,' evidence-based prompts?” We also looked at the effect of the message screens upon the separate outcomes of (i) otitis media, (ii) allergic rhinitis, and (iii) a combined category of the other (less common) conditions. These categories were chosen a priori, as these conditions were the ones found to be most frequently evaluated in the clinic during the study planning stages (and were not necessarily the conditions for which medications were most frequently later prescribed). All analyses were performed by “intention to treat.”Bronchiolitis was studied separately. This was necessary because the “baseline” period (used for randomization and to measure change from baseline) did not include the most recent past bronchiolitis season, and hence change from baseline could not be measured in the analysis. Instead, for bronchiolitis we compared the behavior of providers receiving the intervention directly to the behavior of control providers.We performed two subanalyses. In our first subanalysis we added a “one click” option for allergic rhinitis and otitis media that allowed a provider to rapidly accept a pre-written electronic prescription corresponding to the “correct” message presented on the screen. For example, a provider attempting to prescribe diphenhydramine for allergic rhinitis received an evidence-based alert screen recommending fluticasone for this indication. In the original intervention, a provider wishing to change from diphenhydramine to fluticasone would have had to close out the alert screen, cancel the diphenhydramine prescription, and then begin the fluticasone prescription. With the one-click option, a provider was able instead to click a button that closed both the alert page and the diphenhydramine prescription, and automatically completed a weight-based fluticasone prescription. This one-click option was estimated to save each provider approximately 11 keystrokes or mouse clicks for each prescription dispensed.Our second subanalysis studied whether or not the intervention effectiveness faded over time, even with continued alerts. To assess whether providers were tiring of the intervention, we divided the time following the beginning of the intervention into five quarters (3-mo periods) following the introduction of the intervention; within each time period, the intervention effect was otherwise assessed exactly as in the main analysis. Dividing the study in this way allowed us to see whether the effect waned over the period of the study, and to test whether providers might be paying less attention to the intervention with repeated exposure over time.As in the original study, provider behavior change was measured as the difference, by study arm, between the outcomes in the period before and after the trial . Measuring each provider's behavior as a change from baseline served two functions. First, it controlled for each provider's individual prescribing practices, and second, it reduced the random variance in the outcome measure, affording our analysis greater power. Because we measured the outcomes as a change in individual behavior, it was not necessary to control for provider-specific potential confounders. To test the primary hypothesis we used weighted regression analyses, controlled for clustering by provider, to test the difference in behavior change between the treatment and control groups. Weighted regression analyses, again controlling for clustering by provider, were also used to assess the statistical significance of the behavior change within the treatment and control groups.The provider panels were unbalanced because of different work styles and schedules, and some providers had many more visits than others. As a result, the outcomes (mean change in provider behavior) were estimated with a greater degree of precision for providers with many visits than for providers with fewer visits. To account for this in the regression analyses, we conducted weighted analyses, whereby each provider's behavior contributed information to the analysis proportional to the precision with which their mean was estimated. As in the original analysis, this method achieved greater precision in the intervention estimates than unweighted analyses.Not all providers treated patients for each randomized intervention during both the before and after period, and therefore, the number of providers contributing information to each intervention varied slightly.http://www.stata.com).All analyses were conducted with Stata, version 6.0, statistical software  and for 18 mo at SP (from June 2002 to December 2003), during which 57,319 and 33,127 visits were made for pediatric care at the two sites, respectively.  At the two sites combined there were 1,933 prescriptions written for otitis media, 754 for acute sinusitis, 372 for allergic rhinitis, 235 for pharyngitis, 96 for croup, 85 for urticaria, 79 for bronchiolitis, and seven for constipation. The baseline rates of prescribing in accordance with the evidence are shown in For the primary outcome of our study—all condition-specific outcomes assessed as a single group—the intervention had a statistically significant effect upon provider prescribing behavior . Among pLooking at the conditions separately , provideFor bronchiolitis, providers in the intervention group at PCC prescribed albuterol for bronchiolitis substantially less often than providers in the control group . At SP the rate of prescribing for bronchiolitis was so low  as to preclude analysis.In the one-click option subanalysis, there was little evidence that this option further impacted provider behavior. There was no significant change or improvement in prescribing for either otitis media or allergic rhinitis at either PCC or SP when the one-click option was added to the screens for providers already receiving the intervention. For example, at PCC prescribing amoxicillin twice daily for otitis media increased by 2%, while proper dosing for otitis media fell by 2%.In the second subanalysis, we found little evidence of provider fatigue. For one condition—otitis media—the impact of the intervention was rather weak for the first two quarters of the intervention, but then demonstrated a markedly improved impact over the next three study quarters . When we looked at all conditions combined, the intervention demonstrated some variability but overall had a constant effect over the study.In the Institute of Medicine report “Crossing the Quality Chasm” , the ComThere was considerable variation in the degree of effect of the intervention based on which message screen was being viewed. This is not surprising as the messages themselves differed in the strength of their recommendations. A key feature of the system (which distinguishes it from many previous interventions) was that the evidence-based support system was nether prescriptive nor proscriptive. The messages were developed from the best available evidence, which was frequently limited. In a few cases, the only evidence came from adolescent and adult studies, and providers had to decide if the study conclusions were applicable to children. Some of the messages included a “number needed to treat,” and providers likely weighed this information differently based on individual patient preferences, the condition being treated, and the severity of the condition at the time of presentation. This approach attempted to empower providers with information, and was in keeping with recommendations for applying evidence to practice –41, but There were some limitations to our trial. First, only eight of the 44 providers we studied practiced outside of an academic training environment, limiting our ability to make inferences about the performance of this system in the general practice arena. Second, because of power limitations, we were unable to assess whether the evidence screens may have had different effects for clinicians at different levels of training. Finally, we specifically planned to study the ability of electronic point-of-care systems to effect physician behavior change, not the effectiveness of an electronic point-of-care system to deliver messages about a single condition. This required that, for our main outcome, we considered all behavior change to be of merit and of equal importance statistically. Such an assumption, while necessary for our study, might not necessarily be well founded. It may be that we would have observed stronger effects and more robust findings had we focused on only one or two outcomes and employed a more comprehensive and concentrated intervention geared towards effecting management change within these conditions. In this regard, future evaluations might focus on trying to effect behavior