Tracking changes in opioid prescriptions dispensed following the enactment of a prescription drug monitoring program use mandate

BACKGROUND

Prescription drug monitoring programs (PDMPs) are state-based surveillance tools used to track controlled substances dispensed to patients and identify patients at-risk of misuse. Starting April 2017, Wisconsin required all prescribers access PDMP to review patient information before issuing a controlled substance prescription order for more than a 3-day supply. A primary goal of PDMP use mandates is to reduce avoidable prescribing and mitigate opioid related mortality and morbidity. Current literature has not evaluated the existence of a time point post-policy implementation, at which the trend in opioid dispensing changes, reflecting normalization/maintenance of opioid prescribing.

OBJECTIVE

We sought to evaluate the impact of the PDMP use mandate on trends in opioid prescriptions dispensed and test a hypothesis that a change or inflection in opioid prescriptions dispensed occurred post-mandate implementation.

METHODS

Interrupted Time Series Analysis (ITSA) design was used to examine whether the level (immediate impact) and trend in opioid prescribing changed significantly after the PDMP use mandate was implemented. We used a novel Change Point Analysis (CPA) approach to test the hypothesis i.e., identify if and when a change or inflection in opioid dispensing trend occurred after implementation of the PDMP use mandate.

RESULTS

ITSA model results showed a significant drop in opioid prescriptions dispensed (p < 0.05) immediately after the mandate implementation (i.e., April 2017). Results of the CPA identified a significant inflection in opioid prescriptions dispensed starting January 2019 (21-months post-policy implementation). An ITSA model using the inflection point as an interruption showed that the trend in opioid prescriptions dispensed became flatter after the inflection point, suggesting normalization.

CONCLUSION

Using a novel CPA approach, the findings showed an inflection in the trend in opioid prescriptions dispensed post-PDMP use mandate implementation, implying that most of the avoidable prescribing likely was curtailed. The results suggest that the patient information presumably accessed from the WI PDMP interface was useful in helping prescribers to make an informed clinical decision about opioid prescribing.

Land use change and climate dynamics in the Rift Valley Lake Basin, Ethiopia

Land use and climate dynamics have a pronounced impact on water resources, biodiversity, land degradation, and productivity at all scales. Thus, in this study, we present the spatio-temporal dynamics of land use change and climate aiming to provide a scientific evidence about gains and losses in major land use categories and associated drivers and significancy and homogeneity of climate change. To this end, Landsat images and historical climate data have been used to determine the dynamics. In addition, population census data and land use policy have been considered to assess the potential drivers of land use change. The spatio-temporal land use dynamics have been evaluated using transition matrix and dynamics index. Likewise, shifts in the climate data were analyzed using change point analysis and three homogenous climate zones have been identified using principal component analysis. The results show that, from 1989 to 2019, the areal percentage of agricultural land increased by 27.5%, settlement by 0.8%, and barren land 0.4% while the natural vegetation, wetland, water body, and grass land decreased by 24.5%, 1.6%, 0.5%, and 2.1%, respectively. The land use dynamics have been stronger in the first decade of the study period. An abrupt shift of climate has occurred in the 1980s. In the last four decades, rainfall shows a not significant decreasing trend. However, a significant increasing trend has been observed for temperature. Rapid population growth, agricultural expansion policy, and climate variability have been identified as the underlying drivers of land use dynamics.

Influence path identification of topography, soil, hydrology and landscape on phosphorus buffering capacity in typical agricultural catchments in central subtropical China

The catchment phosphorus buffering capacity (PBF) determines the pressure-state-response relationship between anthropogenic P inputs and aquatic ecosystems at a catchment scale, and is affected by biogeochemical, hydrological, and ecological catchment characteristics. However, the complex relationship between these catchment characteristic factors and their impact pathways on PBF remains ambiguous, leading to large uncertainty in balancing agricultural productivity and water conservation via improving BF through management practices. In this study, the short-term buffering index, calculated from net anthropogenic P input and riverine P exports, was used to quantify the spatiotemporal variations in PBF in source agricultural catchments in the Dongting Lake basin. Partial least squares structural equation modeling was used to investigate the relationship between the PBF and the catchment characteristics. The results indicate that catchment PBF was directly determined by soil properties and hydrological conditions, while landscape patterns significantly mediated the effects of topography on soil and hydrology. Considering the pathway preferences of the model, landscape patterns could be the priority for characterizing and regulating PBF. According to a change-point analysis, the probability of PBF weakening increases dramatically when the proportion of farmland (Farm%) > 24.6%, degree of patch interspersion (Contagion index) < 64.5%, and Perimeter-Area Ratio Distribution (PARA) > 348.7. These findings provide new insights into catchment buffering mechanisms and can be used to promote the simultaneous achievement of agricultural production and environmental conservation goals.

