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We implemented six confounding adjustment methods: (1) covariate-adjusted regression, (2) propensity score (PS) regression, (3) PS stratification, (4) PS matching with two calipers, (5) inverse probability weighting and (6) doubly robust estimation to examine the associations between the body mass index (BMI) z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding v. formula only (n=437) and cesarean section v. vaginal delivery (n=1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were −0.33 (95% CI −0.53, −0.13) and −0.24 (−0.46, −0.02), respectively. The other approaches resulted in smaller n (204–276) because of poor overlap of covariates, but CIs were of similar width except for inverse probability weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from −0.01 to −0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method. [ABSTRACT FROM PUBLISHER]

Summary: Guidelines recommend screening for osteoporosis with bone mineral density (BMD) testing in menopausal women, particularly those with additional risk factors for fracture. Many eligible women remain unscreened. This randomized study demonstrates that a single outreach interactive voice response phone call improves rates of BMD screening among high-risk women age 50-64. Introduction: Osteoporotic fractures are a major cause of disability and mortality. Guidelines recommend screening with BMD for menopausal women, particularly those with additional risk factors for fracture. However, many women remain unscreened. We examined whether telephonic interactive voice response (IVR) or patient mailing could increase rates of BMD testing in high risk, menopausal women. Methods: We studied 4,685 women age 50-64 years within a not-for-profit health plan in the United States. All women had risk factors for developing osteoporosis and no prior BMD testing or treatment for osteoporosis. Patients were randomly allocated to usual care, usual care plus IVR, or usual care plus mailed educational materials. To avoid contamination, patients within a single primary care physician practice were randomized to receive the same intervention. The primary endpoint was BMD testing at 12 months. Secondary outcomes included BMD testing at 6 months and medication use at 12 months. Results: Mean age was 57 years. Baseline demographic and clinical characteristics were similar across the three study groups. In adjusted analyses, the incidence of BMD screening was 24.6 % in the IVR group compared with 18.6 % in the usual care group ( P < 0.001). There was no difference between the patient mailing group and the usual care group ( P = 0.3). Conclusions: In this large community-based randomized trial of high risk, menopausal women age 50-64, IVR, but not patient mailing, improved rates of BMD screening. IVR remains a viable strategy to incorporate in population screening interventions. [ABSTRACT FROM AUTHOR]

This paper examined whether previously identified community-level factors (high proportion of crowded households and/or persons below the poverty level) remained associated with childhood pneumococcal carriage in the heptavalent pneumococcal conjugate vaccine (PCV7) era. Using logistic regression, individual factors were used to develop base models to which community-level factors were added to evaluate impact on pneumococcal carriage within two paediatric study cohorts from Massachusetts (urban Boston, outside Boston). Six years after introduction of universal childhood PCV7 vaccination, we found no consistent evidence that census tract characteristics (e.g. population size and density, age and race distribution, percent participating in group childcare, parental education, percent lacking in-unit plumbing, poverty, and community stability) affected odds of pneumococcal carriage when added to individual predictors (e.g. younger age, current respiratory tract infections, and attendance in group childcare). How community-level factors influence pneumococcal carriage continues to change in the era of increasing immunization coverage. [ABSTRACT FROM PUBLISHER]

Among US racial/ethnic minority women, we examined associations between maternal experiences of racial discrimination and child growth in the first 3 years of life. We analyzed data from Project Viva, a pre-birth cohort study. We restricted analyses to 539 mother–infant pairs; 294 were Black, 127 Hispanic, 110 Asian and 8 from additional racial/ethnic groups. During pregnancy, mothers completed the Experiences of Discrimination survey that measured lifetime experiences of racial discrimination in diverse domains. We categorized responses as 0, 1–2 or ⩾3 domains. Main outcomes were birth weight for gestational age z-score; weight for age (WFA) z-score at 6 months of age; and at 3 years of age, body mass index (BMI) z-score. In multivariable analyses, we adjusted for maternal race/ethnicity, nativity, education, age, pre-pregnancy BMI, household income and child sex and age. Among this cohort of mostly (58.2%) US-born and economically non-impoverished mothers, 33% reported 0 domains of discrimination, 33% reported discrimination in 1–2 domains and 35% reported discrimination in ⩾3 domains. Compared with children whose mothers reported no discrimination, those whose mothers reported ⩾3 domains had lower birth weight for gestational age z-score (β −0.25; 95% CI: −0.45, −0.04), lower 6 month WFA z-score (β −0.34; 95% CI: −0.65, −0.03) and lower 3-year BMI z-score (β −0.33; 95% CI: −0.66, 0.00). In conclusion, we found that among this cohort of US racial/ethnic minority women, mothers’ report of experiencing lifetime discrimination in ⩾ 3 domains was associated with lower fetal growth, weight at 6 months and 3-year BMI among their offspring. [ABSTRACT FROM PUBLISHER]