change for some particular conditions that, for example, drive a large proportion of health-care costs, or are associated with substantial need for prescribing improvement.While for many conditions prescribing behavior improved, we found that antibiotic prescribing for otitis media increased for providers in both the intervention and controlgroups, counter to the evidence provided in the intervention. Our intervention served primarily to slow the tide towards increased prescribing. This increase in prescribing occurred even though recent publications included in the intervention evidence supported limiting antibiotic use ,44, and Our subanalyses unveiled some important findings relevant to future implementation of these systems. We found that for some conditions there was a delay before the message screens achieved their fullest impact. Technological problems early in the study may have decreased early adoption of the system, or alternatively, providers may have needed time and repeated exposure to the evidence-based messages before they changed their behavior, especially behavior most ingrained such as for otitis media. The second subanalysis assessed our attempt to streamline electronic prescribing. Previous investigators have reported that ease, speed, and some control of the system is crucial to the successful adoption of a clinical support system –50, but An assessment of the generalizability of our findings is critical. One of our two sites was situated within a large academic medical center, while the other clinic was populated by many recent graduates of this same site. Additionally, the practices served a fairly well-educated and primarily English-speaking urban and semi-urban patient population. Hence, our findings might not be generalizable to pediatric practices that differ in meaningful ways either in terms of their patient demographics or provider practice patterns.Additionally, the computer software that was developed and used for this trial was independent from any other computer system in place in these clinics, and was unattached to any other computerized messaging systems. Uptake of systems in other settings might indeed be different, especially if an entire suite of messages is provided that is designed to provide evidence and other information oriented towards improving delivery of care.With regards to this last point, our findings are particularly pertinent to smaller clinical practices that might not be aligned with larger organizations, and who will have to choose EMR systems and DSSs on their own. During our trial we had a number of challenges related to computer software and hardware, and other studies have reported likewise ,52. For We found that initial on-site training of all providers was insufficient to ensure ongoing use of the system, and use was not habitual until after providers had used the system for many months. We offered labor-intensive support with on-site promoters, research assistant presence, E-mail hints, and person-to-person rather than simply electronic feedback mechanisms—all among the recommended strategies for improving adoption of health technology applications ,56.Our effect size compares favorably to other considerably more intensive and expensive interventions –59 and hAny comparison of our findings to those of others must first recognize that there have been many different types of computerized medication management systems developed. Bennett and Glasziou  categoriIt is important to recognize that we studied only a part of the capabilities of this (or similar) systems. An 8% overall improvement in prescribing practices such as we found, along with other point-of-care interventions such as guidelines for chronic care, could have substantial long-term impacts, for example, on reducing antibiotic resistance or costs of care. Nevertheless, the challenge is to continue to improve the efficiencies of these systems. A recent study of computerized decision support in primary care found no effect of its guideline-based interventions due to low levels of use . Given tCONSORT ChecklistClick here for additional data file.(227 KB PDF)Trial ProtocolClick here for additional data file.(237 KB PDF)Figure S1(38 KB PPT)Click here for additional data file.


Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments.Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified.The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems. Metagenomics describes the functional and sequence-based analysis of DNA isolated from environmental sample without first culturing the associated microbes . Four viAnother conceptual "metagenomic" library can be constructed from the combined sequence data collective generated over the last 30-plus years of DNA and protein sequencing and deposited in the international databases. In contrast to the environmental metagenome libraries, the collective metagenome was built by the incremental addition of sequences from many different sources. The SEED database, developed by the Fellowship for Interpretation of Genomes (FIG), is an annotated non-redundant database, compiled from several sources including GenBank (including GenBank's non-redundant and refseq databases), Swiss-Prot, KEGG, and from genome sequencing centers. At the time of analysis, the SEED database contained 1,731,649 proteins  of proteins that were sampled against the number of significantly different subsystems when the Sargasso data was compared to the SEED data at different confidence levels. At the most stringent level (99% confidence that the difference does not occur by chance) there are about 75 unique phylosubsystems in the Sargasso sample (solid red line) and about 150 unique phylosubsystems in the SEED. In either case at least the sample size needs to approach 300,000 proteins to achieve statistical significance. In contrast, at lower confidence levels, there are more significantly different phylosubsystems, and the samples need to be smaller to detect them. Thus with a 90% confidence that the difference do not occur by error there are approximately 100 unique subsystems in the Sargasso data, and a sample size of about 150,000 proteins is required to identify them all.Fig. The AMD dataset has many fewer phylosubsystems than the SEED dataset, and only those subsystems present in the both samples are used in the comparison to identify significantly different phylosubsystems. This limits the analysis to 284 of the 523 different phylosubsystems. Of those that are present in the AMD and the SEED, only 19 are significantly more abundant in the AMD sample , threonine (T), and glycine (G) are overrepresented in the Sargasso Sea metagenome Table . For exaAs previously observed ,7, thereSome phylosubsystems involved in one-carbon metabolism, including the synthesis and degradation of carbohydrates, cell walls, and capsules are more abundant in the Sargasso Sea. In contrast, the genes for the utilization of complex carbon sources including lactose, arabinose, fructose, mannose, galcitol and inositol are all underrepresented in the marine environment suggesting these are not significant sources of carbon in this environment.de novo synthesis and scavenging pathways, as well as ribonucleotide reduction (scavenging ribonucleotides for DNA synthesis) are more abundant in the Sargasso Sea. Similarly, the subsystems involved in the capture of phosphate via the conversion of ADP to ATP coupled to oxidative phosphorylation are also overrepresented in the Sargasso sample. In contrast, nitrogen metabolism phylosubsystems are less abundant in the Sargasso than the SEED, with the sole exception of ammonia assimilation that is marginally overrepresented in the Sargasso sample at larger sample sizes. The Sargasso Sea has previously been reported to be phosphate limited. The concentration of dissolved inorganic phosphate is approximately 4 nM in the Sargasso Sea. By comparison, the North Pacific and typical bacterial minimal media have phosphate concentrations of approximately 100 nM [Phylosubsystems involved in purine and pyrimidine y 100 nM ,19. TogeEstimates of the percentage of bacteria in the ocean that are motile vary from less than 5% to more than 80% ,21, and When the AMD and SEED databases were compared, only phylosubsystems that were in both the AMD and the SEED samples were included. This limited the total number of subsystems that were compared for statistically significant differences. There are far fewer phylosubsystems with significantly different distributions between the AMD and SEED datasets, and phylosubsystems that are significantly more common in the AMD dataset are shown in Table The SEED and Sargasso subsystems were compared