Non-parametric treatment time-lag effect estimation

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.

Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems

Dimethyl sulfide (DMS) serves as an anti-greenhouse gas, plays multiple roles in aquatic ecosystems, and contributes to the global sulfur cycle. The chlorophyll a (CHL, an indicator of phytoplankton biomass)-DMS relationship is critical for estimating DMS emissions from aquatic ecosystems. Importantly, recent research has identified that the CHL-DMS relationship has a breakpoint, where the relationship is positive below a CHL threshold and negative at higher CHL concentrations. Conventionally, mean regression methods are employed to characterize the CHL-DMS relationship. However, these approaches focus on the response of mean conditions and cannot illustrate responses of other parts of the DMS distribution, which could be important in order to obtain a complete view of the CHL-DMS relationship. In this study, for the first time, we proposed a novel Bayesian change point quantile regression (BCPQR) model that integrates and inherits advantages of Bayesian change point models and Bayesian quantile regression models. Our objective was to examine whether or not the BCPQR approach could enhance the understanding of shifting CHL-DMS relationships in aquatic ecosystems. We fitted BCPQR models at five regression quantiles for freshwater lakes and for seas. We found that BCPQR models could provide a relatively complete view on the CHL-DMS relationship. In particular, it quantified the upper boundary of the relationship, representing the limiting effect of CHL on DMS. Based on the results of paired parameter comparisons, we revealed the inequality of regression slopes in BCPQR models for seas, indicating that applying the mean regression method to develop the CHL-DMS relationship in seas might not be appropriate. We also confirmed relationship differences between lakes and seas at multiple regression quantiles. Further, by introducing the concept of DMS emission potential, we found that pH was not likely a key factor leading to the change of the CHL-DMS relationship in lakes. These findings cannot be revealed using piecewise linear regression. We thereby concluded that the BCPQR model does indeed enhance the understanding of shifting CHL-DMS relationships in aquatic ecosystems and is expected to benefit efforts aimed at estimating DMS emissions. Considering that shifting (threshold) relationships are not rare and that the BCPQR model can easily be adapted to different systems, the BCPQR approach is expected to have great potential for generalization in other environmental and ecological studies.

Limiting mobility during COVID-19, when and to what level? An international comparative study using change point analysis

The year 2020 saw a rapid global spread of the highly contagious novel coronavirus COVID-19. To halt the spread of the disease, decision makers and governments across the world have been forced to limit mobility and human interaction, which led to a complete lockdown and the closure of nonessential businesses and public places in many cities and countries. Although effective in curbing the spread of the disease, such measures have had major social and economic impacts, particularly at locations where a complete lockdown was required. In such unprecedented circumstances, decision makers were faced with the dilemma of deciding on how and when to limit mobility to curb the spread of the disease, while being considerate of the significant economic impacts of enforcing such a lockdown. Limited research in this area meant that decision makers were forced to experiment different courses of action without fully understanding the consequences of those actions. To address this critical gap and to provide decision makers with more insights on how to manage mobility during a global pandemic, this paper conducts statistical change point analysis of mobility data from 10 different countries with the aims of establishing links between mobility trends, COVID-19 infections, and COVID-19 mortality rates across different countries where different policies were adopted. Among other findings, the analysis revealed that slow responders experienced significantly higher mortality rates per 100,000 people and were forced to implement stricter lockdown strategies when compared to early responders. The analysis also shows that operating at 40% level of mobility is achievable if appropriate action is taken early enough. The findings of this study are extremely valuable in helping nations better manage a, highly anticipated, second wave of COVID-19 or any other highly contagious global pandemic.

Understanding COVID-19 transmission through Bayesian probabilistic modeling and GIS-based Voronoi approach: a policy perspective

Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Δ (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Δ, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.

Dynamics of sleep: Exploring critical transitions and early warning signals

BACKGROUND AND OBJECTIVES

In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria for sleep stages.

METHODS

EEG (single channel) of two healthy sleeping participants was used to demonstrate this conceptualization. Firstly, distinct EEG epochs were selected, both detected by a MLR classifier and through manual scoring. Secondly, change point analysis was used to identify abrupt changes in the EEG signal. Thirdly, these detected change points were evaluated on whether they were preceded by early warning signals.

