To develop an accurate, proxy-reported bedside measurement tool for assessment of the severity of bronchopulmonary dysplasia  in preterm infants to supplement providers' current biometric measurements of the disease.We adapted Patient-Reported Outcomes Measurement Information System (PROMIS) methodology to develop the Proxy-Reported Pulmonary Outcomes Scale (PRPOS). A multidisciplinary group of registered nurses, nurse practitioners, neonatologists, developmental specialists, and feeding specialists at five academic medical centers participated in the PRPOS development, which included five phases: (1) identification of domains, items, and responses; (2) item classification and selection using a modified Delphi process; (3) focus group exploration of items and response options; (4) cognitive interviews on a preliminary scale; and (5) final revision before field testing.Each phase of the process helped us to identify, classify, review, and revise possible domains, questions, and response options. The final items for field testing include 26 questions or observations that a nurse assesses before, during, and after routine care time and feeding.We successfully created a prototype scale using modified PROMIS methodology. This process can serve as a model for the development of proxy-reported outcomes scales in other pediatric populations. Bronchopulmonary dysplasia (BPD), or chronic lung disease (CLD), is one of the most common sequelae of preterm birth , and itsThe most common definitions of BPD include the receipt of oxygen at 36 weeks post-menstrual age, with or without a physiologic test of oxygen dependency ,11, and A valid bedside assessment tool of pulmonary function will give clinicians and researchers a more effective way to test therapies by reliably identifying subtle effects on infant pulmonary function or by identifying subgroups of infants who respond to therapies such as diuretics or bronchodilators. Our goal was to develop a scale to assess the effects of lung disease on functional outcomes using proxy-reported measures. We adapted Patient-Reported Outcomes Measurement Information System (PROMIS) methodology, a widely recognized system of instrument item selection and refinement for patient-reported outcomes -18, to dThe ultimate goal of PRPOS is to provide clinicians with a set of items and responses in various functional domains that can discriminate between infants with differing degrees of BPD severity. Our secondary goal is to present a model instrument development process that might be replicated for use in diseases of infancy. This paper describes the first five of six steps in the scale development process: (1) identification of domains, items, and responses; (2) item classification and selection using a modified Delphi process; (3) focus group exploration of items and response options; (4) cognitive interviews of proxy reporters on a preliminary scale; (5) final revision before field testing; and (6) reliability testing .We developed PRPOS in the five phases illustrated in Figure We identified an appropriate set of activity domains and assessments for inclusion in the scale using face-to-face interviews with experienced neonatologists, nurses, and neonatal nurse practitioners at two academic medical centers (The University of North Carolina at Chapel Hill [UNC] and Duke University) and input from a panel of national experts in neonatology, pediatric pulmonology, feeding, and development.We conducted interviews individually or in small groups using a "brainstorming" format. We asked respondents to use their clinical experience to identify characteristics of an infant diagnosed with BPD [CLD] at 36 weeks and any activities that precipitated these characteristics. During this phase of the process, items were included if at least two participants agreed on their discriminative utility, with the goal of identifying a complete set of potential items. The resulting set of activity domains and assessments, which grew in the course of the discussions from nine original "assessments and domains" to what began to be called 15 "qualities and conditions," was used in the next phase of the development process.We used a modified Delphi process, a method of obtaining consensus on a subject matter from experts in the field through anonymous solicitation or polling of their opinions , to idenOur modified Delphi process included three steps: (1) a survey, (2) working group meetings, and (3) a second survey reflecting areas where consensus had not yet been achieved. The surveys were designed and administered using the web-based survey software Qualtrics , and each respondent received a unique URL to the surveys. The entire process took place from December 2009 to February 2010.We invited 59 clinicians from five academic medical centers to participate in the two surveys Table ; in addiThe first survey (step one) had three parts. In part one, respondents described how certain qualities or conditions  appear in infants with four levels of BPD [CLD] severity--none, mild, moderate, severe--in three situations . Table In part two of the survey, respondents rated how well each of the observation domains and feeding cues would discriminate levels of CLD severity using a scale of 1 to 9, where 1 = not at all well and 9 = extremely well.In part three, respondents provided open-ended feedback on the types of things that should be recorded before the assessment  and made comments on other things we should consider in developing the scale.Following the survey, we conducted three multidisciplinary workgroups (step two of the modified Delphi process) at UNC and Duke. At the start of the workgroups, we asked participants to score how well a set of items--quality of sleep; alertness, arousability, facial expression; disorganization; difficulty in calming; color change; tone; and feeding mechanics---reflects the severity of CLD in an infant during five states  using a five point scale . We then had guided discussions in which we asked participants to help refine our set of domains, narrow similar terms to a single, best descriptor, and clarify and simplify complex items. At the end of the workgroup, participants completed the score card again, and we determined whether discussion had changed preferences.The feedback we received from the working groups contributed to development of our second survey step 3), in which respondents estimated at what severity of lung disease they might observe a particular behavior or action and how well those items discriminate levels of CLD severity. Table , in whicIn February 2010, we conducted two focus groups of bedside nurses, a physical therapist, and a developmental specialist to clarify domains, confirm item definitions, and refine the wording of potential scale items and corresponding response options ,20. An eEach focus group was presented with the same scenario describing the clinical course of a premature infant at 36 weeks, and then asked to think about the infant in four disease states, no CLD, mild, moderate and severe CLD , the sense of the questions overall, retrieval from memory of relevant information , decision processes, response processes, and instructions for using the tool ,18,21,22The cognitive interviews were approved by the Institutional Review Board at UNC, and all interviewees gave their informed consent prior to the interview. The interviews took place in April and May 2010 and included bedside nurses from three academic medical centers , chosen to elucidate possible regional differences in response to terms. In our cognitive interview process, a bedside nurse used the scale on an infant and then participated in a cognitive interview. The experienced cognitive interviewer followed a semi-structured interview guide with questions about each item, the overall scale, and the directions.Examples of the cognitive interview questions include• On a scale of 1 to 5, with 1 being easiest and 5 being hardest, how easy or hard was it to choose an answer?-or- How sure are you that it is [X]?• How sure are you of your answer? If yes, probe: what response options would you eliminate?)• Would it be easier for you if you could choose from fewer options? • Would it be easier for you if you could choose from more options?  and 43 responses to the second survey (response rate = 64%). Seventeen people took part in the working groups: ten from UNC, including nurses and a feeding specialist, and seven from Duke, including developmental/family specialists, researchers, and a nurse.The open-ended responses to the first survey provided us with user-generated, specific terms and phrases with which respondents could describe an infant's appearance at the four levels of BPD severity. Nurses and neonatal nurse practitioners provided more detailed descriptions than did neonatologists, and the feeding and developmental specialists provided more nuanced responses about feeding and development.Table Respondents reported that pre-assessment data should include information on the clinical environment , administration and timing of medications , procedures and tests , and respiratory support .The workgroup participants assisted in narrowing multiple terms to a single, best term for 12 items. For example, eyebrow descriptors "furrowed," "scrunched," "contracted," and "tense" were narrowed to "furrowed." In addition, participants clarified, defined, or distinguished similar descriptions for eight items. For instance, participants helped discriminate between eyes closed due to stress, described by the term "eyes tightly closed," and eye closure that does not indicate distress, denoted by "closed and sleepy" eyes. In three cases, workgroup participants simplified terms; for example, we reduced descriptions of musculoskeletal tone from four to three because of clinicians' inability to discriminate accurately between four different levels.Participants also highlighted areas of uncertainty, expressing concern that some of our feeding items  might be influenced by the feeder's technique and level of experience or the infant's development and feeding skills, rather than by the infant's level of CLD severity. The groups also noted that it is difficult to decipher whether "raised" and "furrowed" eyebrows signal distress related to the infant's CLD.When we asked workgroup members to rescore after discussion, their responses did not change significantly from what they reported before discussion. Overall, most items scored as "moderately" or "pretty closely" reflecting severity of CLD in infants.Results from the second survey of the modified Delphi process suggested that we had a range of behaviors and actions that would indicate different levels of CLD severity for each domain  because of difficulty in defining an appropriate scale (sleep) or low scores on the CLD discrimination question. We also added two areas--retractions and nasal flaring-- for inclusion on the tool, but we determined that we did not need to explore these further during the focus groups.Eighteen beside nurses and specialists participated in the two focus groups, with nine participants in each group. All participants had at least three years of experience in the neonatal intensive care unit. The focus group discussions helped us to confirm response options for our items and determine the scale endpoints from no disease to severe CLD. Focus groups also helped us discover which terms should not be used as response options . As we note above, we began by presenting the focus groups with eleven areas, arousal, general state during care time, calming, eyes, eyebrows, color, tone, desaturations during feeding, respiratory rate during feeding, desaturations, and tachypnea, and asked group members to discuss transition/arousal, calming, agitation and energy/activity level, eye appearance, color change, tone, desaturations, and respiratory rate. We also asked focus group members to think about descriptors of general state--mainly calm or quiet, restless, agitated or irritable, distressed, and frantic--and of the ability to calm--self-calms, calms with containment, voice soothing, irritable, not easily calmed, frantic/inconsolable. In the course of listening