Baseline Trends Research Articles

Syndromic Surveillance of Respiratory Disease in Free-Living Chimpanzees.

Disease surveillance in wildlife is rapidly expanding in scope and methodology, emphasizing the need for formal evaluations of system performance. We examined a syndromic surveillance system for respiratory disease detection in Gombe National Park, Tanzania, from 2004 to 2012, with respect to data quality, disease trends, and respiratory disease detection. Data quality was assessed by examining community coverage, completeness, and consistency. The data were examined for baseline trends; signs of respiratory disease occurred at a mean frequency of less than 1 case per week, with most weeks containing zero observations of abnormalities. Seasonal and secular (i.e., over a period of years) trends in respiratory disease frequency were not identified. These baselines were used to develop algorithms for outbreak detection using both weekly counts and weekly prevalence thresholds and then compared retrospectively on the detection of 13 respiratory disease clusters from 2005 to 2012. Prospective application of outbreak detection algorithms to real-time syndromic data would be useful in triggering a rapid outbreak response, such as targeted diagnostic sampling, enhanced surveillance, or mitigation.

Accounting for baseline trends in intervention studies: Methods, effect sizes, and software

AbstractSingle-case experimental studies are relevant and important to substantiate the effectiveness of behavioral, clinical, and educational interventions. When a baseline trend is present in int...

Multiscale Fluctuation-Based Dispersion Entropy and Its Applications to Neurological Diseases

Fluctuation-based dispersion entropy (FDispEn) is a new approach to estimate the dynamical variability of the fluctuations of signals. It is based on Shannon entropy and fluctuation-based dispersion patterns. To quantify the physiological dynamics over multiple time scales, multiscale FDispEn (MFDE) is developed in this paper. MFDE is robust to the presence of baseline wanders or trends in the data. We evaluate MFDE, compared with popular multiscale sample entropy (MSE), multiscale fuzzy entropy (MFE), and the recently introduced multiscale dispersion entropy (MDE), on selected synthetic data and five neurological diseases' datasets: 1) focal and non-focal electroencephalograms (EEGs); 2) walking stride interval signals for young, elderly, and Parkinson's subjects; 3) stride interval fluctuations for Huntington's disease and amyotrophic lateral sclerosis; 4) EEGs for controls and Alzheimer's disease patients; and 5) eye movement data for Parkinson's disease and ataxia. The MFDE avoids the problem of the undefined MSE values and, compared with the MFE and MSE, leads to more stable entropy values over the scale factors for white and pink noises. Overall, the MFDE is the fastest and most consistent method for the discrimination of different states of neurological data, especially where the mean value of a time series considerably changes along with the signal (e.g., eye movement data). This paper shows that MFDE is a relevant new metric to gain further insights into the dynamics of neurological diseases' recordings. The MATLAB codes for the MFDE and its refined composite form are available in Xplore.

Using Group-based Trajectory Models and Propensity Score Weighting to Detect Heterogeneous Treatment Effects: The Case Study of Generic Hormonal Therapy for Women With Breast Cancer.

We extend an interrupted time series study design to identify heterogenous treatment effects using group-based trajectory models (GBTMs) to identify groups before a new policy and then examine if the effects of the policy has consistent impacts across groups using propensity score weighting to balance individuals within trajectory groups who are and are not exposed to the policy change. We explore this by examining how adherence to endocrine therapy (ET) for women with breast cancer was impacted by reducing copayments for medications by the introduction of generic ETs among women who do not receive a subsidy (the "treatment" group) to those that do receive a subsidy and are not exposed to any changes in copayments (the "control" group). We examined monthly adherence to ET using the proportion of days covered for women diagnosed with breast cancer between 2008 and 2009 using SEER-Medicare data. To account for baseline trends, we characterize adherence for 1 year before generic approval of ET using GBTMs, within each groups we generate inverse probability treatment weights of not receiving a subsidy. We compared adherence after generic entry within each GBTM using a modified Poisson model. GBTMs for adherence in the 1-year pregeneric identified 6 groups. When comparing patients who did and did not receive a subsidy we found no overall effect of generic introduction. However, 1 of the 6 identified adherence groups postgeneric adherence increased [the "consistently low" (risk ratio=1.91; 95% confidence interval=1.34-2.72)]. This study describes a new approach to identify heterogenous effects when using an interrupted time series research design.