RESULTS

Multiple change points were identified in the EEG signal, mostly in interplay with N2. The dynamics before these changes revealed, for a part of the change points, indicators of generic early warning signals, characteristic of complex systems (e.g., ecosystems, climate, epileptic seizures, global finance systems).

CONCLUSIONS

The sketched new framework for studying critical transitions in sleep EEG might benefit the understanding of individual and pathological differences in the dynamics of sleep stage transitions. Formalising sleep as a nonlinear dynamical system can be useful for definitions of sleep quality, i.e. stability and accessibility of an equilibrium state, and disrupted sleep, i.e. constant shifting between instable sleep states.

Relationship between Some Environmental and Climatic Factors on Outbreak of Whiteflies, the Human Annoying Insects

Background

The reports of numerous outbreaks of whiteflies from different parts of the world have increased its medical importance. The aim of this study was to determine relationship between environmental changes and climatic factors with the outbreak of the whitefly population in Tehran, the capital of Iran.

Methods

This study was carried out in urban areas of Tehran, where the increasing population of whiteflies was reported frequently during 2018. In order to entrap the whiteflies, 20 yellow sticky cards smeared with white refined grease were installed on the trunks of the trees at twice per month as trapping time intervals. The captured flies were transferred and conserved in cans containing 70% alcohol and were counted accurately under a stereomicroscope. To determine the relationship between air quality index, precipitation, air temperature and air humidity as environmental and climatic factors with the abundance of whiteflies, change point analysis and Generalized Estimating Equations (GEE) was used.

Results

The most density of white flies per trap was 256.6 and 155.6 in early October and late September respectively. The number moved closer to zero from November to April. The population of whiteflies was inversely correlated with the level of air quality index (p= 0.99) and precipitation (p= 0.95), and it had a direct correlation with the high temperature. Also, the population of whiteflies had a direct correlation with the level of air humidity in the first half of the year.

Conclusion

According to these findings, during spring and summer from early May to early October.

Understanding COVID-19 transmission, health impacts and mitigation: timely social distancing is the key

COVID-19 is a highly infectious disease caused by SARS-CoV-2, first identified in China and spread globally, resulting into pandemic. Transmission of virus takes place either directly through close contact with infected individual (symptomatic/asymptomatic) or indirectly by touching contaminated surfaces. Virus survives on the surfaces from few hours to days. It enters the human body through nose, eyes or mouth. Other sources of contamination are faeces, blood, food, water, semen etc. Parameters such as temperature/relative humidity also play an important role in transmission. As the disease is evolving, so are the number of cases. Proper planning and restriction are helping in influencing the trajectory of the transmission. Various measures are undertaken to prevent infection such as maintaining hygiene, using facemasks, isolation/quarantine, social/physical distancing, in extreme cases lockdown (restricted movement except essential services) in hot spot areas or throughout the country. Countries that introduced various mitigation measures had experienced control in transmission of COVID-19. Python programming is conducted for change point analysis (CPA) using Bayesian probability approach for understanding the impact of restrictions and mitigation methods in terms of either increase or stagnation in number of COVID-19 cases for eight countries. From analysis it is concluded that countries which acted late in bringing in the social distancing measures are suffering in terms of high number of cases with USA, leading among eight countries analysed. The CPA week in comparison with date of lockdown and first reported case strongly correlates (Pearson's r = - 0.86 to - 0.97) to cases, cases per unit area and cases per unit population, indicating earlier the mitigation strategy, lesser the number of cases. The overall paper will help the decision makers in understanding the possible steps for mitigation, more so in developing countries where the fight against COVID-19 seems to have just begun.

Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI

Preterm birth adversely affects postnatal brain development. In order to investigate the critical gestational age at birth (GAB) that alters the developmental trajectory of gray and white matter structures in the brain, we investigated diffusion tensor and quantitative T2 mapping data in 43 term-born and 43 preterm-born infants. A novel multivariate linear model-the change point model, was applied to detect change points in fractional anisotropy, mean diffusivity, and T2 relaxation time. Change points captured the "critical" GAB value associated with a change in the linear relation between GAB and MRI measures. The analysis was performed in 126 regions across the whole brain using an atlas-based image quantification approach to investigate the spatial pattern of the critical GAB. Our results demonstrate that the critical GABs are region- and modality-specific, generally following a central-to-peripheral and bottom-to-top order of structural development. This study may offer unique insights into the postnatal neurological development associated with differential degrees of preterm birth.