to focus group discussion, we chose to eliminate the questions about color and tone, and also to eliminate questions about eyebrows, but retain questions on eyes, and add questions about respiratory rate and desaturation during both care time and feeding.Six bedside nurses from three academic medical centers, UNC (n = 3), Stanford University (n = 2), and the University of Iowa (n = 1) participated in one-hour cognitive interviews.Overall, the nurses reported that the questions were easy to answer. Interview respondents found that the tool's instructions were understandable for the overall assessment and the care time portion of it, but they found the instructions less clear for the feeding portion of the assessment. At least one respondent suggested wording changes to the response options of 12 of 20 questions, but half or more of the respondents suggested changes to the response options for only these four questions: (1) How would you describe the infant's general state?; (2) How would you describe the infant's tone?; (3) How do the infant's eyes appear as you begin care?; and (4) How would you describe the infant's endurance during care time?In response to these cognitive interview results, we changed the response options in four cases about which at least half the respondents had suggestions. The old and new responses to the questions are presented in Table We refined the directions for using the scale, particularly for the feeding assessment section. We defined "desaturation" as an oxygen saturation of less than 80%, and we defined "increased respiratory rate" as a respiratory rate above 60 or, if the infant's baseline respiratory rate was already above 60, an "increase" is defined as a respiratory rate above the baseline. We provided instructions for how to calculate the baseline respiratory rate--count for 30 seconds, then multiply by 2--and we revised other question wording and response options, examples of which can be seen in Table The use of the PROMIS methodology in PRPOS's development assures us that the creation of the instrument has been both transparent and replicable expert clinical judgment from registered nurses, neonatal nurse practitioners, neonatologists, and developmental and feeding specialists has informed all the phases of the development process. We continually refined the scale's potential set of items and response options with the goal of achieving a parsimonious set of items going into the cognitive interviews. We did not have to remove any items during the final scale revision. The prototype scale includes 26 questions about the infant that a nurse assesses before, during, and after a routine care time and feeding, and takes less than 2 minutes to complete.Our scale development process was similar to, but more broadly inclusive and iterative than, the development of the Premature Infant Pain Profile ,24 becauEach instrument development phase could not alone lead to a successful product, but no phase was dispensable, and, taken together, they have generated a set of items ready for quantitative assessment. Our development process is limited by the fact that it is performed only in academic medical centers, although it is reasonable to assume that most non-academic center neonatal intensive care units would share many features of the academic medical center environment. Our focus groups were conducted at only two neonatal intensive care units both located in a single state, opening the possibility of limitations by region, or practice culture. Our more geographically dispersed cognitive interviewing and field testing should help us identify any such problems.The PRPOS is currently undergoing field testing at five academic medical centers, where bedside nurses are applying the assessment tool to a cohort of 150-200 neonates (25-40 per institution) between 23 and 30-6 weeks gestational age at birth  and between 36-0 and 36-6 weeks postmenstrual age. At the conclusion of field testing, we will perform psychometric analyses of the data to test item validity and reliability, for the purpose of further scale refinement.We expect that use of the PRPOS to assess observable, functional domains will greatly enhance the current unidimensional assessment of BPD severity based on oxygen use alone. For example, the PRPOS might allow clinicians and researchers to test therapies for BPD more effectively by accurately identifying subtle effects on lung function. In addition, refinement in the definition of BPD may allow more accurate prediction of important outcomes such as hospital length of stay and re-hospitalization after discharge, and further refine the relationship between BPD and neurodevelopmental outcome.Use of a structured approach modelled on the rigorous PROMIS methodology helped us develop and refine a proxy-reported measurement instrument over a short period of time, while maintaining precision, clarity, discrimination, and comprehensiveness balanced with parsimony. This approach will serve as a useful model for others interested in developing proxy-reported outcomes measures.BPD: bronchopulmonary dysplasia; CLD: chronic lung disease; ELGAN: extremely low gestational age newborn; PRPOS: proxy: reported pulmonary outcome scale.The authors declare that they have no competing interests.Research question: WAP, MML; Study conceptualization and design: WAP, MML, STR, DAD, SEM; Data collection: WAP, SEM, LMP; Data analysis and interpretation: WAP, SEM, STR, DAD; Initial draft and revisions of manuscript: SEM, WAP, STR; Manuscript revision: DAD, MML, LMP. All authors read and approved the final manuscript.Box S1. Focus Group Scenario. This file presents the scenario used in the focus group discussions.Click here for fileTable S1. Survey 2 results for CLD severity classification of behaviors and actions in each domain. This file shows a table of the domains and behaviors/actions used in the second survey, with an indication of whether the behavior/action was classified as being characteristic of no, mild, moderate, or severe lung disease.Click here for file


Arabidopsis thaliana, but their applicability to important crops such as cotton and soybean is not fully known.Trichome hairs affect diverse agronomic characters such as seed weight and yield, prevent insect damage and reduce loss of water but their molecular control has not been extensively studied in soybean. Several detailed models for trichome development have been proposed for P1 allele. DGE data and RNA-Seq data were mapped to the cDNAs (Glyma models) predicted from the reference soybean genome, Williams 82. Extending the model length by 250 bp at both ends resulted in significantly more matches of authentic DGE tags indicating that many of the predicted gene models are prematurely truncated at the 5' and 3' UTRs. The genome-wide comparative study of the transcript profiles of the wild-type versus mutant line revealed a number of differentially expressed genes. One highly-expressed gene, Glyma04g35130, in wild-type soybean was of interest as it has high homology to the cotton gene GhRDL1 gene that has been identified as being involved in cotton fiber initiation and is a member of the BURP protein family. Sequence comparison of Glyma04g35130 among Williams 82 with our sequences derived from CS and CG isolines revealed various SNPs and indels including addition of one nucleotide C in the CG and insertion of ~60 bp in the third exon of CS that causes a frameshift mutation and premature truncation of peptides in both lines as compared to Williams 82.Two high throughput transcript sequencing methods, Digital Gene Expression (DGE) Tag Profiling and RNA-Seq, were used to compare the transcriptional profiles in wild-type  and a mutant  soybean isoline that carries the dominant P1 locus, a BURP family member (Glyma04g35130) from soybean has been shown to be abundantly expressed in the CS line and very weakly expressed in the glabrous CG line. RNA-Seq and DGE data are compared and provide experimental data on the expression of predicted soybean gene models as well as an overview of the genes expressed in young shoot tips of two closely related isolines.Although not a candidate for the  Plant trichomes are appendages that originate from epidermal cells and are present on the surface of various plant organs such as leaves, stems, pods, seed coats, flowers, and fruits. Trichome morphology, varying greatly among species, includes types that are unicellular, multicellular, glandular, non-glandular (as in soybean), single stalks (soybean), or branched structures (Arabidopsis) . VariousGLABRA1 (GL1) results in glabrous Arabidopsis plants dATP by random primer reaction method dATP labeled probed), washing, and exposure to Hyperfilm . The same EST probe used for RNA blot was used in the DNA blots.For DNA blots, genomic DNA was isolated from lyophilized soybean shoot tips using the method described by Dellaporta in 1993  with minMH designed experiments, performed RNA and DNA extractions and blots, amplified and sequenced BURP gene from CS and CG genotypes, analyzed DGE data for functional categories, and drafted the manuscript; NK performed transcript cloning, RNA blots, analyzed DGE data using DESeq software, analyzed RNA-Seq data, BURP genome sequence data, and drafted sections of the manuscript; MS annotated Glyma models with multiple databases. LOV designed initial approach, led and coordinated the project, and edited the manuscript. All authors have read and approved the final manuscript.Alignment of DGE tags to extended Glyma model and their annotations.Click here for fileThe top 300 genes that are highly expressed in Clark standard and Clark glabrous.Click here for fileDifferential expression from DGE and RNA-Seq of Clark standard and Clark glabrous.Click here for fileDESeq analysis of Clark standard and Clark glabrous.Click here for file


According to health status reports, chronic disease prevalence appears to be rising in western Alaska Native (AN) people, and accurate population-based data are needed. Four cohort studies of western AN people were conducted in the Norton Sound and Yukon-Kuskokwim regions, but none have been large enough to allow reliable estimates of rates of chronic diseases and evaluate their risk factors.In this article, the methods used to combine 4 major cohort studies of rural western AN people are described and the benefits and challenges encountered in combining data and standardizing surveillance methods for these studies are discussed.Tribal permission was obtained for each cohort study and the consolidated study. Data from baseline exams were directly combined or harmonized into new variables. Common surveillance methods were developed and implemented to identify incidence and risk factors for cardiovascular disease (CVD) events and type 2 diabetes.A cohort of 4,569 western AN participants , aged 18–95 years, was established to study CVD and diabetes prevalence. Prospective surveillance data over an average 6.7-year follow-up can now be used to study CVD and diabetes incidence and associated risk factors in a subset of 2,754 western AN participants  who consented to initial surveillance.The combined cohort provides statistical power to examine incidence rates and risk factors for CVD and diabetes and allows for analyses by geographic region. The data can be used to develop intervention programmes in these populations and others. Reports in the mid-1950s suggested a low prevalence of cardiovascular disease (CVD) and type 2 diabetes mellitus (DM2) in western Alaska Native (AN) people living in remote, rural communities of the Norton Sound and Yukon-Kuskokwim regions of Alaska  study combined the 4 major cohort studies: the Alaska–Siberia Project (ASP), the Center for Alaska Native Health Research (CANHR), the Alaska Education and Research Toward Health (EARTH) and the Genetics of Coronary Artery Disease in Alaska Natives (GOCADAN).Overall goals were to create a large representative cohort to describe the prevalence and incidence of CVD and DM2 and examine risk factors influencing these diseases in western AN people. This article describes the original study samples, methods used to consolidate baseline data, and common