Objective: To examine characteristics of the transition from pediatric to adult care in emerging adults with type 1 diabetes and evaluate associations between transition characteristics and glycemic control.Research Design and Methods: We developed and mailed a survey to evaluate the transition process in emerging adults with type 1 diabetes, aged 22 to 30 years, receiving adult diabetes care at a single center. Current A1C data were obtained from the medical record.Results: The response rate was 53% (258 of 484 eligible). The mean transition age was 19.5 ± 2.9 years, and 34% reported a gap >6 months in establishing adult care. Common reasons for transition included feeling too old (44%), pediatric provider suggestion (41%), and college (33%). Less than half received an adult provider recommendation and <15% reported having a transition preparation visit or receiving written transition materials. The most recent A1C was 8.1 ± 1.3%. Respondents who felt mostly/completely prepared for transition had lower likelihood of a gap >6 months between pediatric and adult care (adjusted odds ratio 0.47 [95% CI 0.25-0.88]). In multivariate analysis, pretransition A1C (β = 0.49, P < 0.0001), current age (β = -0.07, P = 0.03), and education (β = -0.55, P = 0.01) significantly influenced current posttransition A1C. There was no independent association of transition preparation with posttransition A1C (β = -0.17, P = 0.28).Conclusions: Contemporary transition practices may help prevent gaps between pediatric and adult care but do not appear to promote improvements in A1C. More robust preparation strategies and handoffs between pediatric and adult care should be evaluated. [ABSTRACT FROM AUTHOR]

Preterm birth affects over 12% of all infants born in the United States; yet the biology of early delivery remains unclear, including whether epigenetic mechanisms are involved. We examined associations of maternal and umbilical cord blood long interspersed nuclear element-1 (LINE-1) DNA methylation with length of gestation and odds of preterm birth in singleton pregnancies in Project Viva. In white blood cells from maternal blood during first trimester (n = 914) and second trimester (n = 922), and from venous cord blood at delivery (n = 557), we measured LINE-1 by pyrosequencing [expressed as %5 methyl cytosines within the LINE-1 region analyzed (%5mC)]. We ran linear regression models to analyze differences in gestation length, and logistic models for odds of preterm birth (<37 v. ⩾37 weeks’ gestation), across quartiles of LINE-1. Mean (s.d.) LINE-1 levels were 84.3 (0.6), 84.5 (0.4) and 84.6 (0.7) %5mC for first trimester, second trimester and cord blood, respectively. Mean (s.d.) gestational age was 39.5 (1.8) weeks, and 6.5% of infants were born preterm. After adjustment for maternal age, race/ethnicity, body mass index, education, smoking status and fetal sex, women with the highest v. lowest quartile of first trimester LINE-1 had longer gestations [0.45 weeks (95% CI 0.12, 0.78)] and lower odds of preterm birth [OR 0.40 (0.17, 0.94)], whereas associations with cord blood LINE-1 were in the opposite direction (−0.45 weeks, −0.83, −0.08) and [OR 4.55 (1.18, 17.5)]. In conclusion, higher early pregnancy LINE-1 predicts lower risk of preterm birth. In contrast, preterm birth is associated with lower LINE-1 in cord blood. [ABSTRACT FROM PUBLISHER]

Background: Automated electronic medical records may be useful for syndromic surveillance to quickly detect infectious disease outbreaks. Some syndromic surveillance systems include every encounter in the analysis, whereas others exclude individuals' repeat encounters within the same syndrome occurring within a short period of time, with the rationale that these represent follow-up visits rather than new episodes of illness. [ABSTRACT FROM PUBLISHER]

