Designing a solution to enable agency-academic scientific collaboration for disasters

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecology and Society 22 (2017): 18, doi:10.5751/ES-09246-220218.As large-scale environmental disasters become increasingly frequent and more severe globally, people and organizations that prepare for and respond to these crises need efficient and effective ways to integrate sound science into their decision making. Experience has shown that integrating nongovernmental scientific expertise into disaster decision making can improve the quality of the response, and is most effective if the integration occurs before, during, and after a crisis, not just during a crisis. However, collaboration between academic, government, and industry scientists, decision makers, and responders is frequently difficult because of cultural differences, misaligned incentives, time pressures, and legal constraints. Our study addressed this challenge by using the Deep Change Method, a design methodology developed by Stanford ChangeLabs, which combines human-centered design, systems analysis, and behavioral psychology. We investigated underlying needs and motivations of government agency staff and academic scientists, mapped the root causes underlying the relationship failures between these two communities based on their experiences, and identified leverage points for shifting deeply rooted perceptions that impede collaboration. We found that building trust and creating mutual value between multiple stakeholders before crises occur is likely to increase the effectiveness of problem solving. We propose a solution, the Science Action Network, which is designed to address barriers to scientific collaboration by providing new mechanisms to build and improve trust and communication between government administrators and scientists, industry representatives, and academic scientists. The Science Action Network has the potential to ensure cross-disaster preparedness and science-based decision making through novel partnerships and scientific coordination.The authors thank the David and Lucile Packard Foundation for a grant to undertake this project and enable participation of a wide range of participants and interviewees. We thank the Center for Ocean Solutions and ChangeLabs for their oversight and support

Challenges in the Quest for Keystones

Identifying keystone species is difficult-but essential to understanding bow loss of species will affect ecosystems

Curves of Placental Weights of Live-Born Twins

The purpose of this study is to present curves of estimated placental growth in twins and to evaluate the relative contribution of gestational age, zygosity, chorionicity, fusion of the placentas, sex of the individual and of the twin pair, site of the umbilical cord insertion, birth order, maternal age, and parity. Perinatal data and placental data were obtained from 6315 live-born twin pairs from the East Flanders Prospective Twin Survey. Of 4318 twin pairs, with no missing values, the placental weights of different gestational ages were analyzed using a nonlinear multivariate Gaussian regression. Two groups were distinguished: (1) twins with two separate placentas, and (2) twins with only one placental mass (one placenta in case of monochorionic twins or two fused placentas in case of dichorionic placentas). Overall, placental weight was influenced by gestational age, fusion of the placentas, and parity. In the case of one placental mass, monozygotic dichorionic twins had the lowest weights. If two separate placentas were present, birth order played a role in favor of the first-born twin. For parity and zygosity, the differences were most pronounced between 27 and 29 weeks, whereas the difference for birth order was most pronounced between 33 and 37 weeks. In conclusion, basic physiological characteristics, routinely examined at birth, influence placental weight. Taking these covariates into account allows a better evaluation of the placental weight given a gestational age, as an indicator of growth

Comparisons of mortality and pre-discharge respiratory outcomes in small-for-gestational-age and appropriate-for-gestational-age premature infants

BACKGROUND: There are differences in the literature regarding outcomes of premature small-for-gestational-age (SGA) and appropriate-for gestational-age (AGA) infants, possibly due to failure to take into account gestational age at birth. OBJECTIVE: To compare mortality and respiratory morbidity of SGA and AGA premature newborn infants. DESIGN/METHODS: A retrospective study was done of the 2,487 infants born without congenital anomalies at ≤36 weeks of gestation and admitted to the neonatal intensive care unit (NICU) at John Dempsey Hospital, between Jan. 1992 and Dec. 1999. Recent (1994–96) U.S. birth weight percentiles for gestational age (GA), race and gender were used to classify neonates as SGA (<10th percentile for GA) or AGA (10(th)–90th percentile for GA). Using multivariate logistic regression and survival analyses to control for GA, SGA and AGA infants were compared for mortality and respiratory morbidity. RESULTS: Controlling for GA, premature SGA infants were at a higher risk for mortality (Odds ratio 3.1, P = 0.001) and at lower risk of respiratory distress syndrome (OR = 0.71, p = 0.02) than AGA infants. However multivariate logistic regression modeling found that the odds of having respiratory distress syndrome (RDS) varied between SGA and AGA infants by GA. There was no change in RDS risk in SGA infants at GA ≤ 32 wk (OR = 1.27, 95% CI 0.32 – 1.98) but significantly decreased risk for RDS at GA > 32 wk (OR = 0.41, 95% CI 0.27 – 0.63; p < 0.01). After controlling for GA, SGA infants were observed to be at a significantly higher risk for developing chronic lung disease as compared to AGA infants (OR = 2.2, 95% CI = 1.2 – 3.9, P = 0.01). There was no significant difference between SGA and AGA infants in total days on ventilator. Among infants who survived, mean length of hospital stay was significantly higher in SGA infants born between 26–36 wks GA than AGA infants. CONCLUSIONS: Premature SGA infants have significantly higher mortality, significantly higher risk of developing chronic lung disease and longer hospital stay as compared to premature AGA infants. Even the reduced risk of RDS in infants born at ≥32 wk GA, (conferred possibly by intra-uterine stress leading to accelerated lung maturation) appears to be of transient effect and is counterbalanced by adverse effects of poor intrauterine growth on long term pulmonary outcomes such as chronic lung disease