methods used to identify incident CVD events and DM2 in a representative subset.The WATCH protocol was approved by the University of Alaska Fairbanks, the MedStar Health Research Institute, and the Alaska Area institutional review boards (IRBs). Tribal approval was granted by the Alaska Native Tribal Health Consortium, the Norton Sound Health Corporation, and the Yukon-Kuskokwim Health Corporation.Modelled after the design and methods of the Strong Heart Study , the ASPThe CANHR studies are population-based observational studies to improve understanding of risk factors for obesity, DM2, and CVD ; Gerald The EARTH study is a community-based observational investigation of chronic disease risk factors ; Anne P.GOCADAN is a population-based prospective cohort study with enrolment initiated in 2000–2004 . A total of 1,214 participants were recruited in 9 predominantly Inupiat communities in the Norton Sound region .Three committees were developed for WATCH.The Tribal Coordinating Committee was established to maintain the community-based participatory methods promoted in each original study and to provide community input and oversight. This committee is composed of tribal leaders from the Alaska Native Tribal Health Consortium, Norton Sound Health Corporation, and Yukon-Kuskokwim Health Corporation boards and the Alaska Native Health Board, which represents AN people statewide . It convThe steering committee, comprised of the primary and co-investigators from the original 4 studies and the WATCH operations leader, was designed to oversee WATCH operations. This committee maintained continuous links to tribal communities through the Tribal Coordinating Committee. The steering committee continues to provide a forum in which to obtain information about the original study designs and methods, access external collaborators, assure adherence to the study protocol, and analyze data.The operations committee was responsible for database construction and the WATCH data centre. This committee identified all similar baseline data collected by the original studies and recommended consolidation methods to the steering committee. Operations committee activities are coordinated by the operations leader, who works closely with the WATCH study coordinator, WATCH database manager, and key personnel familiar with the details of data collection and management in the original studies. The steering and operations committees continue to meet monthly by teleconference to review recommended procedures for data analysis and dissemination.The WATCH data consolidation flow for both the prevalence and surveillance phases is shown in The WATCH data set was created by: (a) identifying and consolidating baseline variables; (b) implementing consistent prospective surveillance methods to identify incident CVD events and DM2 cases; and (c) constructing a relational database to permit data export for statistical analyses.The operations committee examined all data codebooks, operations manuals, and questionnaires from the 4 original studies, reviewed the literature comparing methods of data collection, and consulted with clinical and epidemiology experts. Data were first organized by similar topics, including clinical, anthropometric, and laboratory data; demographic and tobacco use information; self-reported medical and family history; self-perceived wellness; self-reported dietary intake; diet-derived nutrient intake; self-reported physical activity; and medical record-abstracted health history.Variable keys were created for each topic area. Variables measuring the same property and having similar collection methods  were combined directly. Variable keys were used to note variations in measurement techniques between studies. Variables measuring similar properties but obtained using dissimilar collection methods  or requiring more than 1 data element , were harmonized into a newly created variable. Some variables (e.g. certain blood metabolites) collected by only 1 study and variables that were too dissimilar to be combined or harmonized were excluded.In general, clinical, anthropometric, and laboratory measurements were retained as continuous data. Minimal harmonization was necessary for these variables. The data were combined once all values were transformed to common measurement units.Self-reported information on lifestyle and medical history was collected by all studies. Although questions and response choices varied, several demographic, tobacco use, personal health history, family history, self-perceived wellness, diet, and physical activity variables were combined or harmonized.A final set of WATCH disease prevalence variables was constructed. For prevalent CVD, the CANHR and EARTH studies reviewed all available medical records at baseline. The presence of diagnosis codes indicating CVD and cardiac procedures in these 2 studies was validated through supporting documents reviewed by trained research nurse abstractors. The ASP and GOCADAN studies limited baseline record review to participants who reported, or whose medical history suggested, a possible prior CVD event at baseline exam. GOCADAN gathered medical record information necessary for at least 2 physicians to adjudicate prior CVD events using standardized criteria. In the ASP cohort, a single physician reviewed medical records at baseline for prior events.diagnosed (present prior to baseline) if a diagnosis for the condition was noted in the participant's medical record. Undiagnosed diabetes, hypertension, and hypercholesterolaemia were defined in all 4 studies using baseline exam measurements in the absence of medication use or a pre-existing diagnosis in the medical record.For the ASP, CANHR, and GOCADAN studies, reports of diagnosed diabetes, hypertension, and hypercholesterolaemia were confirmed by inspection of medications produced by participants at the baseline exam. For the EARTH and CANHR studies, these chronic conditions were The aim of the WATCH surveillance was to use identical methods and to obtain complete ascertainment of all-cause mortality, CVD events, and incident DM2 occurring since baseline. Surveillance using standardized criteria included a review of all events that might be CVD- or DM2-related, followed by systematic adjudication of the participant medical records from the component studies. Follow-up ended in December 2010.Supplementary Table I.) In all cases of possible CVD deaths, records were reviewed for possible non-fatal CVD events during the year prior to the death, with separate adjudication packets created for each possible CVD event following the procedures for morbidity surveillance.Deaths were identified using field contacts, newspaper obituary reviews, and vital statistics reports. Death certificates were requested and the causes of death were recorded.  codes in the medical record indicating hospitalization for a wide range of possible CVD. Relevant information was deidentified, copied, and assembled into adjudication packets. All fatal and non-fatal events were adjudicated by 2 trained physicians, with a third physician consulted to resolve ambiguities. Potential cases were identified by diagnoses or Medical records from CANHR, EARTH, and GOCADAN were also reviewed for incident DM2, defined as: (a) having any laboratory results in follow-up exams (GOCADAN and CANHR) or the medical record that were within the diabetes range  based on 2010 American Diabetes Association criteria ; (b) havThe combined WATCH study cohort with baseline data for prevalence consists of 4,569 study participants . As showConsent for prospective follow-up was not obtained by the ASP. In GOCADAN, all participants agreed to prospective follow-up. In CANHR and EARTH, signed consent to prospectively reviewed medical records was obtained from a subset of participants after initiation of the WATCH study .The WATCH subset consenting to prospective surveillance  consisted of 2,754 western AN people . The age and gender distributions of the total WATCH cohort and the surveillance subset were similar . BaselinThe individual cohorts comprising the WATCH study provided cross-sectional prevalence data for the Norton Sound and Yukon-Kuskokwim regions. Each individual cohort had limited statistical power to assess incidence rates and examine associations between risk factors and chronic diseases. In contrast, the consolidated WATCH cohort provides a broader sample of western AN people and enhanced statistical power. This is the first study to enable analyses of rates of chronic diseases and risk factor associations in western AN people living in the remote, rural communities of the Norton Sound and Yukon-Kuskokwim regions.Consolidation of the baseline data was aided by the similarity in the methods used by the 4 component studies. In cases where methods differed, the annotated variable keys will allow future investigators to analyze and interpret the combined data. Identical surveillance methods provide a resource for obtaining information on incidence rates of CVD and DM2 and assessing risk factors for each condition. This first phase of surveillance averaged 6.7 years; further follow-up will enhance the numbers of outcomes and allow more stratified analyses.The WATCH investigators encouraged full participation of the participating communities. The Tribal Coordinating Committee provided advice and input in setting priorities for analyses of interest to the communities. A primary concern was confidentiality, leading to the double de-identification of the data in the final data set. As with studies in most Native communities, the data and samples belong to the community members; thus, the Tribal Coordinating Committee will continue to serve a key role in future data analyses and ancillary studies.As in all collaborative studies, multiple IRBs and research review committees were involved, increasing the time required for initiation of the study and approvals for abstracts and manuscripts. Twenty-two months were required to obtain approval for data consolidation from all IRBs and tribal approval committees. This extended time can be viewed as a challenge to be met in developing a consortium of studies and obtaining funding. The investigators used this time to make decisions on variable consolidation methods and to design the database. By the time approval was granted, most of the procedures and database construction were in place.The WATCH study's primary strength is the consolidation of 4 cohorts representative of populations in the 2 major areas of residency in western Alaska. Each component study adhered to standardized protocols with careful quality control. Thus, the combined baseline data set is of high quality, without large numbers of missing or outlier variables. The same surveillance and adjudication procedures were used for the component studies, assuring consistent outcome data. The Tribal Coordinating Committee assured input and acceptance by the communities, and all investigators were committed to adhering to the limits set by the communities. There are few examples of similar collaborations, and none have been conducted in an indigenous population. Three collaborations of studies have focused on White populations where data sets were harmonized to compare prevalence of risk factors  or to evAlthough techniques were similar and bias was assessed, the analyses of combined variables may be less precise than the 4 original databases. Future investigators using WATCH data will need to consult the variable keys and consider how the data were collected and consolidated. While much larger than any 1 of its component studies, the number of outcomes for CVD and DM2 in this population remains small. Given the low rate of these outcomes and the relatively young age of the cohort, longer follow-up will increase the power and allow for additional stratified analyses.In summary, a consolidated data set from 4 studies of western AN people has been created, allowing sufficient power to collect and analyze population-based data on CVD and DM2 in this population for the first time. This is an initial step in developing a translational research programme to reduce health disparities in western AN people.