to both the whale fall and farm metagenome samples . For thiet al folate biosynthesis is less abundant in the Sargasso metagenome than either the Whale Fall or Soil Metagenomes whereas this analysis demonstrated that there is significantly more folate biosynthesis in the Sargasso than the other samples. There were 9,311 proteins with similarity to folate biosynthesis subsystem from the SEED database in the Sargasso metagenome, 602 proteins with similarity in the farm soil metagenome and 491 proteins with similarity in the Whale Fall metagenome. In contrast, Tringe et al. identified 7,283 proteins, 1,253 proteins, and 889 proteins respectively. These differences are probably due to the difference in annotation of the SEED subsystems and KEGG pathways. These differences also highlight the need for continued careful annotation of genomes, and comparative analysis of different annotation systems and methods.The KEGG pathways have historically focused on core metabolism, annotating enzymes that have been classified with EC numbers. In contrast, the SEED subsystems include core metabolism and the data is extended to subsystems that cover cellular processes and functions, regulation, and so forth. Although the two classification techniques are not directly comparable, and statistical confidence was not provided with the differences between KEGG pathways in the supplemental data from the previous analysis, some clear parallels can be seen between these analyses see . For exaCommunity genome sequencing – metagenomics – can provide fine detailed analysis of the metabolism occurring in different ecosystems. However, metagenomics analysis is limited to a purely descriptive science without a rigorous statistical comparison of the prevalence of different genes in different environments. Our analysis demonstrates an application of statistics to identify those areas of metabolism that are significantly over represented in different environments.The method described here is predicated on the expectation that genes that are more useful in an environment are more commonly found in that environment. Or put another way, there is an enrichment or selection for sets of genes in different environments. A statistical analysis, using a resampling with replacement technique, was developed to generate both the difference in occurrence of each subsystem in each sample, and to generate confidence intervals for the likelihood that these differences are observed by chance. By using these statistical techniques to compare the genetic composition of different environments, the areas of metabolism and biochemistry that are important in a particular environment, in comparison to other environments, can be identified. Like other studies, this analysis demonstrated that microbes in the surface waters of the ocean are much more likely to contain genes involved in photosynthesis than the control data set. The non-redundant database used as a control is not expected to contain large numbers of photosynthetic organisms because it is skewed towards microbial pathogens.Our analysis also demonstrated more than 150 other subsystems that are over represented in the Sargasso Sea sample when compared to the control set. The skew in the database alone cannot explain this difference, and these subsystems must be important for survival in the ocean. Some examples, such as the synthesis of serine, threonine, and glycine, directly testable hypotheses can be generated from these analyses. For other examples, the explanation of the differences between samples may be more elusive. Several pieces of evidence will assist in determining the roles of different subsystems in different environments. For example, the inclusion of more environmental data with each sample will allude to some of the differences in metabolic potential between samples; the careful dissection of the presence of different subsystems in different organisms will identify which organism in which environment is performing the different biochemical reactions; and the extension of other techniques such as metabolic modelling into the environmental arena may provide insights into the critical biochemical mechanisms in each environment.Comparative metagenomics is a powerful mechanism for highlighting the ecological differences between environmental samples. Our analysis has generated several hypotheses that can be readily tested on microbes from the Sargasso Sea:1. Serine, threonine, and glycine betaine are primarily being used as osmoprotectants. Increased intracellular concentrations of serine may protect and against the osmolarity of the ocean. In contrast, sucrose and trehalose are not being used as readily for osmoprotection.2. Microbes in the Sargasso Sea are more limited for phosphate than nitrogen.3. Microbes in the ocean are not generally using flagella based motility but are probably using one of the less-characterized mechanisms of locomotion.4. Archaea in the AMD sample are degrading arginine and histidine.The subsystems approach to investigating environmental genomes demonstrates the intricate interplay between the abundance of genes in the environment and the biology of that environment. In addition to answering that age-old knock-knock joke  by catalShewanella sp. or Burkholderia sp. were removed from the Sargasso Sea metagenome because of contamination concerns [The complete SEED database v4 was used as the source of all data . Construconcerns . This da-20) to another protein previously assigned to a subsystem. Each protein was also classified as Bacteria, Eukarya, and Archaea, based on the Domain assignment of the most similar protein. There were a total of 276 annotated subsystems in the SEED. Bacteria had proteins in 257 of the subsystems. Archaea and Eukarya had proteins in 132 and 134 of the subsystems, respectively. This means that each there were a total of 523 potential data points. The term phylosubsystem is used to reflect that fact that the assignments are based both on the subsystem and Domain.Each protein from the AMD, Sargasso, or the SEED database was compared to proteins in the SEED database previously assigned to particular subsystems by the SEED annotators . A proteth and 951st elements would be the upper and lower limits on the confidence interval. 3) For any given subsystem, if the median from step 1 was inside the confidence interval determined in step 2 it was considered that the subsystems were not significantly different at the given confidence level. If the median was outside the confidence interval, it was assumed that the difference was significant at the given confidence level.A flowchart of the methods used to identify subsystems with statistically significant differences between databases is shown in Fig. A software package that calculates the statistics from appropriately formatted files is provided as supplemental material see . The sofBRB developed the statistics and compared the samples.FR contributed to the discussion on roles of subsystems in the samples. RE generated the connections between proteins and subsystems. All authors contributed to authoring and editing the manuscript.Step-by-step description of the methods used to calculate the differences between subsystemsClick here for fileA tar gzip compressed archive of source code and instructions for calculating difference of medians as described here. Version 0.01 of the software, released under the terms of the Gnu General Public License (GPL).Click here for filePresence of subsystems in different environmental samples in tab-separated text format.Click here for filePresence of subsystems in the AMD, Sargasso, and SEED samples in tab-separated text format.Click here for fileet al (reference 6) or using the method described herein. Note that the relative gene content of the subsystems and Kegg pathways is never identical.Abundance of selected Kegg Pathways and Subsystems that are approximately similar as detected either based on the Normalized differences of Tringe Click here for file