Effectiveness of an academic detailing intervention in primary care on the prescribing of non-steroidal anti-inflammatory drugs

PurposeThe objective of this study was to examine the impact of an academic detailing programme in primary care in Norway on the prescribing rate of diclofenac, naproxen and non-steroidal anti-inflammatory drugs (NSAIDs) in total.MethodsAn academic detailing programme was delivered to general practitioners (GPs) in two Norwegian cities. The key message was to avoid diclofenac and COX-2 inhibitors and to use naproxen as the NSAID of choice. We analysed prescription data for 12 months before and after the programme to estimate its impact, using interrupted time series to control for underlying trends, and using the rest of Norway as a comparator. The primary outcome was change in the proportion of the population filling a prescription for diclofenac; secondary outcomes were change in naproxen prescribing and change in total NSAID prescribing.ResultsControlling for baseline trends, and relative to changes in the rest of Norway, there was a statistically significant reduction in the prescribing rate of diclofenac in both cities (− 18% and − 16%, respectively) immediately after the intervention. The impact of the programme on prescribing of diclofenac was maintained by the end of the 12 month follow-up period. An increase in the prescribing of naproxen was observed in both cities. The programme had no impact on the overall rate of prescribing of NSAIDs.ConclusionAcademic detailing was effective in changing the choice of prescribed NSAID amongst Norwegian GPs. Academic detailing is potentially an important method for providing GPs with independent, evidence-based updates on pharmacotherapy to improve prescribing.

Association of the Hospital Readmissions Reduction Program With Mortality Among Medicare Beneficiaries Hospitalized for Heart Failure, Acute Myocardial Infarction, and Pneumonia

The Hospital Readmissions Reduction Program (HRRP) has been associated with a reduction in readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. It is unclear whether the HRRP has been associated with change in patient mortality. To determine whether the HRRP was associated with a change in patient mortality. Retrospective cohort study of hospitalizations for HF, AMI, and pneumonia among Medicare fee-for-service beneficiaries aged at least 65 years across 4 periods from April 1, 2005, to March 31, 2015. Period 1 and period 2 occurred before the HRRP to establish baseline trends (April 2005-September 2007 and October 2007-March 2010). Period 3 and period 4 were after HRRP announcement (April 2010 to September 2012) and HRRP implementation (October 2012 to March 2015). Announcement and implementation of the HRRP. Inverse probability-weighted mortality within 30 days of discharge following hospitalization for HF, AMI, and pneumonia, and stratified by whether there was an associated readmission. An additional end point was mortality within 45 days of initial hospital admission for target conditions. The study cohort included 8.3 million hospitalizations for HF, AMI, and pneumonia, among which 7.9 million (mean age, 79.6 [8.7] years; 53.4% women) were alive at discharge. There were 3.2 million hospitalizations for HF, 1.8 million for AMI, and 3.0 million for pneumonia. There were 270 517 deaths within 30 days of discharge for HF, 128 088 for AMI, and 246 154 for pneumonia. Among patients with HF, 30-day postdischarge mortality increased before the announcement of the HRRP (0.27% increase from period 1 to period 2). Compared with this baseline trend, HRRP announcement (0.49% increase from period 2 to period 3; difference in change, 0.22%, P = .01) and implementation (0.52% increase from period 3 to period 4; difference in change, 0.25%, P = .001) were significantly associated with an increase in postdischarge mortality. Among patients with AMI, HRRP announcement was associated with a decline in postdischarge mortality (0.18% pre-HRRP increase vs 0.08% post-HRRP announcement decrease; difference in change, -0.26%; P = .01) and did not significantly change after HRRP implementation. Among patients with pneumonia, postdischarge mortality was stable before HRRP (0.04% increase from period 1 to period 2), but significantly increased after HRRP announcement (0.26% post-HRRP announcement increase; difference in change, 0.22%, P = .01) and implementation (0.44% post-HPPR implementation increase; difference in change, 0.40%, P < .001). The overall increase in mortality among patients with HF and pneumonia was mainly related to outcomes among patients who were not readmitted but died within 30 days of discharge. For all 3 conditions, HRRP implementation was not significantly associated with an increase in mortality within 45 days of admission, relative to pre-HRRP trends. Among Medicare beneficiaries, the HRRP was significantly associated with an increase in 30-day postdischarge mortality after hospitalization for HF and pneumonia, but not for AMI. Given the study design and the lack of significant association of the HRRP with mortality within 45 days of admission, further research is needed to understand whether the increase in 30-day postdischarge mortality is a result of the policy.