Human studies show widespread population exposure to background levels of phthalates. Limited evidence suggests that particularly high exposure levels may result from orally ingested medicinal products containing phthalates as excipients (inactive ingredients).Background: In animal studies, some Objective: In this study we aimed to identify and describe the scope of prescription (RX) and nonprescription  medicinal products and dietary supplements marketed in the United States and Canada since 1995 that include phthalates as excipients.Methods: We used lists of modified-release drug products to identify potential drug products. Inclusion of phthalates was verified using available electronic databases, print references, published package inserts, product packages, and direct communication from manufacturers. Additional products were identified using Internet searches utilizing keywords for phthalates.Results: Based on labeling information, 6 RX drug products included DBP as an excipient, and 45 specified the use of diethyl phthalate (DEP). Phthalate polymers with no known toxicity—hypromellose phthalate (HMP), cellulose acetate phthalate (CAP), and polyvinyl acetate phthalate (PVAP)—were included in 75 RX products. Three OTC drug and dietary supplement products listed DBP, 64 listed DEP, and > 90 indicated inclusion of polymers.Conclusions: Numerous RX and OTC drug products and supplements from a wide range of therapeutic categories may use DBP or DEP as excipients in oral dosage forms. The potential effects of human exposure to these phthalates through medications are unknown and warrant further investigation. Phthalates, in combination with various polymers, may be used as plasticizers and film coating agents in orally ingested solid pharmaceutical dosage forms and in numerous types of modified-release drug delivery systems such as enteric-coated and delayed-release tablets, pelletized delayed-release capsules, enteric-coated capsules, and controlled-release transdermal films (Inactive ingredients (excipients) are defined as “any component other than the active ingredient in a drug product” (FDA 2011). These various compounds may be used in drug formulations for a number of functions related to the preparation and stability of the physical dosage form or the delivery of active ingredients . Excipieal films .Several different phthalates are currently used as excipients in approved pharmaceutical formulations: diethyl phthalate (DEP), dibutyl phthalate (DBP), dimethyl phthalate (DMP), dioctylphthalate (DOP), hypromellose phthalate (HMP), cellulose acetate phthalate (CAP), polyvinyl acetate phthalate (PVAP), and polyethylene terphthalate (PET) [Food and Drug Administration (FDA) 2010b]. These compounds are not themselves technically “approved” for general use as excipients; rather, they are permitted to be included in their corresponding approved drug products, with maximum levels designated for each type of dosage form and route of administration . The maxNonprescription pharmaceutical  and dietary supplement products may contain excipients found in approved prescription (RX) products; however, their composition is not always reviewed prior to marketing. OTC drug products marketed under monograph guidelines do not require premarket review of their ingredients, but contents are reviewed for products approved under new drug applications (NDAs) and abbreviated NDAs (ANDAs) (FDA 2010a). Dietary supplements are regulated as food products under the Dietary Supplement Health and Education Act of 1994 , and theManufacturers are required to disclose a listing of inactive ingredients in drug product labeling, except when they are used in patented delivery mechanisms where the composition is considered to be a confidential trade secret . Although the FDA might have information on the type and amount of phthalates used in specific approved drug products, they cannot make the information public because of its proprietary nature.In this study we sought to identify and describe the scope of medicinal pharmaceutical products and dietary supplements marketed in the United States and Canada since 1995 that could be confirmed to contain phthalates as excipients based on available product information supplied by regulators, manufacturers, labelers, distributors, and marketers of these products.A search of the published literature on the use of phthalates in orally ingested medications suggested that phthalates are frequently included as inactive ingredients in modified-release drug formulations , as well as in formulations for film coatings. Drug products using these formulations rely on the integrity of their coating systems for proper delivery of their active ingredients and therefore should not be crushed or chewed. Lists of drug products that are not recommended to be crushed or chewed have been published for reference by health care professionals to ensure their proper administration , FDA Approved Drug Products database , DailyMed [National Library of Medicine (NLM) 2011a], and LabelDataPlus . Names of brand and generic drug products marketed since 1995 appearing on the lists were searched within each available electronic or print reference, and all potential modified-release formulations were selected from the results. Once a label was located, we searched the document for all inactive ingredients. If multiple labels were available within a database, we investigated all potential inactive ingredient formulation changes by reviewing the older labels. If an older formulation of the product contained phthalates but the newer formulations did not, we searched all available labels by revision date to determine when the formulation changed. If none of the available compendia provided sufficient label information for a product, we searched the manufacturer or distributor website. If no label was found, we contacted the manufacturer and requested a copy of the package insert.All inactive ingredients for RX medications on the “do not crush list” were first reviewed using a combination of print and electronic resources including the DataPlus . CanadiaDataPlus  and the We performed further Internet searches using terms containing “phthalate” and “medication,” or suspected medication names and drug classes , to locate additional products and product categories. Patents for proprietary drug delivery systems were reviewed for the use of phthalate plasticizers in specific drug products or groups of products.Physicians’ Desk Reference for Nonprescription Drugs, Dietary Supplements, and Herbs , Drugs@FDA, LabelDataPlus, DailyMed, and Dietary Supplements Labels Database (NLM 2011b) either did not include these products or did not provide complete, searchable label information on their inactive ingredients. For these products, we obtained detailed information on inactive ingredients from manufacturer and distributor web sites, or if possible, directly from product package labeling retrieved from store shelves. Canadian dietary supplement products were searched using the Health Canada Licensed Natural Health Products Database , and 50 contained the ortho-phthalates DEP and/or DBP  (and DBP) .Use in OTC drugs and dietary supplements. Enteric coatings, film coatings, and specialized capsule formulations utilizing phthalates were noted for a number of different product categories and purposes in OTC drugs and supplements. These functions included prevention of premature degradation of substances such as enzymes, acid reducers, and probiotics by stomach acid; reduce stomach upset from iron and laxatives; minimize the aftertaste from fish oil and garlic; and produce soft gelatin capsules  that may be easier to swallow or contain solubilized drugs and oils .Thirty-eight of the OTC drug products  and 24 oCanada. Several RX and OTC medications approved for use in both the United States and Canada contained the same inactive ingredients in the product labeling  (ulcolax)  and 2; hulcolax)  did not ulcolax) , 49 liceulcolax) .Changes over time. A handful of products changed the use of specific excipients over time either by removing specific phthalates or adding phthalates to newly approved formulations. Solvay Pharmaceuticals removed DBP from its new FDA-approved formulations of pancrelipase capsules in April 2009. The older formulation, Creon Minimicrospheres,  argued that the new formulations infringed on certain sections of their patent involving the use of phthalates, including DEP and HMP, as coating agents , which required that we seek information directly from the manufacturer. Although the FDA might have a more complete listing of products, including the type and amount of phthalate in specific drug products, this information is considered proprietary and cannot be accessed. Given the thousands of orally ingested products on the market , it is difficult to quantify the prevalence of phthalate-containing products. However, it is informative to identify specific drug products that have included phthalates in their formulations.It is also important to note the limitations and difficulties that were associated with some of the references used to compile the information for this study. Information on the specific excipients used in these products was generally available; however, it was not centrally located, comprehensive, or easily queried.The FDA Approved Drug Products Database (Drugs@FDA) did not contain labels for all products, especially generics, and only some entries contained copies of approval history documents that detail product formulations. Several additional labels were available on LabelDataPlus, DailyMed, or manufacturer web sites, demonstrating that electronic versions of the labels were available but were not linked to the FDA database. For example, detailed label information for prescription and OTC ranitidine tablets from Wockhardt was available on DailyMed and LabelDataPlus but not on Drugs@FDA. The available OTC labels linked these approved products to labeling for several private label distributors ; however, these labelers and distributors often change the source for their private-label products over time. Neither Drugs@FDA nor Health Canada’s Drug Product Database permitted a systematic search of excipients in RX and OTC drug products. LabelDataPlus permitted queries for inactive ingredients; however, it was not comprehensive.Physicians’ Desk Reference  contained some additional product labels, especially older versions of labels and discontinued products ; however, available information was limited to manufacturers who elected to include their products in the reference.The We found several NDAs and ANDAs containing information on excipients using Drugs@FDA. However, because of the proprietary nature of the formulations, in most instances information concerning the