Introduction: Syndromic surveillance's capability to augment existing surveillance for community-acquired gastrointestinal disease is unknown. Objective: The objective of this study was to evaluate the capability of a syndromic surveillance system to detect outbreaks of gastrointestinal disease. Methods: A retrospective analysis was conducted comparing ambulatory care data from a health plan with a set of 110 gastrointestinal-disease outbreaks identified by the Minnesota Department of Health during 2001-2002. Unusual clusters of illness (i.e., signals) in the health-plan data were identified by analyzing daily counts of gastrointestinal illness using an adjusted space-time scan statistic. Concordance was defined as ≤5 km between outbreak and signal and the signal occurring within 1 week of the outbreak. Results: During 104 weeks, the number of signals was roughly what would have been expected by chance, suggesting that the modeling did a good job of estimating the expected counts of illness and that false alarms would not have occurred much more often than the number predicted at the various thresholds. During the same period, the health department identified 110 eligible gastrointestinal outbreaks. Apparent associations of the three statistically most unusual concordant signals with outbreaks of viral or bacterial gastrointestinal illness were ruled out by the health department on the basis of detailed knowledge of the circumstances and low numbers of affected persons seeking medical care. Conclusion: No previously known gastrointestinal outbreaks were identified by this surveillance system. However, relatively few recognized outbreaks resulted in patients seeking medical care, and the sensitivity of this system to detect outbreaks of real significance to public health remains to be determined. Prospective evaluation probably will be required to understand the usefulness of syndromic surveillance systems to enhance existing disease surveillance. [ABSTRACT FROM AUTHOR]

Introduction: Syndromic surveillance systems can be useful in detecting naturally occurring illness. Objectives: Syndromic surveillance performance was assessed to identify an early and severe influenza A outbreak in Denver in 2003. Methods: During October 1, 2003-January 31, 2004, syndromic surveillance signals generated for detecting clusters of influenza-like illness (ILI) were compared with ILI activity identified through a sentinel provider system and with reports of laboratory-confirmed influenza. The syndromic surveillance and sentinel provider systems identified ILI activity based on ambulatory-care visits to Kaiser Permanente Colorado. The syndromic surveillance system counted a visit as ILI if the provider recorded any in a list of 30 respiratory diagnoses plus fever. The sentinel provider system required the provider to select "influenza" or "ILI." Results: Laboratory-confirmed influenza cases, syndromic surveillance ILI episodes, and sentinel provider reports of patient visits for ILI all increased substantially during the week ending November 8, 2003. A greater absolute increase in syndromic surveillance episodes was observed than in sentinel provider reports, suggesting that sentinel clinicians failed to code certain cases of influenza. During the week ending December 6, when reports of laboratory-confirmed cases peaked, the number of sentinel provider reports exceeded the number of syndromic surveillance episodes, possibly because clinicians diagnosed influenza without documenting fever. Conclusion: Syndromic surveillance performed as well as the sentinel provider system, particularly when clinicians were advised to be alert to influenza, suggesting that syndromic surveillance can be useful for detecting clusters of respiratory illness in various settings. [ABSTRACT FROM AUTHOR]

Introduction: Syndromic surveillance's capability to augment existing surveillance for community-acquired gastrointestinal disease is unknown.Objective: The objective of this study was to evaluate the capability of a syndromic surveillance system to detect outbreaks of gastrointestinal disease.Methods: A retrospective analysis was conducted comparing ambulatory care data from a health plan with a set of 110 gastrointestinal-disease outbreaks identified by the Minnesota Department of Health during 2001-2002. Unusual clusters of illness (i.e., signals) in the health plan data were identified by analyzing daily counts of gastrointestinal illness using an adjusted space-time scan statistic. Concordance was defined as <5 km between outbreak and signal and the signal occurring within 1 week of the outbreak.Results: During 104 weeks, the number of signals was roughly what would have been expected by chance, suggesting that the modeling did a good job of estimating the expected counts of illness and that false alarms would not have occurred much more often than the number predicted at the various thresholds. During the same period, the health department identified 110 eligible gastrointestinal outbreaks. Apparent associations of the three statistically most unusual concordant signals with outbreaks of viral or bacterial gastrointestinal illness were ruled out by the health department on the basis of detailed knowledge of the circumstances and low numbers of affected persons seeking medical care.Conclusion: No previously known gastrointestinal outbreaks were identified by this surveillance system. However, relatively few recognized outbreaks resulted in patients seeking medical care, and the sensitivity of this system to detect outbreaks of real significance to public health remains to be determined. Prospective evaluation probably will be required to understand the usefulness of syndromic surveillance systems to enhance existing disease surveillance. [ABSTRACT FROM AUTHOR]