Personal statements and referees' reports are widely used on medical school application forms, particularly in the UK, to assess the suitability of candidates for a career in medicine. However there are few studies which assess the validity of such information for predicting unhappiness or dissatisfaction with a career in medicine. Here we combine data from a long-term prospective study of medical student selection and training, with an experimental approach in which a large number of assessors used a paired comparison technique to predict outcome.Data from a large-scale prospective study of students applying to UK medical schools in 1990 were used to identify 40 pairs of doctors, matched by sex, for whom personal statements and referees' reports were available, and who in a 2002/3 follow-up study, one pair member was very satisfied and the other very dissatisfied with medicine as a career. In 2005, 96 assessors, who were experienced medical school selectors, doctors, medical students or psychology students, used information from the doctors' original applications to judge which member of each pair of doctors was the happier, more satisfied doctor.None of the groups of assessors were significantly different from chance expectations in using applicants' personal statements and the referees' reports to predict actual future satisfaction or dissatisfaction, the distribution being similar to binomial expectations. However judgements of pairs of application forms from pairs of doctors showed a non-binomial distribution, indicating consensus among assessors as to which doctor would be the happy doctor . Assessors taking longer to do the task concurred more. Consensus judgements seem mainly to be based on referees' predictions of academic achievement .Although widely used in medical student selection to assess motivation, interest and commitment to a medical career, the personal statement and the referee's report cannot validly be used by assessors, including experienced medical school selectors, to identify doctors who will subsequently be dissatisfied with a medical career. Many doctors in the UK, and probably elesewhere, are unhappy with their careers in medicine, with a fifth or more of junior doctors considering leaving medicine . MedicalApplicants to UK medical schools apply by means of the UCAS (previously UCCA) form, which contains standard demographic data, information on educational achievement and qualifications, a personal statement from the applicant, and a report from a referee . EmpiricExperimental research manipulating the informational content of personal statements, resumes and cover letters shows that applicants are judged as more competent and to have greater potential when the resumes contained relevant educational references , whereasIn this study we used a simple experimental design to assess the ability of assessors to distinguish pairs of doctors who had been qualified for five or six years, one of whom who was very happy and satisfied with their medical career and the other was very unhappy and dissatisfied, using only the personal statement and referee's report submitted as a part of the doctors' UCCA application forms to medical school, twelve years previously.; ).This study was approved under the normal procedures of the Ethics Committee of the UCL Department of Psychology. The study was exempt from the need for specific permission by the UCL Research Ethics Committee  design in which a group of assessors in 2005 decided which of two doctors, one very happy and one very unhappy, was the happier doctor (assessed in 2002/3), based on the personal statement and referee's report from the application forms the doctors had submitted to UCCA in 1990.The doctors were part of the 1991 Cohort Study and had applied to medical school during the autumn of 1990 for admission in October 1991 , when a "Which is the satisfied, happy doctor? Definitely A/Probably A/Probably B/Definitely B". For the main statistical analysis, judgements of 'definitely' and 'probably' were combined; for correlations with the use of 'definite' judgements, see The assessors made their judgements in questionnaire booklets which contained twenty pairs of UCCA forms, with each side of a double-page spread showing the personal statement and referee's report from a satisfied and a dissatisfied doctor, randomised to left (labelled A) or right (labelled B). Pairs were matched for sex, and were from applicants who were UK nationals aged under 21 when they applied to medical school. All forms were transcribed, checked against the originals, fully anonymised and printed in a standard format. For each pair of forms, there was a question at the bottom of the page: The pairs of forms were assessed by four different groups of assessors: 35 experienced medical school selectors , a convenience sample of 19 doctors known to SI, 22 medical students from UCL, and 20 psychology students from UCL  we are referring to the individuals who applied to medical school in 1990, and whose UCCA forms were used in this study, and to the questionnaire responses made by the same individuals as practising doctors in 2002/3. In particular it should be noted that both the doctors and the assessors each completed a Big Five personality assessment, and each is analysed below.2 = .018, 1df, P = .89).The pairs of application forms were judged by 96 assessors, 48 of whom looked at Book 1 and 48 looked at Book 2. A chance null hypothesis predicts that random guessing would result in an assessor making an average of 10 correct judgements out of 20 (50%). In fact overall, of the 1920 judgements made by the 96 judges, 963 (50.2%) were correct, which is not significantly different from 50%  = .932, P = .429). The chance expectations of individual assessors can be modelled using a binomial distribution . Figure  = .932, A possible explanation for the inability of assessors to perform above chance expectations is that the task is simply too hard, and assessors are, in effect, forced to make decisions at random. That possibility can be evaluated by looking at the performance of the assessors on the 40 individual pairs of forms. Each pair of doctors was judged by 48 assessors, and therefore the binomial chance expectation is that on average the correct judgement for a particular pair of doctors will be made by 24 out of the 48 assessors. Figure less likely to be correct overall  and more likely to concur with other judges , both correlations with time remaining significant after partialling out the other measure.Figure actual happiness of doctors and the background measures, but rather between those same background measures and which doctors are thought more likely to be the happier by the assessors. Although personality is known to be a strong correlate of happiness and satisfaction in doctors [It is clear that assessors are unable to carry out the task beyond chance expectations, but they nevertheless concur with one another in their judgements. The question therefore arises as to the nature of the information from the forms which is acting as the basis for the assessors' (erroneous) consensus. We had background measures on the doctors in the pairs and we therefore looked to see whether those measures predicted the judgements of the assessors. It should be emphasised here that we are not looking here at correlations between the  doctors ,7, thereUK medical schools put extensive effort into reading the application forms submitted by applicants, using them as a basis for shortlisting for interview (and in some cases for making offers), and assessing both academic achievement and motivation from them. However the present study suggests that assessors, including experienced medical school selectors, are unable to use application forms to judge which applicants will become happy and satisfied doctors. That does not mean, though, that selectors are making random judgements on the basis of the forms. There is a consensus between assessors, which is greatest amongst those who have spent longest reading the forms. However that consensus seems principally to result from using information about the academic potential of applicants, despite there being good reason to believe that it is not a lack of academic ability which causes the dissatisfaction of unhappy doctors, or that academic ability correlates with happiness . In viewThe implicit use of academic ability as a criterion is consistent with other work , which hThe overall outcome measure in our study, of satisfaction and happiness with medicine as a career, might be criticised on the grounds that what is really required is knowledgeable, effective, doctors who stay in post. If knowledgeable, academically well-qualified doctors alone is all that is required, then probably the best predictor of academic success is educational achievement ,10, but Perhaps the most surprising thing about our study is that the task was completely impossible. That might reflect the relative poverty of the information provided on the application forms , and for referees' reports the median length is 398 words ). The information provided might also be inaccurate or misleading, either unintentionally, or as part of a process of 'impression management' [We had wondered whether there may be further information about applicants concealed within the subtleties and the nuances of the language used (e.g. the use of negative versus positive emotion words). We therefore used a computerised text analysis program, LIWC ,13, to aThe Schwartz Report into admissions to universities, was critical of the use of selection methods for which there was not demonstrable evidence of reliability or validity . UCCA/UCThe author(s) declare that they have no competing interests.The 1991 cohort study was originated by ICM. ICM and EF had the idea of transcribing and analysing UCCA forms, and the forms were scanned and transcribed by JL and EF. ICM designed the experimental study, and SI and AC carried out the experiment as a part of research projects submitted as a component of their degree programmes. ICM wrote the first draft of the manuscript, and all authors revised the manuscript. ICM is the guarantor for the study.The pre-publication history for this paper can be accessed here:Supplementary Information – three additional tables and one additional figureClick here for file