type and amount of inactive ingredients used were redacted from the available documents. In LabelDataPlus, the strength of each inactive ingredient was provided for one omeprazole product—Omesec Delayed Release Capsules—listed as containing 0.144 mg DEP  would not only facilitate critical research on the risks and safety of phthalate-containing medications but also would allow prescribers and consumers to select alternative products that are phthalate free. Estimates of exposure from these products can vary according to manufacturer-specific formulation characteristics, including the dosage form, dose, date of use, and country of manufacture. Researchers, consumers, and health care providers may need to contact manufacturers directly to confirm the presence of phthalates and the amounts used in a particular drug product.ortho-phthalates as excipients, including labels of 50 RX, 40 OTC, and 26 dietary supplement product formulations that listed DEP and/or DBP . The potential effects of human exposure to these phthalates through medications are unknown and warrant further investigation. The present findings should assist researchers in conducting the necessary studies of risk and safety of phthalates in human populations, but such efforts are limited by the lack of centralized, comprehensive, and publically available information on the presence of phthalates in the full range of RX, OTC, and dietary supplement products. Future research should pay particular attention to the amount of phthalate, specifically DBP, used in each dosage form so that estimates of exposure from medications and supplements can be quantified.Using a combination of resources, we were able to identify > 100 drug and dietary supplement products that indicated the use of


The Distributed Surveillance Taskforce for Real-time Influenza Burden Tracking andEvaluation (DiSTRIBuTE) project began as a pilot effort initiated by theInternational Society for Disease Surveillance (ISDS) in autumn 2006 to create acollaborative electronic emergency department (ED) syndromic influenza-like illness(ILI) surveillance network based on existing state and local systems and expertise.DiSTRIBuTE brought together health departments that were interested in: 1) sharingaggregate level data; 2) maintaining jurisdictional control; 3) minimizing barriersto participation; and 4) leveraging the flexibility of local systems to create adynamic and collaborative surveillance network. This approach was in contrast to theprevailing paradigm for surveillance where record level information was collected,stored and analyzed centrally. The DiSTRIBuTE project was created with a distributeddesign, where individual level data remained local and only summarized, stratifiedcounts were reported centrally, thus minimizing privacy risks. The project wasresponsive to federal mandates to improve integration of federal, state, and localbiosurveillance capabilities. During the proof of concept phase, 2006 to 2009, tenjurisdictions from across North America sent ISDS on a daily to weekly basisyear-round, aggregated data by day, stratified by local ILI syndrome, age-group andregion. During this period, data from participating U.S. state or local healthdepartments captured over 13% of all ED visits nationwide. The initiative focused onstate and local health department trust, expertise, and control. Morbidity trendsobserved in DiSTRIBuTE were highly correlated with other influenza surveillancemeasures. With the emergence of novel A/H1N1 influenza in the spring of 2009, theproject was used to support information sharing and ad hoc querying at the state andlocal level. In the fall of 2009, through a broadly collaborative effort, theproject was expanded to enhance electronic ED surveillance nationwide. The Distributed Surveillance Taskforce for Real-time Influenza Burden Tracking andEvaluation (DiSTRIBuTE) project is a case example for a new paradigm in the collectionand sharing of public health data The origin and evolution of DiSTRIBuTE was influenced by and responsive to the needs ofpublic health departments and their use of syndromic surveillance systems for influenzamonitoring in their own jurisdictions. The project was consistent with emerging modelsof population health data sharing and with changing federal perspectives on syndromicand biosurveillance architecture. Founded in this context, DiSTRIBuTE was based on a setof core principles: 1) share aggregate level data to minimize risk of exposure ofpersonally identifiable information; 2) maintain jurisdictional control of surveillancedata and information; 3) minimize barriers for health department participation; and 4)follow a collaborative approach to build on the flexibility of local systems and createa dynamic network. The resulting network was built upon the expertise and infrastructureof participating public health departments. In this paper, we set the context and describe the development of DiSTRIBuTE, presentingthe goals and underlying principles behind the project and describing its evolution fromthe autumn of 2006 to the summer of 2009. Finally, we consider the lessons learnedrelevant to the use of syndromic surveillance for national influenza monitoring, andmore generally to the sharing of population health data in the rapidly changing healthinformation technology landscape.Over the last decade, many public health agencies have implemented syndromic surveillancesystems to provide early warning and detailed situational awareness of diseaseoutbreaks, bioterrorist threats, and other ongoing health crises or events Despite the potential advantages of these syndromic systems, they have not been embraceduniversally. This reticence has been due to concerns about the programs’ utility forinitial outbreak and specific disease detection, limited public health funding andworkforce resource constraints, particularly at the state and local level, and tensionsbetween public health and national security priorities As experience increased with the development and application of syndromic surveillance,evidence from health departments began to emerge that these data could provide importantinformation at the local and regional levels to improve monitoring of seasonal andepidemic influenza In the US, ILI surveillance has traditionally been conducted through a volunteer sentinelphysician network by the Centers for Disease Control and Prevention (CDC) andcoordinated through state and several large metropolitan health departments The CDC ILI network is a highly valued public health surveillance system and serves thecritical roles of monitoring ILI trends and collecting viral samples in all 50 states.Potential benefits of syndromic surveillance, however, over the traditional sentinelphysician network include: faster provision of data due to electronic, automated datasubmission; lower burden on healthcare providers who would otherwise have to reportmanually; better stability of data, since providers might otherwise drop out or havedelays if reporting manually; year-round reporting, unlike the sentinel system whichonly receives reports from the majority of participants during influenza season;availability of age-specific denominator data; flexible case definitions; and in manyjurisdictions, potential for greater population coverage than with the sentinelphysician network.The DiSTRIBuTE effort was based on a practical philosophy of data sharing and on two keyobservations. First, for legal and organizational reasons, public health departments canshare non-specific surveillance information and aggregate level data more easily thanpatient level records from health facilities within their jurisdictions. Second, publichealth departments are more willing to share information and data on disease patternsamong trusted collaborators, particularly when only the minimum data needed to answerthe public health questions are provided, and where jurisdictional control and authorityover data are maintained. Previous national syndromic surveillance efforts, such as theoriginal BioSense system, were top-down models which relied on centralized aggregationof detailed personally identifiable information, were not developed collaboratively and,consequently, were disconnected from the practical needs and resources of public healthdepartments In 2006 and 2007, new federal level biosurveillance and pandemic preparednessrecommendations, notably Homeland Security Presidential Directive, HSPD-21 Drawing on experiences from multiple stakeholders, Markle Connecting for Health proposedprinciples intended to facilitate the sharing of population health data in support ofeffective decision-making The DiSTRIBuTE project emerged out of the unique collaborative environment of ISDS, whichfosters the creation of interdisciplinary, cross-agency collaborations that bridgeresearch, practice and policy. The development of the project was also influenced bycurrent thinking around models for sharing population health data, developing experiencewith syndromic surveillance for influenza, and the evolving national biosurveillancepolicy landscape in the United States.At the outset of the DiSTRIBuTE project, limitations in data sharing were evident innational influenza surveillance and biosurveillance practices, as reflected by positionstatements from within the public health community The primary goals of DiSTRIBuTE were to establish the feasibility of sharing aggregatepopulation health data; and to assess the utility of regional and national sharing of EDsyndromic surveillance data for influenza surveillance. These goals were also in linewith the Markle Connecting for Health principles In order to achieve the original goals of the project, a core set of principles werefollowed. These are summarized in Box 1, and discussed below.Use Aggregate Data. Collection of aggregated data from health jurisdictions hasadvantages over individual-level raw data because it can sufficiently representpopulations while protecting personally identifiable information. DiSTRIBuTE employed asimple data format that included an agreed upon minimum level of data detail for theepidemiological question and public health action of monitoring febrile, respiratory andILI syndromes at the population level.Maintain Jurisdictional Control. Public health departments are more likely to sharesurveillance data among trusted collaborators in an environment that protectsjurisdictional control and where policies are defined in a participatory manner. Theintent of this principle was to ensure that both the data framework and the exchange ofsurveillance findings and interpretation were suitably controlled by the participatingjurisdictions.Minimize Barriers to Participation. The use of a simple aggregate data format minimizedone barrier to participation in the project.  