A comprehensive method of single-case data analysis: Interrupted Time-Series Simulation (ITSSIM).

Single-case experimental methods are used across a range of educational and psychological research. Single-case data are analyzed with a variety of methods, but no statistic has demonstrated clear superiority over other methods. The time-series nature of single-case designs requires special consideration for baseline trend and autocorrelation when estimating intervention effect size. However, standard correction methods are limited because they assume precise statistical estimation of trend and autocorrelation. Unlike standard correction methods, Monte Carlo simulation methods can address the poor precision of single-case effect size indices. This paper presents the rationale for a new simulation method, Interrupted Time-Series Simulation (ITSSIM). A small field test was also conducted, and ITSSIM performed similarly to sophisticated multilevel methods for single-case research. ITSSIM is accessible as a free software application that requires no prior knowledge of statistical computing or syntax. ITSSIM may be used to estimate the effect size of a single interrupted time-series (AB design), and multiple ITSSIM effect size estimates may be combined via meta-analysis. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Extrapolating baseline trend in single-case data: Problems and tentative solutions.

Single-case data often contain trends. Accordingly, to account for baseline trend, several data-analytical techniques extrapolate it into the subsequent intervention phase. Such extrapolation led to forecasts that were smaller than the minimal possible value in 40% of the studies published in 2015 that we reviewed. To avoid impossible predicted values, we propose extrapolating a damping trend, when necessary. Furthermore, we propose a criterion for determining whether extrapolation is warranted and, if so, how far out it is justified to extrapolate a baseline trend. This criterion is based on the baseline phase length and the goodness of fit of the trend line to the data. These proposals were implemented in a modified version of an analytical technique called Mean phase difference. We used both real and generated data to illustrate how unjustified extrapolations may lead to inappropriate quantifications of effect, whereas our proposals help avoid these issues. The new techniques are implemented in a user-friendly website via the Shiny application, offering both graphical and numerical information. Finally, we point to an alternative not requiring either trend line fitting or extrapolation.

Interpreting Kendall’s Tau and Tau-U for single-case experimental designs

Tau (τ), a nonparametric rank order correlation statistic, has been applied to single-case experimental designs with promising results. Tau-U, a family of related coefficients, partitions variance associated with changes in trend and level. By examining within-phase trend and across-phase differences separately with Tau-U, single-case investigators may gain useful descriptive and inferential insights about their data. Heuristic data sets were used to explore Tau-U’s conceptual foundation, and 115 published single-case data sets were analyzed to demonstrate that Tau-U coefficients perform predictably when they are well understood. An understanding of Tau-U’s theoretical basis and unique limitations will help investigators select the appropriate statistical method to test their hypotheses and interpret their results appropriately. Limitations of Tau-U include as follows: vague or inconsistent Tau-U terminology in published single-case research; arithmetic problems that lead to unexpected and difficult-to-interpret results, especially when controlling for baseline trend; Tau-U methods are difficult to graph visually, and a comparison with visual raters found that several Tau-U effect size statistics are weakly correlated with visual analysis.

A Status Review of Pinto Abalone (Haliotis kamtschatkana) Along the West Coastof North America: Interpreting Trends, Addressing Uncertainty, and Assessing Risk for a Wide-Ranging Marine Invertebrate

