Diffusion and social networks: revisiting medical innovation with agents

the classic study on diffusion of Tetracycline by Coleman, Katz and Menzel (1966). Medical Innovation articulates how different patterns of interpersonal communications can influence the diffusion process at different stages of adoption. In their pioneering study, individual network (discussion, friendship or advice) was perceived as a set of disjointed pairs, and the extent of influences were therefore, evaluated for pairs of individuals. Given the existence of overlapping networks and consequent influences on doctors’ adoption decisions, the complexity of actual events was not captured by pair analysis. Subsequent reanalyses (Burt 1987, Strang and Tuma 1993, Valente 1995, Van den Bulte and Lilien 2001) failed to capture the complexity involved in the diffusion process and had a static exposure of the network structure. In this paper, for the first time, we address these limitations by combining Agent-Based Modeling (ABM) and network analysis. Based on the findings of Coleman et. al. (1966) study, we develop a diffusion model, Gammanym. Using SMALLTALK programming language, Gammanym is developed with CORMAS platform under Visual Works environment. The medical community is portrayed in an 8 X 8 spatial grid. The unit cell captures three different locations for professional interactions: practices, hospitals, and conference centers, randomly located over the spatial grid. Two social agents- Doctor and Laboratory are depicted in the model. Doctors are the principal agents in the diffusion process and are initially located at their respective practices. A doctor’s adoption decision is influenced by a random friendship network, and a professional network created through discussions with office colleagues, or hospital visits or conference attendance. A communicating agent, Laboratory, on the other hand, influences doctors’ adoption decisions by sending information through multiple channels: medical representatives or detailman visiting practices, journals sent to doctors’ practices and commercial flyers available during conferences. Doctors’ decisions to adopt a new drug involve interdependent local interactions among different entities in Gammanym. The cumulative adoption curves (Figure 1) are derived for three sets of initial conditions, based on which network topology and evolution of uptake are analyzed. The three scenarios are specified to evaluate the degree of influences by different factors in the diffusion process: baseline scenario with one seed (initial adopter), one detailman and one journal; heavy media scenario with one seed but increasing degrees of external influence, with five detailman and four journals; and integration scenario with one seed, without any external influence from the laboratory

Diffusion and social networks: revisiting medical innovation with agents

the classic study on diffusion of Tetracycline by Coleman, Katz and Menzel (1966). Medical Innovation articulates how different patterns of interpersonal communications can influence the diffusion process at different stages of adoption. In their pioneering study, individual network (discussion, friendship or advice) was perceived as a set of disjointed pairs, and the extent of influences were therefore, evaluated for pairs of individuals. Given the existence of overlapping networks and consequent influences on doctors’ adoption decisions, the complexity of actual events was not captured by pair analysis. Subsequent reanalyses (Burt 1987, Strang and Tuma 1993, Valente 1995, Van den Bulte and Lilien 2001) failed to capture the complexity involved in the diffusion process and had a static exposure of the network structure. In this paper, for the first time, we address these limitations by combining Agent-Based Modeling (ABM) and network analysis. Based on the findings of Coleman et. al. (1966) study, we develop a diffusion model, Gammanym. Using SMALLTALK programming language, Gammanym is developed with CORMAS platform under Visual Works environment. The medical community is portrayed in an 8 X 8 spatial grid. The unit cell captures three different locations for professional interactions: practices, hospitals, and conference centers, randomly located over the spatial grid. Two social agents- Doctor and Laboratory are depicted in the model. Doctors are the principal agents in the diffusion process and are initially located at their respective practices. A doctor’s adoption decision is influenced by a random friendship network, and a professional network created through discussions with office colleagues, or hospital visits or conference attendance. A communicating agent, Laboratory, on the other hand, influences doctors’ adoption decisions by sending information through multiple channels: medical representatives or detailman visiting practices, journals sent to doctors’ practices and commercial flyers available during conferences. Doctors’ decisions to adopt a new drug involve interdependent local interactions among different entities in Gammanym. The cumulative adoption curves (Figure 1) are derived for three sets of initial conditions, based on which network topology and evolution of uptake are analyzed. The three scenarios are specified to evaluate the degree of influences by different factors in the diffusion process: baseline scenario with one seed (initial adopter), one detailman and one journal; heavy media scenario with one seed but increasing degrees of external influence, with five detailman and four journals; and integration scenario with one seed, without any external influence from the laboratory

Characterization of the bias between oxygen saturation measured by pulse oximetry and calculated by an arterial blood gas analyzer in critically ill neonates

Continuous monitoring of oxygenation with pulse oximetry is the standard of care for critically ill neonates. A better understanding of its measurement bias compared to arterial oxygen saturation could be helpful both for the clinician and researcher. Towards that end, we examined the electronic database from a large neonatal ICU. From a 24-month period we identified 23,032 paired SpO2-SaO2 measurements from 1,007 infants who were receiving supplemental oxygen during mechanical ventilation. We found that SpO2 was consistently higher than SaO2. The size of the bias was fairly constant when SpO2 was between 75-93%, above which it dropped steadily. The median size of this bias was 1% SpO2 during hyperoxemia (SpO2 97-100%) with a median variation of 1.3% above and below. During periods of hypoxemia (SpO2 75-85%) and normoxemia (SpO2 89-93%) the bias was approximately 5% SpO2, with a median variation of 5% above and below