Participants were initially asked tosubmit counts of cases measured according to the definitions and standards used in theirexisting systems. This flexibility lowered the barrier to entry into the project,deferred to state and local authority, and leveraged existing practices with localsyndrome definitions and standards. This approach acknowledged the role of local contextin extracting public health information from local clinical data. In other words, weassumed that local syndromes were defined based on regional variations in datacollection standards, idiom, language, syndrome coding, hospital information systems, aswell as other factors, and we wanted to build on this experience and expertise. Create a Collaborative Network. Data exchange and information sharing in the DiSTRIBuTEproject was community-based, and participating jurisdictions were collaborativelyinvolved in the development, specification, and implementation of the system, and in thedata analysis and interpretation. The intent of this principle was to ensure that thedata exchange framework was based on jurisdictional needs and priorities, and to fosterdata sharing among trusted collaborators in an environment where they defined thepolicies and controls in a participatory manner.DiSTRIBuTE began with the comparison of syndromic surveillance trends betweenjurisdictions and with the sharing among syndromic surveillance practitioners of systemcoding and specifications. The public health utility of comparing influenza surveillancetrends and the relative ease of sharing aggregate data, programming code, and systemspecifications between a small number of participant sites suggested that a frameworkand system could be created to scale the effort to a larger group of local, state, andinternational participating health departments.Beginning in 2006, the DiSTRIBuTE project sought to enroll health departments thatconducted ED-based syndromic influenza surveillance in their jurisdictions tovoluntarily share their data Data characteristics and specifications in DiSTRIBuTE were originally aimed at capturingaggregate daily total and influenza-related syndrome counts of ED visits by predefinedage-group and three-digit ZIP-code (ZIP-3). Participant sites reported aggregate countsby groups covering infants and toddlers (age <2 yrs), preschool-age (2-4 yrs),school-age (5-17 yrs), working-age adults in younger (18-44 yrs) and older (45-64 yrs)groups, and senior citizens (age 65+ yrs). Geographic information was originallyrequested to capture patients’ reporting ZIP-3, however, some participating sites choseinstead to submit data based on ED facility ZIP-3, or to aggregate to larger regionalareas than ZIP-3 . Thefebrile, respiratory and influenza-like syndromes initially used in the project wererequested to be comparable to a commonly used “fever/flu” syndrome that many participantsites were familiar with, and which many were currently using for surveillance in theirjurisdictions While participating sites generally did not use identical syndrome groupings to monitorinfluenza-related ED visits in their jurisdictions, the data were requested to besubmitted as the preferred local syndrome grouping used by the health departments, withthe intent of representing the patterns and trends that the jurisdiction wished to haveshown for their region. During the proof of concept period, as sites shared data andobserved each other’s trends, there was interest in comparing syndromes based onconcepts rather than strictly on defined coding standards In a pilot comparison of two DiSTRIBuTE jurisdictions, each used the other’s syndromecoding and applied it to their own data to compare the resulting surveillance trendswith the syndromes they used locally. The results suggested that using the locallydefined syndromes created surveillance time-series that better matched the viral isolatedata of confirmed influenza cases locally ad hoc comparisons were done within the network, notablywith respect to an assessment of DiSTRIBuTE data with Google Flu Trends During the proof of concept period, work focused on comparison of influenza surveillancesystems, evaluation of the model, and harmonization of common ILI syndromes The technical infrastructure to initially support DiSTRIBuTE employed an incrementalapproach to automating and securing the manual processes initially used in the pilot,and then by early participants. Data were originally exchanged by email attachmentsusing comma separated value (CSV) files in similar formats. An automated system wasimplemented to permit secure file transmission using the secure shell file transferprotocol (SFTP) and utilizing existing commercial software at the health jurisdiction,or equivalent free software identified by the project. The users sent their data filesto a central server where automated software services received the data files, performedbasic error detection , and transformed the data into a common format . The resulting files shared acommon syntax that were free of both format and simple content errors. Examples of thelatter included violations of simple data type, basic range checking of results, andconstrained enumerated values (such as age ranges for age stratification), and were freeof format and simple content errors, including violations of simple data type. Thestandardized files were parsed and their content imported into a simple database, whichwas implemented using the open source MySQL database package. Open source software wasemployed for the entire centralized server, using the "Linux, Apache, MySQL, PHP, Perl"development framework.The display of DiSTRIBuTE trends was created as the primary means for sharing andpresenting data from participating health jurisdictions. Initially, weekly aggregateratios of febrile respiratory or influenza-like syndromes, based on each participatingsite’s routine syndromic criteria for monitoring seasonal influenza, to total ED visits,were visualized as regional weekly time-series , and as age-specific temporalepidemic response surface (TERS) plots . The regional time-series plotspresented variation in relative baseline levels during non-influenza periods during theproof of concept period, from lower quartile weekly ratios of 1.4% to over 6% of totalED visits. Peak seasonal epidemic influenza levels by participating health departments,ranged from 6% to over 16%. The variation in non-influenza period baseline levels andpeak seasonal epidemic levels indicated that the overall magnitude of the data were notdirectly comparable. However, the relative pattern of the time-series reported byparticipating systems were noted as being consistent with regional, state and localinfluenza surveillance systems and measures While participating DiSTRIBuTE sites were not representative of the whole nation on apopulation basis, taken as an aggregate 13% convenience sample of the U.S., the data forthe 2006-2007 and 2007-2008 influenza seasons were highly correlated with national ILIsurveillance data FIGURE 1 - DiSTRIBuTE time-series visualizations by jurisdiction, February 16,2009. ILI syndrome time-series are plotted as ratios for DiSTRIBuTE and national CDCsentinel ILI, and as counts by subtype for viral influenza isolate data FIGURE 2 - DiSTRIBuTE age-specific visualization, February 16, 2009. ILI syndrometime-series as age-specific temporal epidemic response surface (TERS) plots for2006-2009 are shown as relative increase, calculated as observed over the lower-quartilebaseline ratio by age-group and jurisdiction The age-specific visualizations presented participant jurisdiction data as aninterpolated surface gradient of the relative magnitude of visits, calculated asobserved ratios over lower-quartile baseline, by age-group through time National surveillance trends in ILINet from 2008 and later were highly correlated withDiSTRIBuTE data, however ILINet included three DiSTRIBuTE participating healthdepartment ED systems during this period. The inclusion of these three state and localsyndromic systems represented a large portion (roughly 27%) of the total national ILINetvisit volume, and this resulting overlap prevents direct comparison of the systemswithout disaggregating the regional data. A suitable gold standard for evaluation ofpopulation level influenza trends, whether from clinical sentinels or syndromic ED data,is time-series of laboratory confirmed influenza infections. The national viral isolatedata, as a proportion of weekly viral tests positive for influenza, were highlycorrelated with the combined DiSTRIBuTE data .FIGURE 3 - DiSTRIBuTE and CDC ILINet and viral surveillance data, 2007-2009.DiSTRIBuTE ILI syndrome time-series are shown as ratios, with national CDC sentinel ILIand viral influenza isolate data www.syndromic.org, and throughmeetings, webinars and presentations. Later visualizations were presented on a userwebsite hosted at the University of Washington, where a series of automated queries andstatistical and graphing routines were implemented as scripts in the R statisticalprogramming language and run on a daily basis to produce images for both public andrestricted-access web applications. The public site showed time series of daily andweekly syndrome ratios. The restricted site was accessible by only those jurisdictionscontributing data to DiSTRIBuTE . It included both daily and weekly ratios forseveral ILI and gastroenteritis syndromes, as well as their constituent data, syndromecounts and visit totals.Originally, visualizations and findings were summarized and disseminated via email, onthe ISDS website As the number of participant sites grew, features were added to enhance the ability tocreate custom visualizations, as well as to enter descriptive data, or metadata,specific to each site. These data included information needed to manage the creation ofcombined or composite time series, control the flow of data based on specific terms ofparticipation for particular sites, specify order of appearance for menu items andvisualizations, provide denominator information such as estimates of the populationcovered or number of hospitals in a particular jurisdiction, and contact information forepidemiologists and IT staff with each jurisdiction and site. The development and evolution of the DiSTRIBuTE project changed dramatically with theemergence of novel A/H1N1 influenza in April of 2009. With the first influenza pandemicin over 40 years, the demands on local, state, and national public health practiceincreased. There were notable lessons learned with the DiSTRIBuTE effort during theemergence and progression of the pandemic in the spring of 2009 and with the publichealth concern over its anticipated recrudescence the following summer and fall – theselessons are presented in the sections below. The DiSTRIBuTE findings and the federalsurveillance needs in response to the pandemic