Pinto abalone (Haliotis kamtschatkana), the widest ranging abalone species in North America, occurs from Alaska, United States to Central Baja California, Mexico. The species has been observed in intertidal and subtidal habitats from 0 to 40mdepth. The best available data indicate that pinto abalone abundance has declined in many areas throughout the species' range due to fisheries harvest. Subsistence and personal use fisheries in Alaska and a commercial fishery in Mexico persist. Preliminary data from 2008 to 2016 indicate signs of recovery for some pinto abalone populations along the British Columbia coast due to multiple contributing factors including a reduction in illegal harvest, natural recovery following fishery closure, and low predation pressure. By contrast, pinto abalone populations at the San Juan Islands in Washington are experiencing recruitment failure and continuing to decline, despite closure of the fisheries and no evidence of poaching. Throughout the remainder of the species' range, trends are less clear, due to the lack of regular, long-term monitoring surveys for pinto abalone. The limited data from surveys and/or opportunistic sightings indicate that pinto abalone populations are small, patchily distributed, and/or fluctuate episodically in Alaska, California, and Mexico, with evidence of recent recruitment in a number of locations within these three areas. Baseline abundance and trend data for the species before the advent of commercial fisheries and, in some areas, the local extirpation of sea otters is lacking. Without a clear baseline with which to compare the current abundance levels and trend information, it is difficult to interpret what these levels mean for the status and viability of the species. Threats to pinto abalone were evaluated and characterized using a qualitative rating (i.e., low, moderate, high, very high) based on the threats' scope, severity, and persistence and the sufficiency of the data to support the rating. Several threats that posed a moderate level of risk to pinto abalone were identified including the following: low densities as a result of historical overfishing; the potential threat posed by ocean acidification; and illegal take because of poaching and inadequate law enforcement. The overall risk that pinto abalone face throughout their range was evaluated, and it was determined that they have a low to moderate level of extinction risk now and in the foreseeable future (over both the 30-y and 100-y time horizons). There is a high level of uncertainty regarding demographic factors, in particular regarding whether abundance and productivity levels are sufficient to support the persistence and recovery of the species in the face of continuing and potential future threats. Although recruitment failure may be occurring in some areas (e.g., San Juan Islands Archipelago), in other areas throughout the range recurring and/or recent recruitment events have been observed, despite low densities, and have even resulted in increased densities (across all size classes) at several index sites in British Columbia. Limitations in using demographic data to guide conservation actions and help ensure species persistence could be overcome by conducting consistent monitoring of pinto abalone populations throughout their range.

Impact of a Metoprolol Extended Release Shortage on Post-Myocardial Infarction β-Blocker Utilization, Adherence, and Rehospitalization.

Shortages of chronic medications are an increasingly common problem, yet little is known about their impact on drug utilization and clinical outcomes. We evaluated the population-level impact of metoprolol extended release shortage that occurred in the United States in 2009 to 2010. We conducted a population-based, time series analysis of 38 914 patients (mean age, 60 years; 69% men) discharged after hospitalization for myocardial infarction (MI) between January 2006 and November 2012 in a large commercial insurance database. The shortage period was defined as February 2009 to June 2010. Data before September 2008 was defined as preshortage period and data after June 2010 as postshortage period. Outcomes were proportion of patients who filled any long- or short-acting β-blocker within 30 days of discharge, adherence to β-blockers within the first year of therapy among patients who initiated β-blockers, and rates of 1-year rehospitalization for MI or unstable angina. Post-MI statin utilization and adherence were evaluated as control outcomes. During the preshortage period, 70% of patient filled a β-blocker, mean monthly adherence was 76%, and the average monthly rate of rehospitalization was 6.5 events per 100 person-years, as compared with β-blocker use of 62%, average adherence of 70%, and rehospitalization rate of 5.6 events per 100 person-years during the shortage. After accounting for the baseline (preshortage) trends, the shortage was associated with significant monthly reductions in postdischarge β-blocker use (-0.57% of patients [95% CI, -0.90 to -0.24] per month) and an immediate decrease in adherence (-4.58% days covered [95% CI, -6.12 to -3.04]). No negative impact on rates of rehospitalization, post-MI statin utilization, or statin adherence was observed. β-Blocker utilization began to increase after the resolution of the shortage. The nationwide metoprolol extended release shortage in the United States was associated with fewer patients receiving any long- or short-acting β-blocker post-MI and lower adherence to β-blocker therapy for those who did receive it, but did not appear to appreciably affect clinical outcomes at the population level.

