Quantifying the variation in neonatal transport referral patterns using network analysis.
Ontology highlight
ABSTRACT: Objective
Regionalized care reduces neonatal morbidity and mortality. This study evaluated the association of patient characteristics with quantitative differences in neonatal transport networks.Study design
We retrospectively analyzed prospectively collected data for infants <28 days of age acutely transported within California from 2008 to 2012. We generated graphs representing bidirectional transfers between hospitals, stratified by patient attribute, and compared standard network analysis metrics.Result
We analyzed 34,708 acute transfers, representing 1594 unique transfer routes between 271 hospitals. Density, centralization, efficiency, and modularity differed significantly among networks drawn based on different infant attributes. Compared to term infants and to those transported for medical reasons, network metrics identify greater degrees of regionalization for preterm and surgical patients (more centralized and less dense, respectively [p < 0.001]).Conclusion
Neonatal interhospital transport networks differ by patient attributes as reflected by differences in network metrics, suggesting that regionalization should be considered in the context of a multidimensional system.
SUBMITTER: Kunz SN
PROVIDER: S-EPMC8613294 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Quantifying global international mobility patterns can improve migration governance. Despite decades of calls by the international community to improve international migration statistics, the availability of timely and disaggregated data about long-term and short-term migration at the global level is still very limited. In this study, we investigate the feasibility of using non-traditional data sources to fill existing gaps in migration statistics. To this end, we use anonymised and publicly available data provided by Facebook's advertising platform. Facebook's advertising platform classifies its users as "lived in country X" if they previously lived in country X, and now live in a different country. Drawing on statistics about Facebook Network users (Facebook, Instagram, Messenger, and the Audience Network) who have lived abroad and applying a sample bias correction method, we estimate the number of Facebook Network (FN) "migrants" in 119 countries of residence and in two time periods by age, gender, and country of previous residence. The correction method estimates the probability of a person being a FN user based on age, sex, and country of current and previous residence. We further estimate the correlation between FN-derived migration estimates and reference official migration statistics. By comparing FN-derived migration estimates in two different time periods, January-February and August-September 2018, we successfully capture the increase in Venezuelan migrants in Colombia and Spain in 2018. FN-derived migration estimates cannot replace official migration statistics, as they are not representative, and the exact methods the FN uses for classifying its users are not known, and might change over time. However, after carefully assessing the validity of the FN-derived estimates by comparing them with data from reliable sources, we conclude that these estimates can be used for trend analysis and early-warning purposes.
| S-EPMC6812739 | biostudies-literature
Project description:BackgroundAdverse drug reactions (ADRs) affect the health of hundreds of thousands of individuals annually in the United States, with associated costs of hundreds of billions of dollars. The monitoring and analysis of the severity of ADRs is limited by the current qualitative and categorical systems of severity classification. Previous efforts have generated quantitative estimates for a subset of ADRs but were limited in scope because of the time and costs associated with the efforts.ObjectiveThe aim of this study is to increase the number of ADRs for which there are quantitative severity estimates while improving the quality of these severity estimates.MethodsWe present a semisupervised approach that estimates ADR severity by using social media word embeddings to construct a lexical network of ADRs and perform label propagation. We used this method to estimate the severity of 28,113 ADRs, representing 12,198 unique ADR concepts from the Medical Dictionary for Regulatory Activities.ResultsOur Severity of Adverse Events Derived from Reddit (SAEDR) scores have good correlations with real-world outcomes. The SAEDR scores had Spearman correlations of 0.595, 0.633, and -0.748 for death, serious outcome, and no outcome, respectively, with ADR case outcomes in the Food and Drug Administration Adverse Event Reporting System. We investigated different methods for defining initial seed term sets and evaluated their impact on the severity estimates. We analyzed severity distributions for ADRs based on their appearance in boxed warning drug label sections, as well as for ADRs with sex-specific associations. We found that ADRs discovered in the postmarketing period had significantly greater severity than those discovered during the clinical trial (P<.001). We created quantitative drug-risk profile (DRIP) scores for 968 drugs that had a Spearman correlation of 0.377 with drugs ranked by the Food and Drug Administration Adverse Event Reporting System cases resulting in death, where the given drug was the primary suspect.ConclusionsOur SAEDR and DRIP scores are well correlated with the real-world outcomes of the entities they represent and have demonstrated utility in pharmacovigilance research. We make the SAEDR scores for 12,198 ADRs and the DRIP scores for 968 drugs publicly available to enable more quantitative analysis of pharmacovigilance data.