The DiSTRIBuTE project demonstrated that it was feasible to share population health ILIdata nationally in a manner that addresses the needs of local jurisdictions The principle of keeping barriers to participation low facilitated the growth of theDiSTRIBuTE network. One of the other apparent effects of this principle was that datasubmitted by different regions often reflected different syndromic definitions, withvarying sensitivity and specificity for monitoring conditions of interest. Someepidemiologists and other observers perceived this difference as a limitation to overalldata quality, particularly with regard to comparing data .across regions. However,others viewed the heterogeneity as something that was recognized and accounted for bythe jurisdictions, where definitions were derived empirically or in response to a localsurveillance need. Recognition of these differing perspectives led almost immediately toongoing collaborative assessment of syndrome definitions and other surveillancepractices.The success of the DiSTRIBuTE project in demonstrating the feasibility of implementing aninnovative low cost model for national syndromic influenza surveillance suggests that itis worth considering expansion of this model to other surveillance activities and toother fields in public health and health care. In DiSTRIBuTE, the data collectionprocess was generalized to acute gastroenteritis syndromes, and potential expansion, forinstance, to the monitoring of quality of care would be more ambitious and rewarding. Infact, elements of the DiSTRIBuTE model could conceivably be applied to any system thatrelies on collaborative networks, rigorous data collection, detailed and comprehensivedata, and ongoing technical support.The lessons regarding standardization of influenza surveillance are also instructive. Theissue of standardization remains a major challenge for syndromic surveillance. Whilerelative disease patterns and trends can be compared between systems with heterogeneoussyndrome definitions, the ability to compare more direct epidemiological measures islimited. ISDS and collaborators continue to work on strategies to find common ground forsyndrome definitions and coding that can inform national agendas for syndromicsurveillance.Providing an emphasis on establishing data transmission while accepting variations indata standards and then allowing the community to move to collaborative harmonizationappears to be a viable approach and one that offers an alternative to top-downstandards-setting approaches, which can be slow to complete and result in high technicalbarriers to participation. It also needs to be acknowledged that population surveillanceof aggregate data has inherent limitations for queries and analysis at deeper levels ofdetail.The question of cost is also important to consider. The DiSTRIBuTE model demonstratedthat a national network can be organized to support existing state and local systems,expertise and infrastructure for both public health surveillance and emergencypreparedness and response with modest funding . For the contributing systems, the necessarycosts incurred by state and local jurisdictions to support syndromic surveillance wereoffset in part or in total through federal funding by the CDC through Public HealthEmergency Preparedness (PHEP) cooperative agreements, and Public Health EmergencyResponse (PHER) grants, and through foundation support or direct spending by thejurisdictions themselves. While the total costs required for developing and operatingthe existing infrastructure of state and local syndromic surveillance through federalPHEP and PHER funding is unknown, federal level efforts to create a national centralizednetwork, such as BioSense, cost roughly $30 million per year during the period 2003 to2009 During the period from 2008 to 2009, the participating DiSTRIBuTE jurisdictionsrepresented over 16% of the US population, and captured roughly 13% of all ED visitsnationwide. By early 2011, the network had grown to over 43 reporting sites and capturesover 40% of all ED visits. The DiSTRIBuTE project has changed the practice of syndromicsurveillance in the US. It promoted an assessment of practice patterns and helped toidentify variations among different public health jurisdictions, while offering nationalperspective and peer comparisons. DiSTRIBuTE has been useful for understanding thecurrent landscape of syndromic  surveillance, enhancing data quality, and creating aframework that can be applied to syndromes beyond influenza.www.syndromic.org).In 2009, DiSTRIBuTE was identified as a case example in a White House recommendation bythe Presidents Council of Advisors on Science and Technology for implementation of anationwide ED surveillance network as part of the preparedness and response effort tothe 2009 influenza A/H1N1 pandemic The significant progress in population level syndromic surveillance that the DiSTRIBuTEproject made during the proof of concept period from 2006 to 2009 demonstrated that anational network can be created with modest funding and without unnecessary exposure ofrecord level information or unnecessary burden on health practitioners – and all whilesupporting existing state and local public health systems and capacity and providingrapid, high volume, summary electronic surveillance. Electronic data processes andapproaches like this can not replace clinician reporting or laboratory testing ofclinical samples collected by sentinel physicians, but they can augment these systems ina way that confirms and expands our understanding of population level disease trends.Tremendous potential remains for DiSTRIBuTE to continue to improve the nation’s abilityto prepare for, monitor and respond to disease outbreaks.The authors wish to acknowledge the initiation, insight, guidance, and support on thisproject from founding ISDS President Farzad Mostashari. During the proof of conceptperiod, the ISDS Board of Directors provided institutional oversight and guidance (2006to 2010): John S. Brownstein, David L Buckeridge, Howard S. Burkom, Jean-Paul Chretian,Duncan Cooper, Julia Gunn, Lori Hutwagner, William B. Lober, Joe Lombardo, FarzadMostashari, Marc Paladini, Julie Pavlin. The authors thank Laura Streichert forassistance with editing the manuscript.Participants in the ISDS DiSTRIBuTE Working Group meetings provided critical guidance andcontent expertise during the project, from proof of concept to nationwideimplementation. Participants and data contributing sites included the following: NewYork City Department of Health & Mental Hygiene, Marc Paladini, Don Olson, RichardHeffernan; Boston Public Health Commission, Justin Pendarvis, Julia Gunn; Public Health- Seattle & King County, Atar Baer; Virginia Department of Health, Michael A.Coletta; Georgia Division of Public Health, Karl Soetebier, Erin L. Murray; NorthCarolina Division of Public Health, Lana Deyneka, Amy Ising, Jeffrey Engels; IndianaDepartment of Health, Ryan Gentry, Michael Wade; Utah Department of Health, MelissaDimond, Felicia Alvarez, Robert T. Rolfs; Washington State Department of Health, BryantThomas Karras; Kingston, Ontario, Canada, Kieran Moore. Working Group participants withcollaborating academic or non-profit groups: University of Washington, William B Lober,Debra Revere, Ian Painter; Johns Hopkins, Howard S. Burkom; Markle Foundation, JillSchulmann, Carol Diamond. Participants with data submission in-progress or ad-hoc duringthe proof of concept period, 2006 to 2009: New York State Department of Health,Geraldine Johnson, Hwa-Gan Chang; Maine Department of Health, Amy Robbins; VermontDepartment of Health, Pam Berenbaum; Duval County, Florida, Taj Azarian, Saad Zahir;Houston, Patricia Sinawe; Cook County, Illinois, Megan Patel; North Dakota Department ofHealth, Julie Wagendorf, Lindsey Vander Busch; Wisconsin State Department of Health,Richard Heffernan; Connecticut State Department of Health, Alan Siniscalchi, KatherinePurviance; Florida State Health Department, Aaron-Kite Powell; Ohio Department ofHealth, Brian Fowler, Richard Thomas; INSERM and UMPC, Paris, France, Camille Pelat,Alain-Jacques Valleron.During the H1N1 pandemic, the US CDC played a vital role in the expansion of Distributefrom a proof of concept project to a nationwide project that currently includesforty-two participating public health jurisdictions. Beginning in August 2009, the groupthat is now the Public Health Surveillance and Practice Office (PHSPO) of the CDCprovided, through the BioSense program and other mechanisms, substantial support tothe Distribute project in terms of funding; allocation of personnel; site outreach,recruitment and technical assistance; and coordination with other groups at CDC,including the Influenza Division. As of August 2011, more than one third of thejurisdictions participating in Distribute submit their data via CDC (either throughBioSense or through reporting data directly to CDC). In summary, the CDC providedsupport that was necessary for the rapid expansion of Distribute in the Fall of2009, and the CDC continues to support and fund the ISDS Distribute project and itscommunity of practice.The participating local and state public health agencies played a vital rolein Distribute before and during the pandemic period. Through their voluntary anddedicated participation, the system rapidly expanded in 2009 from 10 to 35 contributingstate and local health jurisdictions. Many of the approaches to improving data quality,analysis, and visualization were initiated by staff from these agencies, who devotedsignificant time and energy, as well as local IT resources, to ensuringthat Distribute could expand to provide National coverage during a public healthemergency.The development and proof of concept phase of the ISDS DiSTRIBuTE project was supportedby the Centers for Disease Control and Prevention (CDC) through a cooperative agreementwith the National Association of County and City Health Officials (NACCHO), 2006 to2008, and the Markle Foundation, 2008 to 2009 (grant #081005BP-Q). Implementation andexpansion of DiSTRIBuTE into a nationwide system was supported by the Markle Foundation,2009 to 2010 (grant #101003BP-B), and by the CDC through a cooperative agreement withthe Public Health Informatics Institute (PHII), 2009 to 2011.ICMJE criteria for authorship read and met, DRO, MP, WBL, DLB; agree with themanuscript’s results and conclusions, DRO, MP, WBL, DLB; wrote the first draft ofthe paper, DRO and MP; revised the paper for substantive content and interpretation,DRO, MP, WBL, DLB. Contributed to the roundtable meetings and discussions thatcontributed to the development of the project, led to the paper, and were providedopportunity to review and revise paper, ISDS DiSTRIBuTE Working Group members.The authors have declared that no competing interests exist.