Case descriptions suggest IBS patients are more likely to have other disorders, including migraine, fibromyalgia, and depression. We sought to examine the prevalence of these conditions in cohorts of people with and without IBS.The source of data was a large U.S. health plan from January 1, 1996 though June 30, 2002. We identified all people with a medical claim associated with an ICD-9 code for IBS. A non-IBS cohort was a random sample of people with an ICD-9 code for routine medical care. In the cohorts, we identified all claims for migraine, depression, and fibromyalgia. We estimated the prevalence odds ratios (PORs) of each of the three conditions using the Mantel-Haenszel method. We conducted quantitative sensitivity analyses to quantify the impact of residual confounding and in differential outcome identification.We identified 97,593 people in the IBS cohort, and a random sample of 27,402 people to compose the non-IBS comparison cohort. With adjustment, there was a 60% higher odds in the IBS cohort of having any one of the three disorders relative to the comparison cohort . There was a 40% higher odds of depression in the IBS cohort . The PORs for fibromyalgia and migraine were similar . Differential prevalence of an unmeasured confounder, or imperfect sensitivity or specificity of outcome detection would have impacted the observed results.People in the IBS cohort had a 40% to 80% higher prevalence odds of migraine, fibromyalgia, and depression. Irritable bowel syndrome (IBS) is a functional disorder of the gastrointestinal tract, which results in the clinical symptoms of altered bowel habits and abdominal pain ,2. It isPeople with IBS are reportedly more likely to have other disorders including migraine, fibromyalgia, and depression. These disorders are known in some literature as "affective spectrum disorders", or "functional somatic syndromes" ,5. This Data on the prevalence of depression in conjunction with IBS has been less clear. One study showed no association with psychological factors and health seeking behavior among IBS patients . In contAlthough the hypothesis surrounding the prevalence of these three disorders in association with IBS has been previously explored, many published studies have been based on data from clusters of case reports or from small clinical practices. Further limitations in these studies have been the lack of a reference cohort, and inability to fully control for potentially confounding variables. To address the validity concerns in previous research, we sought to examine the prevalence of other disorders, specifically migraine, fibromyalgia, and depression in cohorts of people with IBS and without IBS.The context for this research was data from a large, national health insurance plan in the U.S. Coverage through the plan is available as an employment benefit and its members either are employed or are spouses and dependents of the employed subscriber. A research database contains automated insurance claims for medical services, including those provided by a physician or hospital, along with claims for prescriptions filled at an outpatient pharmacy. For this research, we used data from eight geographically diverse states with the largest concentration of health plan membership, primarily in the Midwest and south/southeastern United States.We identified all people with at least one medical claim associated with a diagnosis of irritable bowel syndrome between January 1, 1996 and June 30, 2002. The ICD-9 CM code corresponding to a diagnosis of irritable bowel syndrome is 564.1 . To idenAmong people in each of the two cohorts, we identified all medical and prescription claims relating to the occurrence of medical care for migraine, depression, and fibromyalgia. To decrease the probability of including claims for diagnostic testing, where the ICD-9 code may not reflect a definitive diagnosis (i.e. a "rule-out" diagnosis), we developed operational definitions for each of the three conditions. The operational definitions were as follows: Medical care for migraine was defined as claims for a physician visit or hospitalization associated with a diagnosis for migraine, and outpatient prescription claims associated with dispensing of an anti-migraine drug . To be classified as having migraine, people were required to fulfill both the diagnosis and the prescription criteria. Medical care for depression was defined as claims for visits with a mental health provider associated with a diagnosis for depression. We defined medical care for fibromyalgia as claims for a physician visit or hospitalization associated with a diagnosis of fibromyalgia.To accommodate the nature of the dynamic populations in this study, and that migraine, fibromyalgia, and depression are chronic diseases without clear dates of onset, in these data we calculated the prevalence of each of the conditions in the following manner. For each month of the study period, we created a series of three dichotomous variables indicating whether the person received medical care for migraine, depression, or fibromyalgia, according to the operational definitions previously described. A person who fulfilled the operational definition for one of these conditions had the indicator variable for that variable flagged as one in that specific month. Once an individual fulfilled the operational definition in one of the months, all subsequent monthly indicator variables for that condition were flagged as one. The prevalence of each condition in the IBS and non-IBS cohorts was calculated as the fraction of people flagged with the condition as of the last month of the study period. Thus, for the purpose of estimating disease prevalence, a person with a short-lived depressive episode would be given the same weight as a person with depression lasting several years. To examine whether clustering any of the three conditions occurred in people, as has been suggested among people with IBS, we also calculated the prevalence of having two conditions and the prevalence of having all three conditions. For a measure of association, we used the prevalence odds ratio (POR). The crude prevalence odds ratio for each condition was calculated as the prevalence odds among the IBS cohort divided by the prevalence odds among the non-IBS cohort.Information bias stemming from the use of medical claims to identify each of the three conditions was a potential problem. People in the IBS cohort may be greater consumers of health care, and due to increased contact with health care providers, they could have a greater opportunity for a diagnosis of other diseases, including these three disorders. To address this source of possible bias, we created a variable representing the mean cost of all medical and pharmacy services reimbursed by the health plan during the study period per person-month of health plan enrollment, and treated it like a confounding variable in the analysis. Cost of medical and pharmacy services has been used in previous studies using these data as a proxy measure for disease severity ,15. ExclTo quantify the possible effect of an unmeasured confounder on the observed prevalence odds ratios, we conducted quantitative sensitivity analyses based on simple methods . This anIn another sensitivity analysis, we quantified the effect of outcome misclassification on the prevalence odds ratios. Although we developed operational definitions to identify the three disorders in medical claims data, there may be clinical inconsistency in the diagnosis of these conditions. These inconsistencies could include false-positive or false-negative diagnoses by a clinician. Using fibromyalgia as an example, we varied the