| S-EPMC8569532 | biostudies-literature
Project description:BackgroundTo investigate the prevalence and patterns of breastfeeding at discharge and in the first six months of life in a high-risk fetal, neonatal and child referral center.MethodsProspective, longitudinal study that included the following three steps: hospital admission, first visit after hospital discharge and monthly telephone interview until the sixth month of life. The total number of losses was 75 mothers (7.5%). Exposure variables were sorted into four groups: factors related to the newborn, the mother, the health service and breastfeeding. The dependent variable is breastfeeding as per categories established by the WHO. All 1200 children born or transferred to the high-risk fetal, neonatal and child referral center, within a seven-day postpartum period, from March 2017 to April 2018, were considered eligible for the study, and only 1003 were included. The follow-up period ended in October 2018. For this paper, we performed an exploratory analysis at hospital discharge in three stages, as follows: (i) frequencies of baseline characteristics, stratified by risk for newborn; (ii) a multiple correspondence analysis (MCA); and (iii) clusters for variables related to hospital practice and exclusive breastfeeding (EBF).ResultsThe prevalence of EBF at hospital discharge was 65.2% (62.1-68.2) and 20.6% (16.5-25.0) in the six months of life. Out of all at-risk newborns, 45.7% were in EBF at discharge. The total inertia corresponding to the two dimensions in the MCA explained for 75.4% of the total data variability, with the identification of four groups, confirmed by the cluster analysis.DiscussionOur results suggest that robust breastfeeding hospital policies and practices influence the establishment and maintenance of breastfeeding in both healthy and at-risk infants. It is advisable to plan and implement additional strategies to ensure that vulnerable and healthy newborns receive optimal feeding. It is necessary to devote extra effort particularly to at-risk infants who are more vulnerable to negative outcomes.ConclusionAt-risk newborns did not exclusively breastfeed to the same extent as healthy newborns at hospital discharge. A different approach is required for at-risk neonates, who are more physically challenged and more vulnerable to problems associated with initiation and maintenance of breastfeeding.
| S-EPMC7412808 | biostudies-literature
Project description:OBJECTIVES:Decision analysis study that incorporates patient preferences and probability estimates to investigate the impact of women's preferences for referral or an alternative strategy of watchful waiting if faced with symptoms that could be due to breast cancer. SETTING:Community-based study. PARTICIPANTS:Asymptomatic women aged 30-60 years. INTERVENTIONS:Participants were presented with 11 health scenarios that represent the possible consequences of symptomatic breast problems. Participants were asked the risk of death that they were willing to take in order to avoid the health scenario using the standard gamble utility method. This process was repeated for all 11 health scenarios. Formal decision analysis for the preferred individual decision was then estimated for each participant. PRIMARY OUTCOME MEASURE:The preferred diagnostic strategy was either watchful waiting or referral to a breast clinic. Sensitivity analysis was used to examine how each varied according to changes in the probabilities of the health scenarios. RESULTS:A total of 35 participants completed the interviews, with a median age 41 years (IQR 35-47 years). The majority of the study sample was employed (n=32, 91.4%), with a third-level (university) education (n=32, 91.4%) and with knowledge of someone with breast cancer (n=30, 85.7%). When individual preferences were accounted for, 25 (71.4%) patients preferred watchful waiting to referral for triple assessment as their preferred initial diagnostic strategy. Sensitivity analysis shows that referral for triple assessment becomes the dominant strategy at the upper probability estimate (18%) of breast cancer in the community. CONCLUSIONS:Watchful waiting is an acceptable strategy for most women who present to their general practitioner (GP) with breast symptoms. These findings suggest that current referral guidelines should take more explicit account of women's preferences in relation to their GPs initial management strategy.