Green space is now widely viewed as a health-promoting characteristic of residential environments, and has been linked to mental health benefits such as recovery from mental fatigue and reduced stress, particularly through experimental work in environmental psychology. Few population level studies have examined the relationships between green space and mental health. Further, few studies have considered the role of green space in non-urban settings. This study contributes a population-level perspective from the United States to examine the relationship between environmental green space and mental health outcomes in a study area that includes a spectrum of urban to rural environments. Multivariate survey regression analyses examine the association between green space and mental health using the unique, population-based Survey of the Health of Wisconsin database. Analyses were adjusted for length of residence in the neighborhood to reduce the impact of neighborhood selection bias. Higher levels of neighborhood green space were associated with significantly lower levels of symptomology for depression, anxiety and stress, after controlling for a wide range of confounding factors. Results suggest that “greening” could be a potential population mental health improvement strategy in the United States. The presence of green space, such as forests and parks, is now widely viewed as a health-promoting characteristic of residential environments, and has been linked to benefits such as recovery from mental fatigue ,4,5,6,7,Some studies have demonstrated that the relationship between green space and health is stronger among lower socioeconomic status groups, the elderly, and others who stay home during the day, providing some support for the notion that the size of the effect of local green space on health is related to the amount of an individual’s exposure to the local environment ,19. ThesHowever, while green space holds great promise as an innovative, place-based solution to improving population health, understanding where and for whom green space confers health benefits has proven complex. Some evidence and theoretical guidance has suggested that several main pathways may be important in linking green space exposure to health benefits. Green space can have direct protective effects against health hazards posed by air pollution, extreme temperature, and noise pollution ,25; has The mental health benefits conferred by green space are of particular interest given a growing body of knowledge that emphasizes stress responses as a main link between neighborhood conditions and health outcomes. Attention Restoration Theory posits that experiences in natural environments can reduce mental fatigue and restore the capability for directed attention. Directed attention is employed “when something (does) not of itself attract attention, but when it (is) important to attend nonetheless” . Maintaiet al. (2010) identified effect sizes and significant levels indicative of improvement in energy, anxiety, anger, fatigue and sadness with exposure to natural environments [Numerous studies, including a large body of experimental work in the field of environmental psychology, have empirically linked nature exposure with attention restoration and stress reduction ,3,4,5,6,ronments , with leronments ,10 and wronments ,31. Previous work has suggested that greenspace may be linked to lower levels of depression, anxiety and/or stress, and that more research is needed ,42. MoreGiven an evidence base suggesting associations between environmental green space and both physical and mental health outcomes in non-US settings, the potential for green space to contribute to reductions in health inequities, and the significant and persistent patterns of place-based health inequities associated with the history of residential racial segregation in the United States, there is a great need for more research examining the potential role for green space as a population health intervention in the US. Further, few studies have considered the role of green space in non-urban settings. This study contributes a population-level perspective from the United States to examine the relationship between environmental green space and mental health outcomes in a study area that includes a spectrum of urban to rural environments. In addition, in recognition of previous work that has found variation in impact of different types of green spaces  and a boThis is a cross-sectional study of the relationship between neighborhood green space and mental health among a probability sample of Wisconsin residents. We analyzed data from the Survey of the Health of Wisconsin (SHOW) database. SHOW is an ongoing survey, established in 2008, which is modeled after the National Health and Nutrition Examination Survey (NHANES). It includes information collected through interviews, physical exams, and biospecimens from a representative sample of Wisconsin residents. Records are geocoded by street address to allow linkage with physical and social environmental characteristics. Details of SHOW methods and rationale have been published previously . BrieflyOur outcome measures comprise the three scales of the 42-item Depression Anxiety and Stress Scales (DASS) instrument , indicatWe controlled for a range of individual level characteristics that may affect the relationship between green space and health ,23,26 inThe US census block group is a geographical unit smaller than the census tract, containing approximately 600 to 3,000 people , and is To measure socio-demographic characteristics of neighborhoods, we used tract level information for years closely aligned with our survey data, from the American Community Survey for the years 2007–2011, using Tiger/Line 2011 census tract boundaries. Consistent with previous research , we contNeighborhood greenness was operationalized using the normalized difference vegetation index (NDVI), which has been shown to be effective in capturing neighborhood greenness for epidemiologic purposes  and is aTree canopy coverage was measured using 2001 Percent Tree Canopy data from the National Land Cover Database . This isr = 0.83), we modeled each of the three outcomes using three different continuous measures of neighborhood greenness—NDVI, percent tree canopy, and average of NDVI and percent tree canopy. Multivariate linear regression models were adjusted for all individual level  and neighborhood level  factors. In addition, models were adjusted for length of residence of the respondent in the neighborhood environment measured. Confounders were incorporated as dummy variables in regression analyses. Multicollinearity was assessed using a criterion of variance inflation factor >10, but no problems were detected.All statistical analyses were undertaken in Stata SE/12  and accoSample characteristics and unadjusted, bivariate analyses are displayed in Higher proportions of publicly insured and lower proportions of privately insured individuals lived in areas with less than 10% tree canopy. Race and ethnicity, educational attainment, and occupational status were not significantly different between the two types of environments. Continuous scores for depression, anxiety and stress differed significantly, with higher levels of each reported in areas with less than 10% tree canopy.Results indicated a largely consistent finding that higher levels of neighborhood green space are associated with better mental health outcomes, when considering a range of possible confounding factors. Results from analyses examining proportion tree canopy are presented in As shown in Educational attainment was only associated with anxiety, with evidence of a gradient effect, with individuals with less than a high school education reporting more anxiety and individuals with post-graduate education reporting less anxiety compared to a reference group of respondents who had some education post-high school but had not acquired a college degree. Income was related to all three mental health outcomes, with those making less than $20,000 per year reporting more symptoms than those making $75,000 or more per year.Those who had never been married reported lower levels of stress; some evidence suggested that those who were divorced, separated or widowed experienced more symptoms of depression. Those uninsured, publicly insured, or with multiple sources of insurance reported more symptoms of depression than those privately insured. Only those with multiple sources of insurance reported significantly higher levels of anxiety. Those publicly insured, with multiple sources of insurance, or “other” sources reported higher levels of stress.Several neighborhood environmental factors were associated with poor mental health symptomology. Compared to a metropolitan reference group, individuals living in micropolitan settings reported higher levels of symptomology for depression and stress. In addition, those in rural areas reported higher levels of depression and anxiety. Higher neighborhood unemployment was associated with higher levels of anxiety and stress, while higher median household income was associated with higher levels of depression. Higher levels of residential racial segregation were also associated with depression, anxiety and stress. Higher proportions of owner occupied housing were associated with lower levels of depression. Population density, proportion with high school degree, residential instability, and poverty were not associated with any of the outcomes examined. Length of residence in a neighborhood was not significant in any of the models.Neighborhood green space was consistently associated with lower levels of depression, anxiety and stress, when controlling for all individual and neighborhood characteristics, as displayed in To enhance interpretability of our findings, we present coefficients illustrating the difference in symptom levels for a 25% change in greenspace in We found in our sample that higher levels of neighborhood green space correspond to better mental health outcomes, when controlling for a wide range of confounding factors. The associations between green space and mental health are significant and sizeable and persist with different measurement techniques. Furthermore, the estimated effect of environmental green space is similar in magnitude to that of other well-known and studied contributors to symptomology for depression, anxiety and stress. For example, results indicate that the difference in depressive symptoms between an individual living in an environment with no tree canopy and an environment with 100% tree canopy is larger than the difference in symptoms associated with an individual who is uninsured compared to an individual with private insurance.This study also revealed interesting differences in access to percentage of tree canopy based on demographic variations in population length of residence, age, income, marital status, and insurance type. Our multivariate adjusted results highlight that those from lower income brackets and without private health insurance experience greater anxiety, stress, and depression, thus supporting the notion that low socioeconomic populations could benefit more from increased exposure to green space . This stAnother important contribution of this study is the examination of neighborhood environmental factors other than green space. The correlation between higher unemployment levels and greater anxiety and stress and the correlation between higher levels of residential segregation and increased anxiety, stress, and depression support the need for research on “greening” and other neighborhood strategies to counteract spatial aspects of socioeconomic disadvantage ,19,56,63r = 0.83), we assume that temporal misalignment does not alter the results of the study significantly. Similarly, the unavailability of Census Block Group information and resultant use of Census Tract data for estimating neighborhood socioeconomic characteristics may introduce some error stemming from spatial misalignment of predictors and outcomes.Strengths of this study include the population-based sample, the use of state-of-the art and detailed measures of green space, the use of well-known and validated outcome measures (DASS instruments), and the consideration of a large number of covariates to control for confounding influences. Several potential limitations of this study are worth considering. SHOW data are based on self-reported health outcomes and non-response might introduce selection bias. The use of SHOW data from 2008–2011 with satellite data from 2009 and tree canopy data from 2001 might introduce some error stemming from temporal misalignment between survey responses and neighborhood greenness. We limited NDVI analyses to 2009 due to the availability of images that were not significantly affected by cloud cover. As NDVI calculations for 2009 and percent tree canopy for 2001 are highly correlated (A final and important limitation is the problem of reverse causality or neighborhood selection bias, whereby it is possible that associations found could in part be attributable to populations with fewer mental health problems moving into greener environments, given that this is a cross-sectional study . While tPrevious work has identified “a positive relation between perceived general health and both agricultural green and natural green in a person’s living environment” . BuildinGiven an evidence base suggesting associations between environmental green space and both physical and mental health outcomes in non-US settings, this work provides further evidence to support neighborhood level greening as a potentially important intervention for improving health and reducing persistent disparities. Furthermore, because of significant and persistent patterns of place-based health inequities associated with the history of residential racial segregation in the US, which often coincide with adverse social and build environments, there is a great need for continued research examining greening as a population health intervention in the US.