Introduction: Syndromic surveillance systems can be useful in detecting naturally occurring illness.Objectives: Syndromic surveillance performance was assessed to identify an early and severe influenza A outbreak in Denver in 2003.Methods: During October 1, 2003 January 31, 2004, syndromic surveillance signals generated for detecting clusters of influenza-like illness (ILI) were compared with ILI activity identified through a sentinel provider system and with reports of laboratory-confirmed influenza. The syndromic surveillance and sentinel provider systems identified ILI activity based on ambulatory-care visits to Kaiser Permanente Colorado. The syndromic surveillance system counted a visit as ILI if the provider recorded any in a list of 30 respiratory diagnoses plus fever. The sentinel provider system required the provider to select 'influenza' or 'ILI 'Results: Laboratory-confirmed influenza cases, syndromic surveillance ILI episodes, and sentinel provider reports of patient visits for ILI all increased substantially during the week ending November 8, 2003. A greater absolute increase in syndromic surveillance episodes was observed than in sentinel provider reports, suggesting that sentinel clinicians failed to code certain cases of influenza. During the week ending December 6, when reports of laboratory-confirmed cases peaked, the number of sentinel provider reports exceeded the number of syndromic surveillance episodes, possibly because clinicians diagnosed influenza without documenting fever.Conclusion: Syndromic surveillance performed as well as the sentinel provider system, particularly when clinicians were advised to be alert to influenza, suggesting that syndromic surveillance can be useful for detecting clusters of respiratory illness in various settings. [ABSTRACT FROM AUTHOR]

Objectives To determine whether birth weight and adult body size interact to predict coronary heart disease in women, as has been observed for men. To determine whether birth weight and adult body size interact to predict risk of stroke. Design Longitudinal cohort study. Setting and participants 66 111 female nurses followed since 1976 who were born of singleton, term pregnancies and reported their birth weight in 1992. Main outcome measures 1504 events of coronary heart disease (myocardial infarction or sudden cardiac death) and 1164 strokes. Results For each kilogram of higher birth weight, age adjusted hazard ratios from prospective analysis were 0.77 (95% confidence interval 0.69 to 0.87) for coronary heart disease and 0.89 (0.78 to 1.01) for total stroke. In combined prospective and retrospective analysis, hazard ratios were 0.84 (0.76 to 0.93) for total stroke, 0.83 (0.71 to 0.96) for ischaemic stroke, and 0.86 (0.66 to 1.11) for haemorrhagic stroke. Exclusion of macrosomic infants (> 4536 g) yielded stronger estimates. Risk of coronary heart disease was especially high for women who crossed from a low centile of weight at birth to a high centile of body mass index in adulthood. The association of lower birth weight with increased risk of stroke was apparent across categories of body mass index in adults and was not especially strong among heavier women. Conclusions Higher body mass index in adulthood is an especially strong risk factor for coronary heart disease among women who were small at birth. In this large cohort of women, size at birth and adiposity in adulthood interacted to predict events of coronary heart disease but not stroke events. [ABSTRACT FROM AUTHOR]

The National Bioterrorism Syndromic Surveillance Demonstration Program identifies new cases of illness from electronic ambulatory patient records. Its goals are to use data from health plans and practice groups to detect localized outbreaks and to facilitate rapid public health follow-up. Data are extracted nightly on patient encounters occurring during the previous 24 hours. Visits or calls with diagnostic codes corresponding to syndromes of interest are counted; repeat encounters are excluded. Daily counts of syndromes by zip code are sent to a central data repository, where they are statistically analyzed for unusual clustering by using a model-adjusted SaTScan ™ approach. The results and raw data are displayed on a restricted website. Patient-level information stays at the originating health-care organization unless required by public health authorities. If a cluster surpasses a threshold of statistical aberration chosen by the corresponding public health department, an electronic alert can be sent to that department. The health department might then call a clinical responder, who has electronic access to records of cases contributing to clusters. The system is flexible, allowing for changes in participating organizations, syndrome definitions, and alert thresholds. It is transparent to clinicians and has been accepted by the health-care organizations that provide the data. The system's data are usable by local and national health agencies. Its software is compatible with commonly used systems and software and is mostly open-source. Ongoing activities include evaluating the system's ability to detect naturally occurring outbreaks and simulated terrorism events, automating and testing alerts and response capability, and evaluating alternative data sources. [ABSTRACT FROM AUTHOR]