Assessment of Opioid Prescribing Practices Before and After Implementation of a Health System Intervention to Reduce Opioid Overprescribing

Overprescribing of opioids has generated and sustains the opioid overdose epidemic. Health systems have a responsibility to lead the effort to reduce overprescribing. To measure the effects of multilevel interventions on opioid prescribing within a health system. Quality improvement study comparing a 6-month preintervention baseline with a 16-month postintervention period ending in April 2018. Inpatient and outpatient clinical activity within a regional health system including an acute care hospital, same-day surgery, and outpatient clinics. Opioid prescribing activity by hundreds of clinicians involving over a million clinical encounters was measured using a health system's electronic medical record. Multiple parallel interventions in different domains, including prescriber education and accountability, enhanced oversight via measurement of individual prescribers, tools to right-size postoperative discharge prescriptions, reduction of default amounts on standard opioid prescription orders, and professionally written patient and public education about opioid risks and alternatives. Morphine milligram equivalents (MME) per encounter per month, MME per opioid prescription, and rate of opioid prescriptions (opioid prescriptions per encounter per month). More than 44 000 clinical encounters per month were recorded. All baseline trends were not significantly different from 0. Total health system MME per encounter decreased 1.0 MME per encounter per month. At the end of the postintervention observation period, the monthly MME per encounter was 58% lower than the average of the 6-month baseline, the MME per opioid prescription per month was 34% less than the average of the baseline, and the opioid prescription rate was 38% lower than the average of the baseline. Opioid overprescribing was reduced with multifocal interventions targeting patient and public demand, creating prescriber awareness and accountability, and creating tools for clinical leadership accountability. The interventions described are adoptable by most organized health systems. Reducing total opioid supply within communities should be given high priority by those with a mission to protect and improve public health.

Implementation of long-term care and hospital utilization: Results of segmented regression analysis of interrupted time series study

This population-based time series study aimed to examine the effects of the long-term care insurance (LTCI) program on hospital utilization in Korea. Health insurance claim data and LTCI data were combined into a database of 92,596 individuals who were enrolled in Health Insurance at baseline. They were made of people who applied to LTCI program at least once since July 2008, and their hospital utilization records since 2002 were observed. Estimates of length of hospital stay (LOS) after the introduction of LTCI program were calculated using a segmented regression analysis. Although average LOS in hospitals implementation was lower pre-implementation period than post-implementation (16.865 days, SD: 4.864; 26.078 days, SD: 2.215, respectively, p < .0001), a decreasing trend was observed along the post-implementation period. The estimate for baseline trend, which reflect a trend in LOS before LTCI implementation, was 0.219 days (p < .0001). The estimate for Level change after intervention which is only indicated as the change of LOS was 2.821 days at the time of LTCI implementation, and was statistically significant (p < .0001). The estimate for Trend change after intervention, reflecting the trend in LOS after LTCI implementation, was −0.313, indicating a decreasing trend in LOS of −0.094 days (p = 0.0055), compared with the baseline trend. LTCI program was significantly associated with a decreasing trend in LOS. The results suggest that the introduction of LTCI program may have played a role in reducing LOS in older adults.

Telomere attrition is associated with declines in medial temporal lobe volume and white matter microstructure in functionally independent older adults.

Although leukocyte telomere length (TL) shortens over the lifespan and is associated with diseases of aging, little is known about the relationships between TL, memory, and brain structure. Sixty-nine functionally normal older adults (mean age= 71.7) were assessed at 2 time points (mean interval= 2.9years). Linear mixed models assessed relationships between TL and hippocampal volume, fractional anisotropy, and mean diffusivity (MD) of the fornix and verbal and visual episodic memory. Unstandardized coefficients are reported in the following, and p values are not corrected for multiple comparisons. A negative baseline trend was observed between TL and fornix MD (b=-0.01, p= 0.06), but no other cross-sectional associations were significant (ps > 0.16). Greater TL shortening at follow-up was associated with greater hippocampal volume loss (b= 27.09, p < 0.001), even after controlling for global volume loss (b= 10.83, p= 0.002). Greater telomere attrition was also associated with larger increases in fornix MD (b=-0.01, p= 0.012) and decreases in fornix fractional anisotropy (b= 0.004, p= 0.002). TL was not associated with changes in episodic memory (ps > 0.23). These relationships may reflect neurobiological influences that affect both TL and brain structure, as well as the effect of TL on brain aging via mechanisms such as cellular senescence and inflammation.