| S-EPMC5988058 | biostudies-literature
Project description:In many countries, the referral-consultation process faces a number of challenges from inefficiencies and rising demand, resulting in excessive wait times for many specialties. We collected referral data from a sample of family doctors across the province of Ontario, Canada as part of a larger program of research. The purpose of this study is to describe referral patterns from primary care to specialist and allied health services from the primary care perspective.We conducted a prospective study of patient referral data submitted by primary care providers (PCP) from 20 clinics across Ontario between June 2014 and January 2016. Monthly referral volumes expressed as a total number of referrals to all medical and allied health professionals per month. For each referral, we also collected data on the specialty type, reason for referral, and whether the referral was for a procedure.PCPs submitted a median of 26 referrals per month (interquartile range 11.5 to 31.8). Of 9509 referrals eligible for analysis, 97.8% were directed to medical professionals and 2.2% to allied health professionals. 55% of medical referrals were directed to non-surgical specialties and 44.8% to surgical specialties. Medical referrals were for procedures in 30.8% of cases and non-procedural in 40.9%. Gastroenterology received the largest share (11.2%) of medical referrals, of which 62.3% were for colonoscopies. Psychology received the largest share (28.3%) of referrals to allied health professionals.We described patterns of patient referral from primary care to specialist and allied health services for 30 PCPs in 20 clinics across Ontario. Gastroenterology received the largest share of referrals, nearly two-thirds of which were for colonoscopies. Future studies should explore the use of virtual care to help manage non-procedural referrals and examine the impact that procedural referrals have on wait times for gastroenterology.
| S-EPMC5567435 | biostudies-other
Project description:OBJECTIVE:To provide a framework for quantifying the robustness of treatment ranks based on Surface Under the Cumulative RAnking curve (SUCRA) in network meta-analysis (NMA) and investigating potential factors associated with lack of robustness. METHODS:We propose the use of Cohen's kappa to quantify the agreement between SUCRA-based treatment ranks estimated through NMA of a complete data set and a subset of it. We illustrate our approach using five published NMA data sets, where robustness was assessed by removing studies one at a time. RESULTS:Overall, SUCRA-based treatment ranks were robust to individual studies in the five data sets we considered. We observed more incidences of disagreement between ranks in the networks with larger numbers of treatments. Most treatments moved only one or two ranks up or down. The lowest quadratic weighted kappa estimate observed across all networks was in the network with the smallest number of treatments (4), where weighted kappa=40%. In the network with the largest number of treatments (12), the lowest observed quadratic weighted kappa=89%, reflecting a small shift in this network's treatment ranks overall. Preliminary observations suggest that a study's size, the number of studies making a treatment comparison, and the agreement of a study's estimated treatment effect(s) with those estimated by other studies making the same comparison(s) may explain the overall robustness of treatment ranks to studies. CONCLUSIONS:Investigating robustness or sensitivity in an NMA may reveal outlying rank changes that are clinically or policy-relevant. Cohen's kappa is a useful measure that permits investigation into study characteristics that may explain varying sensitivity to individual studies. However, this study presents a framework as a proof of concept and further investigation is required to identify potential factors associated with the robustness of treatment ranks using more extensive empirical evaluations.