hypothesized sensitivity and specificity of the outcome to assess the effect of possible diagnosis error. In these calculations, we assumed that the sensitivity and specificity of fibromyalgia detection was non-differential between the IBS and non-IBS cohorts. We calculated simulated prevalence odds ratios accounting for the hypothesized levels of misclassification.All analyses and reporting for this research were performed using de-identified data with respect to Protected Health Information. Under the Health Insurance Portability and Accountability Act (HIPAA) of 1996, this research, using encrypted automated data, did not require approval by a Privacy Board or Institutional Review Board.We identified 97,593 people in the IBS cohort, and a random sample of 27,402 people to compose the non-IBS comparison cohort as a 5% random sample. The distribution of people in the two cohorts by age category and sex is shown in Table The prevalence of migraine, fibromyalgia, and depression in the IBS and the non-IBS comparison cohorts are shown in Table Crude and pooled prevalence odds ratios for the three disorders are shown in Table Results of the sensitivity analysis of adjustment for an unmeasured dichotomous confounder are depicted graphically in Figure Table Within a large, national health insurance plan, we identified a cohort of 97,593 people with IBS, and a comparison cohort of 27,402 people that received routine medical services. Among people with IBS, the odds of having at least one of the three disorders was 60% greater compared with people without IBS. As suggested in previous research, we observed these three disorders to have a greater prevalence among people with IBS in comparison to those without IBS.The crude, unadjusted, comparisons of the IBS to the non-IBS cohort provide an expectation of the prevalence of diagnoses of migraine, depression, and fibromyalgia in a group of people with IBS. This result can be useful for predicting use of health services, such as in an office setting. The adjusted results provide information relating to a causal effect of IBS, and addresses whether having IBS increase the prevalence of these conditions. The prevalence odds ratios were pooled across strata of age, sex, and quintile of the mean cost of medical services per person per month. The mean cost of medical services reflects the average consumption of health care services, and was intended to serve as a proxy measure for disease severity. Although disease severity itself is not a confounder, it creates a source of information bias that acts like a confounding variable owing to differential disease surveillance. Adjustment for these variables shifted the direction of the prevalence odds ratios towards the null for all of the point estimates. While the prevalence odds ratios were still elevated in the direction of excess prevalence in the IBS cohort, it is possible that using another variable that represented direct clinical measurements of disease severity could have improved adjustment for confounding. Such a shift from crude to pooled odds ratios suggests that improved adjustment in confounding could have resulted in a null association.The sensitivity analyses illustrate some of the assumptions that underlie this study. Although residual confounding by an unmeasured variable can never be entirely ruled-out in epidemiologic research, the sensitivity analysis of adjustment for an unmeasured confounder indicated that the observed odds ratio would have been biased only if the prevalence of the confounding variable was extremely disproportionate between the IBS and non-IBS cohorts, and if there was a strong association between the confounder and migraine. This variable could be a common physiologic pathway of IBS and migraine. The sensitivity analysis for misclassification of fibromyalgia showed that variation in the correspondence of the insurance claims to actual clinical diagnoses could introduce bias. Sensitivity of fibromyalgia detection would have a small effect on the observed odds ratio. Variation in the specificity of fibromyalgia detection, on the other hand, would have greater effect on the observed odds ratio, resulting in more substantial bias towards the null than variation in the sensitivity. In interpreting the main findings of this study, we assume there are no unmeasured confounding variables and that the sensitivity and specificity of outcome detection is 100%. The sensitivity analyses, however, illustrate how violations of these assumptions could lead to a change in the interpretation of the results. The interpretation could change to one of no association between the IBS and non-IBS cohorts, if the presence and association with an unmeasured confounding variable was large enough. Alternatively, the interpretation could change to one of association of a considerable magnitude between IBS and the three disorders, if the degree of outcome misclassification was high.The data used for this study were medical and pharmacy claims from a large, national health insurer. A limitation of the insurance claims data is that socioeconomic and quality of life measures, such as work absenteeism, which would be of considerable importance to an analysis of people with multiple comorbidities, are not obtainable. Further, since the people included in this study were those with commercial health insurance enrollment, the observed prevalence may be different than among people who may have different access to health services, such as those without health insurance, or other forms of insurance, such as government-sponsored Medicare or Medicaid plans.All variables used in this study were derived from data elements ascertained from claims submitted for reimbursement, such as ICD-9 diagnosis codes and CPT-4 procedure codes. People in the IBS cohort were identified because they had a medical claim for health services associated with an ICD-9 diagnosis code of irritable bowel syndrome. The predictive value of an ICD-9 diagnosis code corresponding to IBS has been shown be around 80% ,18, reprPeople in the IBS cohort had a 40% to 80% higher prevalence odds of migraine, fibromyalgia, and depression in comparison to people without IBS.The author(s) declare that they have no competing interests.JAC conceived of the study, conducted the analysis, and drafted the manuscript. KJR participated in the design and helped to draft the manuscript. HJC participated in the analysis and helped to draft the manuscript. YZ participated in the analysis and helped to draft the manuscript. FAF participated in the design and helped to draft the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


War and unrest mark the history of Vietnam. For hundreds of years, Vietnam has fought a number of foreign invaders including the French, Japanese, and Chinese. The U.S.–Vietnam War was just one of several chapters in this history, ending in 1973 with the Paris Peace Accords. The American War, as it is referred to in Vietnam, has been estimated to have resulted in the deaths of 1.5 million Vietnamese and 58,000 Americans. The countryside is still dotted by unexploded mines, and questions remain about environmental health concerns such as the lasting effects of dioxin-containing herbicides used during the war.But as a bumper sticker spotted on a Hanoi street pointed out, “Vietnam is a country, not a war,” and today many of Vietnam’s most pressing concerns are related to post-war growth. About half of Vietnam’s current population of 80 million people is under 30 years old. This young generation, many of whom have a limited recollection of wartime, drives a sense of optimism about the future of the country. Foreign investment in this country for 2005 is estimated at US$5.4 billion, only a little less than investment in India, and Vietnam is expected to enter the World Trade Organization in mid-2006. This growth makes Vietnam a duality of a country, on the one hand facing the environmental problems typical of the rural countryside (such as those related to poor sanitation), and on the other facing the occupational and environmental health consequences of rapid industrialization.This was the setting of the joint Second International Scientific Conference on Occupational and Environmental Health and Sixth National Scientific Conference on Occupational Health, held in Hanoi on 16–18 November 2005. The conference gathered 176 participants with 194 abstract presentations. Most of the participants were from Vietnam; 44 were from abroad, mostly from other Asian countries. The conference was organized by the Vietnam Association of Occupational Health and the University of Washington, with the sponsorship of numerous