Effect of Seasonal Variation on Clinical Outcome in Patients with Chronic Conditions: Analysis of the Commonwealth Scientific and Industrial Research Organization (CSIRO) National Telehealth Trial.

BackgroundSeasonal variation has an impact on the hospitalization rate of patients with a range of cardiovascular diseases, including myocardial infarction and angina. This paper presents findings on the influence of seasonal variation on the results of a recently completed national trial of home telemonitoring of patients with chronic conditions, carried out at five locations along the east coast of Australia.ObjectiveThe aim is to evaluate the effect of the seasonal timing of hospital admission and length of stay on clinical outcome of a home telemonitoring trial involving patients (age: mean 72.2, SD 9.4 years) with chronic conditions (chronic obstructive pulmonary disease coronary artery disease, hypertensive diseases, congestive heart failure, diabetes, or asthma) and to explore methods of minimizing the influence of seasonal variations in the analysis of the effect of at-home telemonitoring on the number of hospital admissions and length of stay (LOS).MethodsPatients were selected from a hospital list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. A total of 114 test patients and 173 control patients were available in this trial. However, of the 287 patients, we only considered patients who had one or more admissions in the years from 2010 to 2012. Three different groups were analyzed separately because of substantially different climates: (1) Queensland, (2) Australian Capital Territory and Victoria, and (3) Tasmania. Time series data were analyzed using linear regression for a period of 3 years before the intervention to obtain an average seasonal variation pattern. A novel method that can reduce the impact of seasonal variation on the rate of hospitalization and LOS was used in the analysis of the outcome variables of the at-home telemonitoring trial.ResultsTest patients were monitored for a mean 481 (SD 77) days with 87% (53/61) of patients monitored for more than 12 months. Trends in seasonal variations were obtained from 3 years’ of hospitalization data before intervention for the Queensland, Tasmania, and Australian Capital Territory and Victoria subgroups, respectively. The maximum deviation from baseline trends for LOS was 101.7% (SD 42.2%), 60.6% (SD 36.4%), and 158.3% (SD 68.1%). However, by synchronizing outcomes to the start date of intervention, the impact of seasonal variations was minimized to a maximum of 9.5% (SD 7.7%), thus improving the accuracy of the clinical outcomes reported.ConclusionsSeasonal variations have a significant effect on the rate of hospital admission and LOS in patients with chronic conditions. However, the impact of seasonal variation on clinical outcomes (rate of admissions, number of hospital admissions, and LOS) of at-home telemonitoring can be attenuated by synchronizing the analysis of outcomes to the commencement dates for the telemonitoring of vital signs.Trial RegistrationAustralian New Zealand Clinical Trial Registry ACTRN12613000635763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030&isReview=true (Archived by WebCite at http://www.webcitation.org/ 6xLPv9QDb)

Patterns and comparisons of human-induced changes in river flood impacts in cities

Abstract. In this study, information extracted from the first global urban fluvial flood risk data set (Aqueduct) is investigated and visualized to explore current and projected city-level flood impacts driven by urbanization and climate change. We use a novel adaption of the self-organizing map (SOM) method, an artificial neural network proficient at clustering, pattern extraction, and visualization of large, multi-dimensional data sets. Prevalent patterns of current relationships and anticipated changes over time in the nonlinearly-related environmental and social variables are presented, relating urban river flood impacts to socioeconomic development and changing hydrologic conditions. Comparisons are provided between 98 individual cities. Output visualizations compare baseline and changing trends of city-specific exposures of population and property to river flooding, revealing relationships between the cities based on their relative map placements. Cities experiencing high (or low) baseline flood impacts on population and/or property that are expected to improve (or worsen), as a result of anticipated climate change and development, are identified and compared. This paper condenses and conveys large amounts of information through visual communication to accelerate the understanding of relationships between local urban conditions and global processes.