Predicting Body Height in a Pediatric Intensive Care Unit Using Ulnar Length

Objective: To determine if ulnar length obtained by the bedside nurse can be used to estimate patient length. To compare our findings to previous predictive equations of height and ulnar length. To evaluate the performance of predictive equations for height and ulnar length on patients with syndromes that affect height.Design: Retrospective observational study of prospectively collected data.Settings: Multidisciplinary Pediatric Intensive Care Unit in a university teaching hospital.Patients: 1,177 patients, ages 1 month to 23 years. Mean age was 79.7 months (1,3 IQR 19.5, 164.5 months) and 55.4% male.Measurements: Ulnar length was obtained using digital calipers by bedside nurses in PICU as well as height and weight. The electronic health care record was used to extract patient information.Main Results: The predictive equation for height for the entire group is: height (cm) = 0.59*ulnar length (mm) + 13.1 (r2 = 0.93). Bland Altman analysis of the derivation formula applied to the testing group did not show any systematic bias.Conclusions: Our study shows that ulnar length measurements can be used to predict height with a simple linear formula in a PICU setting. Not having specific individuals or specific training for ulnar measurement did not seem to alter the accuracy (r2 = 0.93). The robust nature of the measurement and ease of use may make this an unconventional but reasonable alternative to obtaining height when that cannot be measured directly

Behavioural and energetic consequences of competition among three overwintering swan (Cygnus spp.) species

Funder: Peter Scott Trust for Education and Research in ConservationFunder: Peter Smith Charitable Trust for NatureFunder: Olive Herbert Charitable TrustFunder: D’Oyly Carte Charitable TrustFunder: N. Smith Charitable SettlementFunder: Robert Kiln Charitable TrustFunder: The estate of the late Professor Geoffrey Matthews OBEAbstractBackgroundWinter numbers of the northwest European population of Bewick’s Swans (Cygnus columbianus bewickii) declined recently by c. 40%. During the same period, numbers of two sympatric and ecologically-similar congeners, the Mute Swan (Cygnus olor) and Whooper Swan (Cygnus cygnus) showed increases or stability. It has been suggested that these opposing population trends could have a causal relationship, as Mute and Whooper Swans are larger and competitively dominant to Bewick’s Swans in foraging situations. If so, effects of competition of Mute and Whooper Swans on Bewick’s Swans should be detectable as measurable impacts on behaviour and energetics.MethodsHere, we studied the diurnal behaviour and energetics of 1083 focal adults and first-winter juveniles (“cygnets”) of the three swan species on their winter grounds in eastern England. We analysed video recordings to derive time-activity budgets and these, together with estimates of energy gain and expenditure, were analysed to determine whether individual Bewick’s Swans altered the time spent on key behaviours when sharing feeding habitat with other swan species, and any consequences for their energy expenditure and net energy gain.ResultsAll three swan species spent a small proportion of their total time (0.011) on aggressive interactions, and these were predominantly intraspecific (≥ 0.714). Mixed-effects models indicated that sharing feeding habitat with higher densities of Mute and Whooper Swans increased the likelihood of engaging in aggression for cygnet Bewick’s Swans, but not for adults. Higher levels of interspecific competition decreased the time spent by Bewick’s Swan cygnets on foraging, whilst adults showed the opposite pattern. When among low densities of conspecifics (&lt; c. 200 individuals/km2), individual Bewick’s Swans spent more time on vigilance in the presence of higher densities of Mute and Whooper Swans, whilst individuals within higher density Bewick’s Swan flocks showed the opposite pattern. Crucially, we found no evidence that greater numbers of interspecific competitors affected the net energy gain of either adult or cygnet Bewick’s Swans.ConclusionsWe found no evidence that Bewick’s Swan net energy gain was affected by sharing agricultural feeding habitat with larger congeners during winter. This was despite some impacts on the aggression, foraging and vigilance behaviours of Bewick’s Swans, especially among cygnets. It is unlikely therefore that competition between Bewick’s Swans and either Mute or Whooper Swans at arable sites in winter has contributed to the observed decline in Bewick’s Swan numbers. Further research is needed, however, to test for competition in other parts of the flyway, including migratory stopover sites and breeding areas.</jats:sec

Effort and work-of-breathing parameters strongly correlate with increased resistance in an animal model

Background: Effort of Breathing (EOB) calculations may be a reliable alternative to Work of Breathing (WOB) calculations in which Respiratory Inductance Plethysmography (RIP) replaces spirometry. We sought to compare EOB and WOB measurements in a nonhuman primate model of increasing extrathoracic inspiratory resistance simulating upper airway obstruction (UAO).Methods: RIP, spirometry, and esophageal manometry were measured in spontaneously breathing, intubated Rhesus monkeys utilizing 11 calibrated resistors randomly applied for 2-min. EOB was calculated breath-by-breath as Pressure Rate Product (PRP) and Pressure Time Product (PTP). WOB was calculated from the Pressure-Volume curve based on spirometry (WOBSPIR) or RIP flow (WOBRIP).Results: WOB, PRP and PTP showed similar linear increases when exposed to higher levels of resistive loads. When comparing WOBSPIR to WOBRIP, a similar strong correlation was seen for both signals as resistance increased and there were no statistically significant differences.Conclusion: EOB and WOB parameters utilizing esophageal manometry and RIP, independent of spirometry, showed a strong correlation as a function of increasing inspiratory resistance in nonhuman primates. This allows several potential monitoring possibilities for non-invasively ventilated patients or situations where spirometry is not available. Impact: EOB and WOB parameters showed a strong correlation as a function of increasing inspiratory resistance in nonhuman primates.There was a strong correlation between spirometry-based WOB versus RIP-based WOB.To date, it has remained untested as to whether EOB is a reliable alternative for WOB and if RIP can replace spirometry in these measurements.Our results enable additional potential monitoring possibilities for non-invasively ventilated patients or situations where spirometry is not available.Where spirometry is not available, there is no need to apply a facemask post extubation to a spontaneously breathing, non-intubated infant to make objective EOB measurements.</p