| S-EPMC6731799 | biostudies-other
Project description:In recent years, several network models have been introduced to elucidate the relationships between diseases. However, important risk factors that contribute to many human diseases, such as age, gender and prior diagnoses, have not been considered in most networks. Here, we construct a diagnosis progression network of human diseases using large-scale claims data and analyze the associations between diagnoses. Our network is a scale-free network, which means that a small number of diagnoses share a large number of links, while most diagnoses show limited associations. Moreover, we provide strong evidence that gender, age and disease class are major factors in determining the structure of the disease network. Practically, our network represents a methodology not only for identifying new connectivity that is not found in genome-based disease networks but also for estimating directionality, strength, and progression time to transition between diseases considering gender, age and incidence. Thus, our network provides a guide for investigators for future research and contributes to achieving precision medicine.
| S-EPMC5686166 | biostudies-literature
Project description:The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While these procedures have provided important insights about the characteristics of human brain networks, they ultimately rely on unwarranted assumptions such as those of noise-free data or the use of an arbitrary threshold. Therefore, resulting structural connectivity estimates as well as derived graph measures fail to fully take into account the inherent uncertainty in the structural estimate. In this paper, we illustrate an easy way of obtaining posterior distributions over graph metrics using Bayesian inference. It is shown that this posterior distribution can be used to quantify uncertainty about graph-theoretical measures at the single subject level, thereby providing a more nuanced view of the graph-theoretical properties of human brain connectivity. We refer to this model-based approach to connectivity analysis as Bayesian connectomics.
| S-EPMC4189434 | biostudies-literature
Project description:Escherichia coli strains are widely used in academic and applied research as well as in biotechnology for production of various compounds. Despite its status as a model organism, strain-specific differences and their underlying contributing factors are still not well characterized. These differences have a major impact on cell physiology and for the applied purposes of synthetic biology, metabolic engineering, and process design. In this study, strain-specific differences are quantified in seven widely-used applied biotechnology strains of E. coli (BL21, C, Crooks, DH5a, K-12 MG1655, K-12 W3110, and W) using genomics, phenomics, transcriptomics and genome-scale modelling to guide the choice of strain for a given product. Even given the genetic similarity of the strains, mMetabolic physiology and gene expression varied widely with downstream implications for productivity, product yield, and titre. Further, these differences can be linked to differential regulatory structure. Analysing high flux reactions and the expression levels of their encoding genes revealed a quantitative link between these sets and show that often, these sets are correlated with strain-specific caveats. Integrated modelling also revealed that certain strains are better suited to produce a given compound or express a desired construct considering native expression states of pathways that enable high-production phenotypes. The result of this study is a resource comparing strains in an important model species and a general strategy for choosing a host strain or chassis selection for applied biotechnology.
2016-09-22 | GSE78756 | GEO
Project description:ObjectivesTo distinguish between variation in referral threshold and variation in accurate selection of patients for referral in fast-track referrals for possible cancer. To examine factors associated with threshold and accuracy and model the effects of changing thresholds.DesignAnalysis of national data on cancer referrals from general practices in England over a 5-year period. We developed a new method to estimate specificity of referral to complement existing sensitivity. We used bivariate meta-analysis to produce summary measures and described practices in relation to these.Setting5479 general practitioner (GP) practices with data relating to more than 50 cancer cases diagnosed over the 5 years.OutcomesNumber of practices whose 95% confidence regions for sensitivity and specificity indicated that they were outliers in terms of either referral threshold or decision accuracy.Results2019 practices (36.8%) were outliers in relation to referral threshold compared with 1205 practices (22%) in relation to decision accuracy. Practice age profile, cancer incidence and deprivation showed a modest association with decision accuracy but not with thresholds. If all practices shared the referral behaviour of those in the highest quintile of age-standardised referral rate, there would be a 3.3% increase in cancers detected through fast-track pathways at the cost of a 36.9% increase in urgent referrals.ConclusionThis new method permits variation in referral to be described more precisely and quality improvement activities to be targeted. Changing referral thresholds without increasing accuracy will result in modest effects on detection rates and a large increase in demand on diagnostic services.
| S-EPMC5629656 | biostudies-literature