Vietnamese, international, and U.S. agencies, including the NIEHS, the National Institute for Occupational Safety and Health (NIOSH), and the John E. Fogarty International Center of the NIH.Early in the conference NIEHS director David Schwartz and Anne Sassaman, director of the NIEHS Division of Extramural Research and Training, presented the vision of the NIEHS for expanding global health as part of its five-year strategic plan and bringing the institute’s expertise to bear on the problems facing this growing country. “NIEHS-supported research has shown that air pollution, which is a big problem in Vietnam, can impact cardiovascular health and increase susceptibility to respiratory infections,” said Sassaman. “These and other environmental health concerns are a growing problem in the developing world.”The United States first established diplomatic relations with Vietnam in 1995, and a bilateral trade agreement signed by the two countries in 2000 opened the door to increasing economic and industrial development in Vietnam. Economic agreements have continued to strengthen with President Bush’s plan to participate in the Asia-Pacific Economic Cooperation Summit to be hosted later this year in Hanoi. In addition to a new wave of U.S. investors, Taiwanese and Japanese business ventures are eager to tap Vietnam’s wealth of cheap, relatively well-educated workers, who are willing to work six days per week, as opposed to the Chinese, who adhere to a five-day work week. Workers in traditional trade villages earn US$60 per month, sometimes less, as the national minimum wage is US$38 per month.  Because of these low wages and the relatively new implementation of occupational health standards, Vietnam may be poised to compete with China in the manufacture of goods for export, further expanding environmental and occupational health concerns.At this point, many multinational companies are adopting a “China plus one” strategy where they continue to support their main manufacturing centers in China and develop subsidiary ventures in another country, increasingly Vietnam. This trend is exemplified in the industrial park located near Hanoi’s international airport. Opened in 2000, the facility already houses 46 factories with more than 16,000 Vietnamese employees. If the Vietnamese government continues to liberalize the economy and improve its infrastructure, the country may significantly increase its share of the global market.“These investments should include protections for workers in the cities as well as in the rural areas,” said Bui Thanh Tam, who cochaired the session on environmental health and industrial hygiene with conference co-organizer Matthew Keifer, director of the International Scholars in Environmental and Occupational Health program at the University of Washington. With this rapidly expanding industrialization of Vietnam, environmental and occupational health protections and improved scientific capacity in these areas will become increasingly important.The conference therefore focused heavily on occupational health. The establishment of safety codes and means for enforcing occupational and environmental health standards that protect workers and communities was a concern that resonated throughout the conference. A study presented by Ta Quang Buu of the Hai Phong Preventive Medicine Center showed that only 3% of enterprises in the region implemented guidelines for injury prevention. The average number of workdays missed due to occupational injuries was 14.5 per person between the years 2000 and 2004, with 70% of these injuries attributable to violations of occupational safety rules. These data will eventually form the basis of a workplace health promotion program for workers and employers.However, it is likely that even these figures are underreported. Another presentation described a collaboration between investigators of the Vietnamese National Institute of Occupational and Environmental Health, the Liberty Mutual Research Institute for Safety in the United States, and the University of Massachusetts Lowell that aims to develop a comprehensive injury reporting system in Vietnam. This project will provide information about the scale of the burden of work injuries in the country.Other presenters described working conditions and the health of workers in the chemical industry. Authors Nguyen The Cong and colleagues at the National Institute of Labour Protection studied data obtained from 24,482 annual medical checkups of workers in this industry. They showed that the most prevalent diseases in this sector were acute ophthalmologic diseases, followed by respiratory and allergic conditions (from 26.8% to 75.1%). There were also findings of silicosis, lead poisoning, and hearing loss among these workers, and the health status of as many as 15.9% of workers was categorized as “unacceptable.” The presenters added that, as Vietnam becomes home to more technical types of ventures that require increased use of industrial chemicals and heavy machinery, these kinds of occupational injuries can be expected to increase unless worker protection guidelines are implemented and enforced.Much of the manufacturing activity is currently concentrated in traditional trade villages, where families work together to produce one specific commodity, such as pottery, silk garments, furniture, or paper. “Low cost is both a strength and a weakness of this manufacturing enterprise,” said Nguyen Duc Hung of the Institute of Labour Science and Social Affairs in his presentation. In an investigation of six traditional trade villages, Hung found that most of the manufacturing enterprises were located within or in close proximity to the villagers’ living quarters. Most of these settings lacked proper ventilation, chemical disposals, or other protections. Levels of respiratory toxics such as carbon monoxide, sulfur dioxide, and nitrogen dioxide were found to be high in these homes. Hung suggested that labor inspections and regulations that are applied to industrial sites should be expanded to traditional trade villages, where occupational exposures can affect whole families, including children.Despite advances in industrialization, rural and some suburban areas of Vietnam still face a lack of basic sanitation, which affects the availability of safe potable water and causes other environmental health problems. Two studies presented by Le Thi Song Huong and colleagues from the Hai Phong Preventive Medicine Center investigated levels of water contamination with fecal coliforms. Analysis of water samples showed that those with low chlorine residues had high levels of coliforms.Water sources are likely contaminated by effluent from poorly designed household latrines. These latrines are often located near the house kitchen and have poor drainage. The investigators helped villagers in the An Duong District to build latrines with better drainage and ventilation, located farther from the kitchen, which reduced the contamination of wells and vegetable gardens with fecal microbes. They were able to increase the number of households throughout the area that had these latrines from 19% to 29%. “Rural sanitation is a big concern and a big challenge, and we need implementation of creative new ways to solve these problems,” said Huong.Another study of fecal contamination of drinking water was conducted in the Cu Jut District in Dac Lac Province by Vuong Tuan Anh and colleagues from the National Institute of Hygiene and Epidemiology of Hanoi. The authors showed that a common source of household drinking water contamination occurred when family members dipped their unwashed hands into water vats stored in the home. The authors speculated that the simple use of long-handled ladles to scoop water combined with hand-washing campaigns could significantly reduce contamination.Vietnam, while undeniably on the rise, has far to go to ensure the environmental and occupational health of its citizens. “After visiting Vietnam,” said Schwartz, “it’s abundantly clear that . . . we need to build research capacity in environmental health to effectively address the global health disparities caused by environmental exposures.”The emergence of international ventures that is permeating Vietnam may yet tip the balance toward a healthier country. Said Le Van Trung, president of the Vietnam Association of Occupational Health and co-organizer of the conference, “This conference is a useful scientific forum and an opportunity for scientists in the local country, in the region, and over the world to exchange information and to discuss occupational and environmental health issues that need to be resolved in the first years of the new millennium.”