Reflections: Academia's Emerging Crisis of Relevance and the Consequent Role of the Engaged Scholar

Universities are facing a crisis of relevance. While there are multiple reasons for this to be happening, one that deserves particular attention is the extent to which academic scholars do not see it as their role to engage in public and political discourse. However, increased engagement is unavoidable in an emerging educational context where the caliber of public discourse has become so degraded and social media is changing the nature of science and scientific discourse within society. Further, there is a demographic shift in play, where young scholars are seeking more impact from their work than their more senior colleagues. In this article, I begin the process of articulating what we know and what we don’t know about the evolving role of the engaged scholar by breaking the conversation into two parts. First, why should academic scholars engage in public and political discourse? Second, how can we structure a set of ground rules that could form what might be considered a handbook for public engagement? In the end, this article is about a reexamination of how we practice our craft, to what purpose and to which audiences.http://deepblue.lib.umich.edu/bitstream/2027.42/136168/1/1343_Hoffman.pd

Neonatal anthropometry: a tool to evaluate the nutritional status and predict early and late risks

Neonatal anthropometry is an inexpensive, noninvasive and convenient tool for bedside evaluation, especially in sick and fragile neonates. Anthropometry can be used in neonates as a tool for several purposes: diagnosis of foetal malnutrition and prediction of early postnatal complications; postnatal assessment of growth, body composition and nutritional status; prediction of long-term complications including metabolic syndrome; assessment of dysmorphology; and estimation of body surface. However, in this age group anthropometry has been notorious for its inaccuracy and the main concern is to make validated indices available. Direct measurements, such as body weight, length and body circumferences are the most commonly used measurements for nutritional assessment in clinical practice and in field studies. Body weight is the most reliable anthropometric measurement and therefore is often used alone in the assessment of the nutritional status, despite not reflecting body composition. Derived indices from direct measurements have been proposed to improve the accuracy of anthropometry. Equations based on body weight and length, mid-arm circumference/head circumference ratio, and upper-arm cross-sectional areas are among the most used derived indices to assess nutritional status and body proportionality, even though these indices require further validation for the estimation of body composition in neonates

Making Marine Life Count: A New Baseline for Policy

The Census of Marine Life aids practical work of the Convention on Biological Diversity, discovers and tracks ocean biodiversity, and supports marine environmental planning

Optimal fetal growth for the Caucasian singleton and assessment of appropriateness of fetal growth: an analysis of a total population perinatal database

BACKGROUND: The appropriateness of an individual's intra uterine growth is now considered an important determinant of both short and long term outcomes, yet currently used measures have several shortcomings. This study demonstrates a method of assessing appropriateness of intrauterine growth based on the estimation of each individual's optimal newborn dimensions from routinely available perinatal data. Appropriateness of growth can then be inferred from the ratio of the value of the observed dimension to that of the optimal dimension. METHODS: Fractional polynomial regression models including terms for non-pathological determinants of fetal size (gestational duration, fetal gender and maternal height, age and parity) were used to predict birth weight, birth length and head circumference from a population without any major risk factors for sub-optimal intra-uterine growth. This population was selected from a total population of all singleton, Caucasian births in Western Australia 1998–2002. Births were excluded if the pregnancy was exposed to factors known to influence fetal growth pathologically. The values predicted by these models were treated as the optimal values, given infant gender, gestational age, maternal height, parity, and age. RESULTS: The selected sample (N = 62,746) comprised 60.5% of the total Caucasian singleton birth cohort. Equations are presented that predict optimal birth weight, birth length and head circumference given gestational duration, fetal gender, maternal height, age and parity. The best fitting models explained 40.5% of variance for birth weight, 32.2% for birth length, and 25.2% for head circumference at birth. CONCLUSION: Proportion of optimal birth weight (length or head circumference) provides a method of assessing appropriateness of intrauterine growth that is less dependent on the health of the reference population or the quality of their morphometric data than is percentile position on a birth weight distribution