Bayesian change-point analysis reveals developmental change in a classic theory of mind task

Although learning and development reflect changes situated in an individual brain, most discussions of behavioral change are based on the evidence of group averages. Our reliance on group-averaged data creates a dilemma. On the one hand, we need to use traditional inferential statistics. On the other hand, group averages are highly ambiguous when we need to understand change in the individual; the average pattern of change may characterize all, some, or none of the individuals in the group. Here we present a new method for statistically characterizing developmental change in each individual child we study. Using false-belief tasks, fifty-two children in two cohorts were repeatedly tested for varying lengths of time between 3 and 5 years of age. Using a novel Bayesian change point analysis, we determined both the presence and-just as importantly-the absence of change in individual longitudinal cumulative records. Whenever the analysis supports a change conclusion, it identifies in that child's record the most likely point at which change occurred. Results show striking variability in patterns of change and stability across individual children. We then group the individuals by their various patterns of change or no change. The resulting patterns provide scarce support for sudden changes in competence and shed new light on the concepts of "passing" and "failing" in developmental studies.

Outbreak definition by change point analysis: a tool for public health decision?

Background

Most studies of epidemic detection focus on their start and rarely on the whole signal or the end of the epidemic. In some cases, it may be necessary to retrospectively identify outbreak signals from surveillance data. Our study aims at evaluating the ability of change point analysis (CPA) methods to locate the whole disease outbreak signal. We will compare our approach with the results coming from experts’ signal inspections, considered as the gold standard method.

Methods

We simulated 840 time series, each of which includes an epidemic-free baseline (7 options) and a type of epidemic (4 options). We tested the ability of 4 CPA methods (Max-likelihood, Kruskall-Wallis, Kernel, Bayesian) methods and expert inspection to identify the simulated outbreaks. We evaluated the performances using metrics including delay, accuracy, bias, sensitivity, specificity and Bayesian probability of correct classification (PCC).

Results

A minimum of 15 h was required for experts for analyzing the 840 curves and a maximum of 25 min for a CPA algorithm. The Kernel algorithm was the most effective overall in terms of accuracy, bias and global decision (PCC = 0.904), compared to PCC of 0.848 for human expert review.

Conclusions

For the aim of retrospectively identifying the start and end of a disease outbreak, in the absence of human resources available to do this work, we recommend using the Kernel change point model. And in case of experts’ availability, we also suggest to supplement the Human expertise with a CPA, especially when the signal noise difference is below 0.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0271-x) contains supplementary material, which is available to authorized users.

A review of the methods for neuronal response latency estimation

Neuronal response latency is usually vaguely defined as the delay between the stimulus onset and the beginning of the response. It contains important information for the understanding of the temporal code. For this reason, the detection of the response latency has been extensively studied in the last twenty years, yielding different estimation methods. They can be divided into two classes, one of them including methods based on detecting an intensity change in the firing rate profile after the stimulus onset and the other containing methods based on detection of spikes evoked by the stimulation using interspike intervals and spike times. The aim of this paper is to present a review of the main techniques proposed in both classes, highlighting their advantages and shortcomings.

What is learned during simultaneous temporal acquisition? An individual-trials analysis

The processes involved in the acquisition of simultaneous temporal processing are currently less understood. For example, it is unclear whether scalar property emerges early during simultaneous temporal acquisition. Using an information-processing model which accounts for the amount of information that each temporal process provides in regard to reward time, we predicted that scalar property would emerge early during the acquisition process, but that subjects should take about 27% longer (more trials) to acquire the long duration than the short duration. To evaluate these predictions, we performed individual-trials analyses to identify changes in timing behavior when rats simultaneously acquire two criterion durations, either 10s and 20s (group 10/20) or 20s and 40s (group 20/40). To analyze the individual trials we used a change-point algorithm to identify changes in rats' wait time. For each individual rat, and for each criterion duration, analyses indicated that simultaneous temporal acquisition is characterized by a sudden change in waiting to a wait-time proportional to the associated criterion. The results failed to indicate group differences in regard to the number of trials it takes for the change in wait-time to occur, but that in both groups, it took longer (more trials) to acquire the long duration than the shorter one, not significantly different from the theoretical prediction. These results are discussed in the framework of an information-processing model informing both associative and temporal learning, thus providing a bridge between the two fields. This article is part of a Special Issue entitled: Associative and Temporal Learning.