Point-of-Care Fecal Calprotectin Monitoring in Preterm Infants at Risk for Necrotizing Enterocolitis

To establish baseline trends in fecal calprotectin, a protein excreted into the stool when there is neutrophilic inflammation in the bowel, in infants at risk for necrotizing enterocolitis (NEC). We performed a prospective observational cohort study in infants with a birth weight of <1500 g without existing bowel disease at a level IV neonatal intensive care unit from October 2015 to September 2016. Stools were collected once daily for 30 days or until 32 weeks postmenstrual age and processed using the Fecal Calprotectin High Range Quantitative Quantum Blue assay. In 64 preterm infants, during the first week after birth, 62% of infants had an initial stool sample with high baseline calprotectin levels (≥200 µg/g). In assessment of maternal and neonatal risk factors, maternal etiology for preterm birth (ie, eclamplsia or preeclampsia) was the only significant factor associated with high baseline calprotectin level. Two patients in the cohort developed NEC. Calprotectin levels for the entire cohort fluctuated during the observed period but generally increased in the third and fourth weeks after birth. At-risk infants had highly variable fecal calprotectin levels, with maternal causes for preterm birth associated with higher baseline levels. More longitudinal data in infants with NEC are necessary to determine whether acute rises in fecal calprotectin levels prior to clinical diagnosis can be confirmed as a diagnostic or prognostic biomarker.

The Influence of Measurement Error Associated With Oral Reading Progress Monitoring Measures on the Consistency and Accuracy of Nonparametric Single-Case Design Effect Size Outcomes

Researchers and practitioners frequently use curriculum-based measures of reading (CBM-R) within single-case design (SCD) frameworks to evaluate the effects of reading interventions with individual students. Effect sizes (ESs) developed specifically for SCDs are often used as a supplement to visual analysis to gauge treatment effects. The degree to which measurement error associated with academic measures like CBM-R influences said ESs has not been fully explored. We used simulation methodology to evaluate how common magnitudes of error influenced the consistency and accuracy of outcomes from two nonparametric SCD ESs, percentage of data exceeding baseline trend and TauU. After accounting for other data characteristics, measurement error accounted for a statistically and practically significant amount of variance in the consistency and accuracy of outcomes from both ESs. This article suggests that the psychometric properties of academic measures are important to consider when interpreting ESs from SCDs.

Tau-U: A Quantitative Approach for Analysis of Single-Case Experimental Data in Aphasia.

Tau-U is a quantitative approach for analyzing single-case experimental design (SCED) data. It combines nonoverlap between phases with intervention phase trend and can correct for a baseline trend (Parker, Vannest, & Davis, 2011). We demonstrate the utility of Tau-U by comparing it with the standardized mean difference approach (Busk & Serlin, 1992) that is widely reported within the aphasia SCED literature. Repeated writing measures from 3 participants with chronic aphasia who received computer-based writing treatment are analyzed visually and quantitatively using both Tau-U and the standardized mean difference approach. Visual analysis alone was insufficient for determining an effect between the intervention and writing improvement. The standardized mean difference yielded effect sizes ranging from 4.18 to 26.72 for trained items and 1.25 to 3.20 for untrained items. Tau-U yielded significant (p < .05) effect sizes for 2 of 3 participants for trained probes and 1 of 3 participants for untrained probes. A baseline trend correction was applied to data from 2 of 3 participants. Tau-U has the unique advantage of allowing for the correction of an undesirable baseline trend. Although further study is needed, Tau-U shows promise as a quantitative approach to augment visual analysis of SCED data in aphasia.

Multi-scale Gaussian process experts for dynamic evolution prediction of complex systems

Predictive analytics has become an important topic in expert and intelligent systems, with broad applications across various engineering and business domains, such as the prediction of exchange rate in finance, weather and demand for energy using mixture of experts. However, selection of the number of experts and assignment of the input to individual experts remain elusive, especially for highly nonlinear and nonstationary systems. This paper presents a novel mixture of experts, namely, nonparametric multi-scale Gaussian process (MGP) experts to predict the dynamic evolution of such complex systems. Concretely, intrinsic time-scale decomposition is first used to iteratively decompose the time series generated from such complex systems into a series of proper rotation components and a baseline trend component. Those components delineate the true time-frequency-energy patterns of the complex systems at different granularity. A Gaussian process (GP) expert is then applied on each component to predict the system evolution at each scale. MGP circumvent the tedious selection and assignment problems via the nonparametric ITD. Summation of those individual forecasts represents the overall evolution of the original time series. Case studies using synthetic and real-world data elucidated that the proposed MGP model significantly outperforms conventional autoregressive models, composite GP model, and support vector regression in terms of prediction accuracy, and it is particularly effective for multi-step forecasting.