An association between bicycling and erectile dysfunction (ED) has been described previously, but there are limited data examining this association in a random population of men. Such data would incorporate bicyclists with varied types of riding and other factors. Data from the Massachusetts Male Aging Study (MMAS) were utilized to examine the association between bicycling and ED. Logistic regression was used to test for an association, controlling for age, energy expenditure, smoking, depression and chronic illness. Bicycling less than 3 h per week was not associated with ED and may be somewhat protective. Bicycling 3 h or more per week may be associated with ED. Data revealed that there may be a reduced probability of ED in those who ride less than 3 h per week and ED may be more likely in bikers who ride more than 3 h per week. More populationbased research is needed to better define this relationship. [ABSTRACT FROM AUTHOR]

The obesity epidemic causes misery and death. Most epidemiologists accept the hypothesis that characteristics of the early stages of human development have lifelong influences on obesity-related health outcomes. Unfortunately, there is a dearth of data of sufficient scope and individual history to help unravel the associations of prenatal, postnatal, and childhood factors with adult obesity and health outcomes. Here the authors discuss analytic methods, the interpretation of models, and the use to which such rare and valuable data may be put in developing interventions to combat the epidemic. For example, analytic methods such as quantile and multinomial logistic regression can describe the effects on body mass index range rather than just its mean; structural equation models may allow comparison of the contributions of different factors at different periods in the life course. Interpretation of the data and model construction is complex, and it requires careful consideration of the biologic plausibility and statistical interpretation of putative causal factors. The goals of discovering modifiable determinants of obesity during the prenatal, postnatal, and childhood periods must be kept in sight, and analyses should be built to facilitate them. Ultimately, interventions in these factors may help prevent obesity-related adverse health outcomes for future generations. [ABSTRACT FROM AUTHOR]

A biography of botanist George Moore is presented. Born February 23, 1871 in Indianapolis, Indiana, Moore received his doctorate from Harvard University before serving as a plant physiologist at the Missouri Botanical Garden. Later, he worked as a professor of applied botany at the Henry Shaw School of Botany at Washington University, served on the United States Department of Agriculture and headed the Botany Department at Dartmouth College.

Introduction: Staff of emergency and ambulatory care departments encounter data that have unique advantages for timely outbreak detection. Patient age distribution might have an effect on timeliness and accuracy of prediction. Objectives: This report identifies patient subpopulations on the basis of sites of care and patient age distribution, which signal influenza epidemics earliest and most accurately. Methods: Analyses performed included cross-sectional, time series analyses of seven patient populations comprising a health maintenance organization providing ambulatory care, three emergency departments (EDs) at urban tertiary care, and three community-based hospitals in eastern Massachusetts. Except for two EDs (one adult and one pediatric), these hospitals serve all patient age groups. Patients having respiratory infection syndromes and who visited each health-care setting during January 1, 2000-September 30, 2004, were identified and categorized by age (Figure). Cross-spectral analyses and Poisson regression models were used to evaluate timelines and prediction for New England's influenza and pneumonia mortality, as reported to CDC. [ABSTRACT FROM AUTHOR]

Background: Gaps in disease surveillance capacity, particularly for emerging infections and bioterrorist attack, highlight a need for efficient, real time identification of diseases.Methods: We studied automated records from 1996 through 1999 of approximately 250,000 health plan members in greater Boston.Results: We identified 152,435 lower respiratory infection illness visits, comprising 106,670 episodes during 1,143,208 person-years. Three diagnoses, cough (ICD9CM 786.2), pneumonia not otherwise specified (ICD9CM 486) and acute bronchitis (ICD9CM 466.0) accounted for 91% of these visits, with expected age and sex distributions. Variation of weekly occurrences corresponded closely to national pneumonia and influenza mortality data. There was substantial variation in geographic location of the cases.Conclusion: This information complements existing surveillance programs by assessing the large majority of episodes of illness for which no etiologic agents are identified. Additional advantages include: a) sensitivity, uniformity and efficiency, since detection of events does not depend on clinicians' to actively report diagnoses, b) timeliness, the data are available within a day of the clinical event; and c) ease of integration into automated surveillance systems. These features facilitate early detection of conditions of public health importance, including regularly occurring events like seasonal respiratory illness, as well as unusual occurrences, such as a bioterrorist attack that first manifests as respiratory symptoms. These methods should also be applicable to other infectious and non-infectious conditions. Knowledge of disease patterns in real time may also help clinicians to manage patients, and assist health plan administrators in allocating resources efficiently. [ABSTRACT FROM AUTHOR]