Control of reproduction and prevention of reproductive health problems are important reasons for women to use health services, but the proper organisational level of service provision is not clear. The purpose of this study was to investigate whether visits to private gynaecologists correlate with better health outcomes and worse participation in organised screening for cancer programs.This is an ecological analysis using municipalities and groups of women at 5-year age intervals within municipalities as study units. First, the Finnish municipalities (n = 452) were classified into three groups by the age-adjusted level of use of private gynaecologists. Secondly, each age group within municipalities was classified into tertiles by the level of private gynaecologist use. The outcomes were participation in cervical and organised breast cancer screening for cancer programmes, stage of gynaecological and breast cancers at diagnosis, and abortion rates and ratios. All data were obtained from national registers by groups at 5-year age intervals and by municipality. Raw and adjusted  odds ratios, total and by urbanity, were calculated.The proportions of women participating in cervical cancer and organised breast cancer screening for cancer were somewhat higher in the groups having a low use of private gynaecologists. The proportions of local cancers of all cervical, uterine, ovarian and breast cancers were similar in the three groups, even though the first analysis method suggested somewhat better results for the low-use group in case of cervical cancer and for the high-use group in case of uterine and breast cancer. The rates of induced abortion were higher in municipalities having a high use of private gynaecologists than in those having lower use.This ecological analysis suggests that frequent use of private gynaecologists relates somewhat to lower organised screening for cancer participation, and is not better in preventing abortions or in detecting cancer earlier. Our results suggest that a planned system relying mainly on general practitioners and public health nurses as the first line care providers is equally good for women's reproductive health as that in which specialists are used. Control of reproduction and prevention of reproductive health problems are important reasons for women to use health services. For example, in Finland in the early 1990s, 17% of all outpatient visits of women aged 25–44 years were due to contraception alone  were calculated.In the second analysis approach, each 5-year-interval age-group in each municipality was the unit of analysis. These units were classified into three groups, each having about an equal number of units, by the level of private gynaecologist use. Because different age groups were used for different outcomes, for some municipalities data were lacking and only units having outcome events were included, the number of women as well as the limits of the low, middle and high gynaecologist use vary  and age- and social-class-adjusted (index grouped into four classes and as a continuous variable within each 5-year age group) odds ratios were calculated. Similar analyses were made separately for urban/suburban and rural municipalities. SAS GENMOD procedure was used to fit the logistic regression model to allow events per trials responses and to fit Poisson regression model to compare rates.Table The proportions of women participating in cervical cancer and organised breast cancer screening were somewhat lower in municipalities having a high use of private gynaecologists, but the differences were small, Table When the age groups having similar private gynaecologist use were pooled over the municipalities , similar results were obtained for organised cervical cancer screening, showing less participation in the middle and high gynaecology visits groups, Table In the case of organised breast cancer screening, the age-adjusted odds ratios Table  gave simThe analyses were also made separately for urban (including semi-urban) and rural municipalities. The results of urban municipalities were similar to analyses that included all municipalities, but those for rural differed. In the rural areas the participation rate in organised cervical cancer screening was higher in the middle-use group  and the high-use group . In the case of organised breast cancer screening, the differences between the groups were small and statistically non-significant.The rates of uterine and ovarian cancers among 20–64 years old women were more common, but breast and cervical cancer rates were less common in the low-use group, Table In the first analysis approach, the proportions of local cancers of all cervical, uterine and ovarian cancers were similar in the three groups. Breast and uterine cancers were found somewhat more often when still localised in the high-use municipalities compared to low or middle use municipalities and cervical cancer was more often localized in the low-use municipalities; but all these differences were small Table . When alIn the second analysis approach , all the raw odds ratios suggested that in the high use group cervical cancer was found to be less often localised . When the gynaecologist visits were used as a continuous variable, the result was similar. However, age-adjustment made that difference statistically non-significant . When related to the number of births, the difference remained. When all visits were inspected, the differences between the three user-groups were similar.Abortion rates – even after age and social class standardisation – were higher in municipalities having a high use of private gynaecologists than in those having lower use, Table The second analysis approach  have catchment areas with referrals to specialist care in outpatient clinics in public hospitals, and preventive care  was meant to taken care of by the public health nurses and midwives. The public system does not include direct access to a specialist in non-emergency situations. However, this system that was created in 1972 was preceded by a public-insurance-based system, which has coexisted alongside . This haEarlier cancer detection and good contraception have been argued as important reasons for private gynaecologist visits. This ecological analysis suggests that frequent use of private gynaecologists is not important for these aspects in the Finnish context. All the differences found were small and can be due to confounding. There was a weak suggestion that private gynaecologist visits might help in diagnosing breast cancer at an earlier stage. But no such benefit was found for other cancers – for the detection of cervical cancer there was a suggestion of delay – or for participation in organised cancer screening, and the abortion rates were higher in municipalities with many visits. If we forget the potential methodological drawbacks, and assume that the indicators used reflect women's reproductive health more generally, our results suggest that a system relying mainly on general practitioners and public health nurses is at least equally good as that in which specialists are used as the first line care providers.Certainly there can be benefits to visiting private gynaecologists that were not captured by our indicators. They may include peace of mind (having had a check-up), treatment of infections and advice on healthy habits. These other outcomes, however, do not require the care provider to have medical specialist education. Costs were not studied here, but gynaecologist visits are more expensive due to fees and extra examinations carried out than visits to nurses or general practitioners. We did not have data of reasons for private gynaecologist visits, but earlier surveys as well as everyday observations suggest that most are for preventive purposes.Our previous analysis showed that visits to public gynaecologists partly compensated, in an ecological analysis, the geographical variation of using private gynaecologists . StudyinIn Finland two cancers are systematically screened for, cervical and breast cancer. The importance of participation in organised screening for cancer rather than relying on spontaneous examinations is supported by studies showing a smaller risk of cervical cancer after organised screening for cancer ,17. It hThere is no organised screening for cancer available for ovarian cancer. The need to regularly visit a gynaecologist has been proposed as necessary for an earlier detection of ovarian cancer. No support for this proposal was found in this study.The higher abortion rates in municipalities having many visits to private gynaecologists can be due to confounding. The difference was not explained by varying distribution of women's age or social class, but it could be due to other differences between the municipalities, which could not be adjusted for. The municipalities with a high use of gynaecologists were larger than the low-use municipalities, and communal cohesion, for example, might be a confounding factor. Furthermore, the available indicator of social class was crude, possibly resulting in residual confounding by social class.The ecological approach was chosen purposely. Our data sources, having personal identification codes, could have provided the possibility (at least in theory) of an individual level inspection. However, such an inspection would have been extremely confounded by reasons for visiting a gynaecologist: in addition to the women visiting gynaecologists because of health check-up, women having gynaecological problems would more likely be in the group of private gynaecologist users. In ecological analysis such a bias is lessened. But ecological analysis has its own drawbacks, including the small possibility to control for confounding. The size of municipality and varying social class distributions in the municipalities of different sizes are potential confounders, but on the other hand, the frequency of using private gynaecologists may be a mechanism through which the impact of municipality size or social class has its influence. Thus, we did not adjust for municipality size. Adjustment by the social class index of the municipality usually decreased the difference between the groups, but the social class index and the rate of gynaecologist visits strongly correlated with each other. This supports the hypothesis that they are part of the same chain, and that the use of private gynaecologists is a mechanism through which social class can have an influence.The health care system – with two-public-channels  – is unique to Finland. Do our results have any relevance to countries having different health care systems and different cancer screening systems? The question of the most appropriate care provider, both in terms of costs and health outcomes, has been of interest especially in the United States where no uniform health care structure exists, with many different organisations and choice of users [see e.g. ]. HoweveOur ecological study suggests that a health care system for women's gynaecological health relying mainly on general practitioners and public health nurses is at least equally good than that in which specialists are used as the first line care providers. However, observational studies, including ours are problematic in evaluating health effects of health services. Further research on the usefulness of visits to private gynaecologists is needed, ideally in a trial setting.The author(s) declare that they have no competing interests.EH, EP and AA designed the study; TS and KT collected the data; TS, KT and EP conducted the data analysis; EH drafted the paper; TS, EP and AA critically revised the paper.The pre-publication history for this paper can be accessed here: