Day 0 was considered as the day of the diagnosis of infection (infected group) and Day 10 was that of ICU stay (non-infected group). The kinetics of CRP and PCT were assessed from Day –10 to Day 10 and evaluated using a general linear model, univariate, repeated-measures analysis. Results: 118 patients  were eligible for the analysis. The groups did not differ in patient age, gender, CRP and PCT serum levels at ICU admission. However, the infected group encompassed patients with a higher severity  and a higher 28–day mortality (p < 0.001). Before D0, CRP kinetics showed a significant increase in infected patients, whereas in noninfected it remained almost unchanged (p < 0.001), while PCT kinetics did not appear to retain diagnostic value to predict superinfection in COVID-19 patients (p = 0.593). Conclusion: COVID-19 patients who developed ICU-acquired infections exhibited different biomarker kinetics before the diagnosis of those infections. Daily CRP monitoring and the recognition of the CRP kinetics could be useful in the prediction of ICU-acquired infections.The SARS-CoV-2 infection is a cause of hypoxemic acute respiratory failure, leading to frequent intensive care unit (ICU) admission. Due to invasive organ support and immunosuppressive therapies, these patients are prone to nosocomial infections. Our aim was to assess the value of daily measurements of C-reactive protein (CRP) and Procalcitonin (PCT) in the early identification of ICU-acquired infections in COVID-19 patients. Methods: We undertook a prospective observational cohort study (12 months). All adult mechanically ventilated patients admitted for ≥72 h to ICU with COVID-19 pneumonia were divided into an infected group ( The emergence of the SARS-CoV-2 infection presented a novel and significant clinical challenge, imposing a substantial burden on healthcare systems worldwide ,2,3. ThiICU-acquired infections are a common complication among critically ill patients, with estimated mortality rate reaching >40% . HoweverThese daily clinical challenges are further exacerbated in COVID-19 patients, characterized by a significant pro-inflammatory cytokine profile, extensive pulmonary involvement, and exceptionally high mortality rates ,12,13,14The objective of our study is to evaluate the value of daily measurements of CRP and PCT in early identification of ICU-acquired infections in COVID-19 patients. We hypothesize that CRP and PCT kinetics could be useful in predicting ICU-acquired infections in COVID-19 patients prior to infection diagnosis.A prospective observational cohort study was performed at an ICU of Centro Hospitalar Lisboa Ocidental. The study was approved by the National Ethics Committee for Clinical Research (reference REC: 2020_EO_02).All adult mechanically ventilated patients admitted consecutively to the ICU between 1 January 2020 and 31 March 2021 were considered for the study. Patients were included if they had an admission to the ICU with a COVID-19 respiratory infection diagnosis and a length of stay of at least 72 h. COVID-19 respiratory infection was diagnosed using clinical and radiological criteria confirming pulmonary involvement with a SARS-CoV-2-positive RT-PCR test.Within the study population, patients were stratified into two distinct groups: the infected group, comprising all patients with a documented ICU-acquired infection during their stay in the intensive care unit (ICU), and the non-infected group, which included all patients without a diagnosis of an intercurrent ICU-acquired infection during their ICU length of stay.An ICU-acquired infection was defined as the presence of new clinical signs of sepsis, a change in baseline Sequential Organ Failure Assessment (SOFA) score of 2 points or more, and a positive culture that warranted the initiation of empiric antibiotic therapy, as determined through comprehensive clinical evaluation conducted by the attending physicians. These assessment criteria were considered only if they manifested at least 48 h after patient admission to the ICU and if the patient was not receiving antibiotics for at least 5 days before infection diagnosis. The non-infected group comprised all patients without criteria for an ICU-acquired infection.Four CRP kinetic patterns were defined after non-linear mixed-effects modeling to the individual CRP kinetic profiles, using as a cutoff value point for infection a previously identified value of 8.7 mg/dL  28]. Pa. Pa28]. To detect ICU-acquired infection, a positive microbiologic culture was considered on the following biological cultures: endotracheal aspirate, bronchoalveolar lavage or blood. For the first, a cutoff of 105 CFU/mL in culture was considered for positivity, and for the bronchoalveolar lavage, a cutoff of 104 CFU/mL was considered for positivity. For the blood samples, a microorganism documentation was used to document culture positivity. This is consistent with the recommendations provided by the Centers for Disease Control and International ERS/ESICM/ESCMID/ALAT guidelines ,30.Clinical data were prospectively collected from patients’ electronic health records and included demographics, comorbidities, daily laboratory values, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, SOFA scores, therapies administered and data related to microbiological samples, from patients’ electronic health records. Additionally, data concerning ongoing or previous antibacterial therapy at the day of ICU-acquired infection diagnosis were also recorded to ensure patient eligibility. CRP and Procalcitonin daily values were registered in both groups. Data were stored in a pseudo-anonymized database.The day of ICU-acquired infection diagnosis was considered to be Day 0 in the infected group. In the non-infected group, Day 10 after ICU admission was considered as Day 0 after its identification as a median day of ICU-acquired infection diagnoses in the infected group. In the non-infected group, the patients were only selected if they did not receive any antibiotic therapy in the previous 5 days before the selected Day 0. CRP and Procalcitonin daily values were registered from Day –10 to Day 10 of ICU stayed in both groups.Biomarkers serum levels were analyzed comparing the infected and non-infected groups. All identified patterns were also compared, assessing their differences in mortality and clinical course outcomes.t test and the Mann–Whitney U test were used for continuous variables, and the χ2 test was used for categorical variables.All Gaussian distributed variables were expressed as mean (SD), and nonnormally distributed variables as median (interquartile range (IQR)). Categorical variables were expressed as numbers and percentages. To assess differences between the two main groups, the student Receiver operating characteristic curves were plotted for the maximum CRP and maximum PCT registered. The accuracy of these variables was assessed by calculating the area under the curve (AUC).Time-dependent analysis of different variables was performed with general linear model, unmatched, univariate, repeated measures analysis using a split-plot design approach, an approach similar to that of a previous study .Multivariate logistic regression model was used to assess potential variables contributing to predict the occurrence of ICU-acquired infection. The variables considered were age, gender, SOFA score at admission and maximum registered CRP and PCT serum levels, before Day 0 in ICU. They were considered to the model if statistically significant in a bivariate analysis and if they had an attributable odds ratio above 1.2. Multicollinearity was checked by computing the correlation coefficient between selected variables, and a coefficient below 0.4 was considered for exclusion of correlation. Model calibration and discrimination were accessed by the Hosmer–Lemeshow goodness-of fit test and c statistic, respectively. Results were reported as the odds ratio with the 95% confidence interval.Non-linear mixed-effects modeling was performed on the individual CRP kinetic profiles, using as the cutoff value point for infection a previously identified value of 8.7 mg/dL. The models returned four distinct patterns, as previously described and as depicted in Goodness of fit was assessed using the Akaike information criterion with values of 126.5 indicating the model’s suitability.p-values < 0.05 were considered statistically significant.All calculations were performed in the SPSS interface (version 26.0.0.0) and R (version 4.0.3). A total of 136 patients were eligible for the study. Of these patients, 18 patients did not stay at ICU for 72 h or longer and were excluded for statistical analysis. The remaining 118 patients were included, 83 (70.3%) patients in the non-infected group and 35 (29.7%) in the infected group (as depicted in p < 0.001). Similarly, ICU length of stay and in-hospital length of stay were different between groups, albeit no differences in in-hospital mortality rates were identified in infected versus non-infected patients  of CRP and PCT were not different between non-infected and infected groups at ICU admission. However, the infected group presented significantly higher maximum values of CRP and PCT, during ICU stay, before Day 0 . The AUCp = 0.009). On the other hand, PCT serum levels were not different between the two groups (p = 0.857)  B.n = 89, comprehending all patients with ICU-acquired infection (n = 35), Hosmer–Lemeshow chi-squared 2.535 (p = 0.96), AUC = 0.846).For the multivariate logistic regression model, only the SOFA score at admission, maximum CRP and PCT before Day 0 were considered in the model, and no multicollinearity was observed. p < 0.001). However, no discernible differences were noted in terms of clinical severity upon admission, maximum CRP levels recorded during the ICU stay, or ICU and in-hospital mortality rates among the patients.Patients were classified based on previously defined CRP kinetic patterns observed prior to Day 0. The majority of patients demonstrated CRP kinetic profiles corresponding to Pattern B  and Pattern C . Pattern A encompassed 9 patients (7.6%), while pattern D included 24 patients (20.4%). Key demographic and primary clinical characteristics of patients, grouped according to their respective patterns, are presented in Our study presents compelling evidence of distinct CRP and PCT kinetics in severe COVID-19 patients with and without ICU-acquired infections. While no differences were observed between the two groups in the levels of biomarkers at ICU admission, there was a significant difference in their maximum values before the day of infection diagnosis. These findings are in accordance with previously published evidence highlighting the potential prediction value of PCT and CRP in the identification of ICU-acquired infections in COVID-19 patients ,12,17. HContrary to the findings of the studies by Farrel-Cortês et al.  and RichInterestingly, our findings derived from the analysis of CRP kinetic patterns, prior to infection diagnosis, also challenge the prevailing paradigm of commonly accepted patterns as prognostic indicators for ICU-acquired infections. Within the analyzed cohort of critically ill COVID-19 patients, a persistent pro-inflammatory profile characterized by persistently elevated serum CRP levels over time was associated with a risk of developing an infection during the ICU stay, as delineated by Patterns A and B . NotablyTo our knowledge, our results show a novel clinical scenario of biomarker profiling and its predictive value in COVID-19 patients before the diagnosis of ICU-acquired infection. They further display that CRP kinetics retains an acceptable infection predictive value and should be promptly considered in conjunction with relevant clinical assessment and microbiological culture collection in septic COVID-19 patients. Therefore, our results provide evidence that continuous monitoring of CRP may have a useful role in critical care setting in these patients and has the potential to refine and compose predictor models to expedite the identification of these infections and mitigate their impact on patient survivability.Regarding strengths, our study represents a longitudinal analysis of a reasonably large cohort of critical care COVID-19 patients, reflecting real-world clinical decision-making. Importantly, biomarker sampling was performed irrespective of clinical suspicion of ICU-acquired infection, and a high overall rate of microbiological sampling was achieved. Furthermore, none of the eligible patients in this study were treated with Interleukin-6 antagonists which could potentially bias our results.However, we acknowledge some limitations in our study. It is retrospective in nature, and we assumed the median day of infection diagnosis as the corresponding Day 0 in the non-infected group, which may introduce determination bias due to the heterogeneous courses of COVID-19 disease over time. Furthermore, we observed an overall higher incidence of positive bacterial isolation than previously documented estimates , and we Our study presents a novel clinical scenario of biomarker profiling and its value in predicting ICU-acquired infections in COVID-19 patients. The kinetics of CRP may play a useful role in critical care settings and has the potential to refine and compose predictor models to expeditiously predict these infections, ultimately reducing their impact on patient survivability. In addition, the identification of the CRP patterns could increase our ability to identify patients with ICU-acquired infection. CRP Patterns B and C were significantly more present in COVID-19 patients with ICU-acquired infection. These findings contribute to the ongoing efforts to improve the management of ICU-acquired infections in critically ill COVID-19 patients and warrant further research to optimize the use of biomarkers in this context.


Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale ( One of the most well-studied and charismatic toothed whale species is the killer whale (Orcinus orca)13. Killer whales live in stable matrilineal groups, and their highly social nature is reflected in a frequent and diverse vocal repertoire14. They are thus an ideal candidate species for acoustic monitoring. Yet the analysis of individual-specific/group-dependent vocalizations  does not suffice for context-dependent communication, as individual vocalizations could not yet be assigned to specific animal behaviors15. As such, turn-taking events  should be examined as well, in order to gain deeper insights into killer whale communication. This requires robust machine-aided segmentation and subsequent localization techniques.Acoustic monitoring has a wide range of potential applications, including important ecological metrics  and providing detailed inferences on animal behavior17, right whale  signal identification18, fin whale  song note detection20, whistle detection/extraction for toothed whales (Odontoceti)22, sperm whale  echolocation click detection as well as individual, coda type, and dialect classification23, ultrasonic bat search-phase echolocation recognition24, odontocetes echolocation click versus non-click event detection25, and koala (Phascolarctos cinereus) vocal activity identification26. Such machine-based segmentation/detection and classification algorithms act as pre-processing and filtering techniques with respect to a subsequent sound source localization algorithm, which then exclusively determines the signal positions of interest.Nowadays, machine (deep) learning approaches are increasingly applied to segment/detect and classify animal-specific vocal activities on various taxonomic levels within noise-heavy bioacoustic data, such as bird species/call type detection and classification30 are also increasingly performed in bioacoustic to locate and identify certain individuals. Different recording setups and array geometries, in combination with distinct localization techniques, were applied to localize and track animal species, such as Difference-of-Arrival (DOA)-based analysis for bird localization in a native New Zealand forest using a linear microphone array31, or applying the MUSIC algorithm for bird localization utilizing a tripod with mounted microphones32. In addition, Time-Difference-Of-Arrival (TDOA)-based methods were applied in passive acoustic monitoring scenarios for fin whale  tracking in the Gulf of Alaska33, Gulf of California34, and Southern California Offshore Range35. Plenty of TDOA-based animal localizations have also been conducted at the U.S. Navy’s Pacific Missile Range Facility (PMRF) in the northwest of the island of Kauai in the Hawaiian islands using a large-scale passive acoustic monitoring network in order to localize and track minke whales 36, fin whales 37, humpback whales (Megaptera novaeangliae)39, and bryde’s whales 40. Furthermore, research has been conducted regarding real-time detection, localization, and tracking of Antarctic blue whales  utilizing PAMGuard together with Directional Frequency Analysis And Recording (DIFAR) sonobuoys and a final beamforming algorithm41. Additional research in which sperm whales  were localized within the waters of the Gulf of Maine was performed using a towed hydrophone array and the application of time-domain beamforming and moving array triangulation42. Moreover, harbor porpoise (Phocoena phocoena) localization in turbulent tidal waters was conducted, in which the received TDOAs from a large aperture vertical hydrophone array were analyzed by applying a Markov chain Monte Carlo (MCMC) method43. Monitoring and localization of cetaceans around wind turbines using a system of 12 hydrophone units was performed by maximizing the log-likelihood function of the possible locations using a Simplex algorithm44.The same sound source localization algorithms conducted in the domain of human speech8 (photo-ID), alongside the opportunity to document additional behavioral observations and map them to the respective killer whale-specific acoustic events. Those obtained data repositories, including killer whale individual-specific vocalization and associated behavioral patterns, provide new and deeper insights into animal understanding. It requires a fully-automated and hybrid machine-based acoustic identification procedure consisting of signal detection and localization in order to collect such highly valuable individual-based acoustic data archives.Applying sound source localization algorithms, in combination with stationary and/or moving hydrophone arrays, enable the possibility to separate animal- and individual-specific vocalization patterns. The majority of marine localization algorithms deal with passive and stationary recording environments. Although acoustic tracking can be realized, there is no chance of simultaneous behavioral observation and/or visual identification. In the case of an active approach, e.g., using a research vessel, there exists the possibility of acoustic localization and additional visual photo-identificationthe proportion between bioacoustic signals and environmental noise,the mixture of signals from different species,the degree of overlapping vocalization events,the recording setup and environment,the noise conditions, andreal-time versus offline processing.The localization accuracy is strongly influenced by the chosen hydrophone array geometry, which in turn highly depends on the properties of the sound of interest , as well as recording conditions and noise characteristics. Given this multitude of potential problems and challenges with respect to a sound source detection and localization, it is necessary to provide an acoustic localization framework that is not just capable of robustly detecting and localizing killer whale individuals in the field, but also provides a simulation framework in order to verify various recording setups, detection, and localization configurations, all together being as close as possible to real-world scenarios.However, in both cases, a variety of challenges has to be considered within such localization-based data acquisition scenarios. Before a sound source can be localized, the corresponding signal of interest has to be identified. Detection accuracy and algorithmic specifications, required for machine-based segmentation, are very much dependent on the following factors: 45, which is a widely used bioacoustic software. ORCA-SPY integrates and combines a state-of-the-art deep-learning-based sound segmentation module, entitled ANIMAL-SPOT46, together with PAMGuard’s45 internal TDOA-based localization plugin. ANIMAL-SPOT46 is a continuation of the in previous work introduced ORCA-SPOT47. ANIMAL-SPOT can be adapted to arbitrary animal vocalizations and was shown to work for 10 different species ranging from Atlantic cod (gadus morhua) to bats . The ORCA-SPY and ANIMAL-SPOT source code is publicly available here48. ORCA-SPY’s simulation framework can be used to estimate the accuracy of arbitrary recording array constellations with bioacoustic target signals before field deployment. The structure of the paper is summarized as follows: Methodology describes a detailed overview of the corresponding methodologies employed within this work. Most importantly, the ANIMAL-SPOT Network, the PAMGuard software and the subject of the paper, the combined tool chain ORCA-SPY.49 experiment in Northern Germany and the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia.Experiments describes the scenarios performed on the simulated experimental framework, the Lake StechlinThe results of the experiments that are taken as basis for the following.The discussion of the findings of the experiments.Lastly, the conclusion and future work will give an outlook on how to possibly improve the tool chain, as well as on what to possibly come in the future.In order to handle all previously mentioned challenges and address the problem of killer whale localization, this work presents ORCA-SPY, to the best of the authors’ knowledge the first study introducing an automated acoustic sound source simulation and real-time localization framework for arbitrary microphone arrays. The framework, consisting of a hybrid approach between deep-learning-based bioacoustic event segmentation and subsequent localization, was embedded into PAMGuard51, which is accessible here50.It is explicitly mentioned that no animals were directly involved in this study. The data material utilized for simulation purposes is based on data samples originating from the Orchive52 Convolutional Neural Network (CNN) entitled ANIMAL-SPOT46 a continuation of ORCA-SPOT, which was adapted to work with PAMGuard since version 2.02.0153. ANIMAL-SPOT enables binary segmentation of target signals, and was trained and tested in a supervised manner, based on a labeled data corpus including 17,104 orcas and 44,323 noise excerpts. The data corpus incorporates samples of: 51, one of the largest animal-specific bioacoustic data repositories, which was recorded over 25 years (1985–2010) by using a stationary hydrophone network in northern British Columbiathe Orchive46.the 2017/2018 fieldwork expedition (DeepAL17 and DeepAL18) data archive, containing additional orca data material, collected via a 15-m research trimaran utilizing hull-mounted hydrophones and a custom-made towed arrayANIMAL-SPOT performs binary classification on pre-processed, augmented, transformed, and 0/1-dB-normalized power spectrograms, leading to a final network input shape of 25646. To segment unseen recordings with respect to orca sound activities and environmental background noise ANIMAL-SPOT applies a sliding window approach, by utilizing a given window size, step size, and network confidence threshold, leading to a frame-wise classification output. ANIMAL-SPOT only decides whether a frame contains vocal killer whale activity or not, without distinguishing between various call types, as well as the number of vocalization activities, such as single calls, multiple calls, and/or overlapping vocalization parts. After ANIMAL-SPOT additional call-type classification is also possible to better target specific groups but the current approach within the scope of this work had no need for specific animal calltype target groups. ANIMAL-SPOT has a mean detection accuracy of 97.9%46. It is more robust towards noise interferences in comparison to other detection methods such as power threshold detectors. The network, in combination with a mid-range NVIDIA GTX 1050 Graphical Processing Unit (GPU), achieved a factor of 25 times faster than real-time46.For our experiments, we used a ResNet18-basedThis real-time factor is necessary for in field research where one might be bound to weaker mobile hardware, such as laptops. Due to the power restrictions on research vessels and the presence of other computationally heavy applications that must run in parallel, the ANIMAL-SPOT ResNet18-based architecture was designed to not be too computationally heavy and to compute a sliding window in less than approximately half the time frame of a window size (i.e. take less than 1 s for a 2 s window) even without GPU acceleration.45. It is primarily used to study cetaceans in the marine environment, providing users with a suite of no-code visualization, data management, and automated analysis tools45. While most of PAMGuard was designed towards marine environments, the physical model also allows for land-based observation of soniferous species, such as bats or birds. PAMGuard is based around a modular structure, allowing operators to tailor their setup depending on the acoustic workflow required. Modules are independent and thus, as new modules are created, they can be integrated without changing PAMGuard’s core code and take advantage of existing upstream and downstream modules45. Due to reasons of space and complexity, this work will focus hereinafter exclusively on the PAMGuard plugins utilized in the context of this study. However, more detailed information about all available PAMGuard modules, and functionalities, together with a user guide, are available on the official PAMGuard website54. Within the scope of this study, the following PAMGuard modules were an indispensable building block of the entire orca sound localization environment54: Sound acquisition,Sound recorder,the raw deep learning classifier, andthe Bearing calculator, applying the embedded Time-Difference-Of-Arrival (TDOA) algorithm.Passive Acoustic Monitoring Guardianship (PAMGuard) is a set of tools for soundscape analysis, detection, classification, and localization of soniferous speciesThe sound acquisition module acquires raw sound data from different sources, e.g., data acquisition devices, servers, and sound files. The sound acquisition module contains a simulated acquisition (SimSAcq) option which allows users to simulate a source at a known location, source level intensity, and sound type. The sound type can be chosen from 13 built-in sound types . The received sound data will then contain the simulated source sounds, along with the appropriate attenuation due to propagation and appropriate time offset if multiple distributed sensors are being used.The sound recorder captures acoustic data from a raw data source. During real-time operation, it is usually used to save full bandwidth or decimated sound files from the data acquisition system. In this context, the sound recorder was used to save the simulated sound data for additional analysis.55.The FFT spectrogram engine provides basic spectrogram functionality, converting raw sound data into a spectrogram with a user-defined FFT length, hop size, and windowing function. The module also contains several optional noise reduction processes, such as click removal, which are detailed hereThe raw Deep Learning Classifier (rDLC) module is part of the current 2.02 (Beta)-version of PAMGuard and performs deep learning-based target versus noise segmentation on a single channel. It provides corresponding detection events to downstream models in case of valid detection (network confidence larger than a given threshold). The raw Deep Learning Classifier is compatible with generic PyTorch generated models, but was developed with the ANIMAL-SPOT architecture in mind. The module contains the pre-processing algorithms to transform raw audio data into network accepted audio signals, such as segmented 0/1-dB-normalized power spectrograms.56. Differences in arrival times between pairs of hydrophones are used to approximate potential bearings of the sound source being modeled in the forms of hyperbolas57. By cross-correlating the TDOA between all hydrophone pairs, the most-likely bearing target can be calculated from the potential bearings.TDOA-based bearing localization, also known as hyperbolic bearing localization, locates the bearing of an unknown sound source  using multiple distributed time-synchronized receivers  by measuring the TDOA between pairs of hydrophones, comparing one reference hydrophone against all others46 was set as the upstream module and used the standard TDOA grid search algorithm. This performs a spherical grid search around the hydrophone array to locate the most likely 3D bearing  was conducted during a field test of a new towed hydrophone array for future scientific expeditions under laboratory conditions at Lake Stechlin49 in northern Germany. The new towed hydrophone array was deployed during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia (EXP-7) for 3 weeks  through (EXP-5)) were exclusively based on simulated localization data as realistic multichannel underwater audio streams, modelled after a variety of real-world situations from previous fieldwork expeditions through 46. The hydrophones of the short array (S) are approximately 1.5 m apart, whereas the long array (L) shows a distance of roughly 2.8 m. The first hydrophones  are 29.3 m during the 2019 expedition and 28 m during the 2022 expedition behind the end of the trimaran.Within the scope of this study, four different hydrophone array geometries were utilized, depicted in Fig. The Lake Stechlin Array was reconstructed from the GPS positions and in-field measurements of the new hydrophone array assembled during the lake Stechlin field experiment.The streamers were attached onto two 10 m long rods to ensure that the streamer would stay straight and parallel during the recording sessions. By utilizing the 20 m long footbridge in Fig. 44.In (EXP-1) both towed hydrophone arrays were evaluated using PAMGuard’s built-in 2 ms-long chirp-signal, which allows the best possible performance of PAMGuard’s TDOA bearing calculator while assuming ideal experimental conditions(EXP-2) verifies the impact of various orca call type structures concerning localization accuracy. Nine call types see Fig.  were uti(EXP-3) aims to simulate a variety of realistic scenarios for a large combination of varying distances , water depths , and added interfering boat noises, at 46 measurements were always performed during moderate driving speed, since driving too slowly caused a sink and/or drift of the hydrophone streamers. Drifting can occur during a change of course or in a strong current at a low speed. However, sinking depends on the speed of the moving boat as well as the buoyancy of the hydrophone array. Both streamers typically sank by about 3.5(EXP-4) simulates the effects of drifting and sinking streamers. During previous fieldwork expeditionsWhereas (EXP-1) through (EXP-4) primarily focus on the detection and localization of single isolated killer whale vocalization events, (EXP-5) addresses multiple vocalizing individuals with and without an added interfering noise signal originating from a boat with an SNR range of A detailed summary of the simulated scenarios can be found in Supplementary section Simulated Experiments. Overall, five scenarios, in descending order of their complexity, were simulated utilizing the SimSAcq module, named (EXP-1) through (EXP-5). Both the short and long arrays were used in (EXP-1), (EXP-2) and (EXP-3), while (EXP-4) and (EXP-5) only used the long array. A set of scenario examples are visualized in Fig. The lake Stechlin experiment was performed in preparation for the DeepAL 2022 expedition. Both the lake Stechlin experiment and the DeepAL expedition were supposed to assess the ORCA-SPY tool chain in live conditions and compare the results of the simulated environment with real-life environments.49.The Lake Stechlin experiments EXP-6) tested ORCA-SPY on the Lake Stechlin Array, as depicted in Fig.  tested OThe second hydrophone from the top of the left streamer of the Lake Stechlin array in Fig. Shipping traffic did not stop on lake Stechlin. Thus, distant engine noises were unavoidable during the recording sessions. In addition, multiple boats were docked to the footbridge at all times and a loud interference noise was created if a docked ship was hit by a wave or collided with the footbridge. To combat the effect of the above mentioned interfering noises, a band-pass filter between 500 Hz and 10 kHz was applied on all channels. None of the noise interferences which were encountered was similar enough to the orca target signal to create false positive detections.The Streamers were aligned as parallel as possible under the circumstance, but due to constraints during deployment, the height between the streamers were found to be slightly different on-site. These differences, as depicted in Fig. Due to the restricting cable length and power supply, we could not deploy the testing network far from the shore and as such were only testing in shallow waters with a depth of less than meters.To construct said reference coordinate system, the GPS positions of the beginning and end of both streamers were taken. But due to GPS inaccuracies, the alignment of the hydrophone array was ambiguous in comparison to the signal position. As such, we did know the GPS position of the signals and the array, but not the ground truth bearing of the signal in comparison to the fixed array 0The DeepAL 2022 expedition deployed ORCA-SPY on the DeepAL2022 array depicted in Fig. The fog bell buoys are positioned along shipping routes, making it hard to find a window to evaluate without interference.The bell signal is weak in comparison to an animal call, so ANIMAL-SPOT can only falsely detect the signal in close proximity and without noise interference.As such, two recording sessions with fog bell buoy 33 were deemed enough for a prove-of-concept. Figures A ground truth bearing of a target animal is required in order to evaluate ORCA-SPY localization accuracy. The problem is that in our passive acoustic monitoring approach, the encountered animals do not have any tracking device which could be used to get a comparable ground truth. We tried to use visual confirmation to infer the localization accuracy, but our method was too imprecise to be used in numerical calculations. In addition, a similar experiment such as the Lake Stechlin setup was ethically unreasonable, since the noise pollution of an 60 dB underwater loudspeaker could be harmful to the existing animals. During the expedition we encountered fog bells, buoys with a loud audible bell and a number designation which can be used to find one’s position on a map in case of heavy fog. Interestingly, ANIMAL-SPOT was never trained on bell sounds and the fog bell signal with its higher harmonics, as displayed in Fig. An inter-comparison with other research studies is difficult due to: (1) different data repositories, including varying data processing and preparation techniques  and (2) various non-comparable approaches . In addition, ORCA-SPY’s accuracy is dependent on the underlying modules used during evaluation. As such, the TDOA bearing calculation results depend on the ANIMAL-SPOT detector and bearing calculator implementations, which can be replaced in future developments Fig. .Figure 9A comprehensive analysis of the results of every experiment can be found in Supplementary section Simulated Experiments. To summarize, across all simulated experiments utilizing 58,320 embedded vocalizing killer whale events, subjected to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from -14.2dB to 3dB, ORCA-SPY achieved a detection rate of 94.0 The challenges and numerical uncertainties discovered during the live deployment of the Lake Stechlin experiment EXP-6) and the fog-bell passes during the DeepAL 2022 expedition EXP-7) also express themselves in the results. In the case of the lake Stechlin deployment of (EXP-6) 3889 detection events were processed from the 31 recording sessions with 3 hours of data material performed in Fig.  and the  also exp46. Deep learning-based binary-class segmentation reduces the number of false alarms and consequently the number of errors in the localization. Nonetheless, some false alarms may still occur, resulting in high localization errors. In general, ORCA-SPY is a conceptual study to analyze and evaluate the approach of combining deep-learning-based killer whale signal identification (see ANIMAL-SPOT46) with simultaneous TDOA-based sound source localization, all together embedded within PAMGuard’s45 software framework as a fully automated workflow. However, the underlying modules used within this approach are interchangeable and adaptable to other active/passive recording constellations and target signals. In this study, the simulated scenarios were verified and designed with a towed hydrophone array in mind. The scenarios were created in accordance with recording setups of previous fieldwork expedition (DeepAL19)46 and preparations for follow-up expedition (DeepAL22)  in Supplementary section Simulated Experiments indicate that the long array yields better results than the short array. This was expected, due to the larger distances between hydrophones   indicate that minor changes  have only a small influence on the localization performance. Supplementary Table As already discovered through (EXP-2) and (EXP-3), the bearing calculation is dependent on the most prominent (highest RMS intensity) feature within an ANIMAL-SPOT segment. Within the scope of (EXP-2) through (EXP-4) the prominent feature was SNR and call type structure driven. The (EXP-5) scenario was designed with multiple targets in one ANIMAL-SPOT segment in mind. The results of (EXP-5), displayed in Supplementary Figure This is also confirmed with regard to the detection samples of (EXP-6) and (EXP-7). From the 3889 of (EXP-6) and 503 localizations of (EXP-7) 1461 (1148 from (EXP-6) and 313 from (EXP-7)) samples had an accuracy error of less than 1546, was embedded as a PAMGuard module in order to build ORCA-SPY. ORCA-SPY is a sound source localization and simulation framework for real-world killer whale identification, and was evaluated within a large-scale experimental built-up in order to simulate real-world scenarios. The framework was successfully tested and deployed in two expeditions. ORCA-SPY found 54,798 out of 58,320 vocalizing killer whale events concerning (EXP-2) and (EXP-3), across various orca call type patterns within SNR scenarios ranging from In this study, a deep-learning-based sound segmentation module, named ANIMAL-SPOTThe first field deployment (EXP-6) of ORCA-SPY was performed at Lake Stechlin in laboratory conditions and yielded an average error of 29.1959 a deep neural network designed to denoise audio data based on a target signal and (2) ORCA-SCAN60 a deep neural network localizer designed to take both DOA and phase information into account to improve localization. Both modules have, to the best of the author’s knowledge, not yet been deployed in a live experiment or integrated into a framework such as PAMGuard45. Moreover, ANIMAL-SPOT46 has been modified in a way that it is capable of handling any other bioacoustic signals, either within binary target/noise segmentation and/or multi-class species/call type identification scenarios. ANIMAL-SPOT61 was evaluated on 10 different species as well as 1 genus and the raw deep learning module was already integrated and is available in PAMGuard45 and consequently also within the ORCA-SPY framework. Thus, it is possible to use animal-dependent detectors/classifiers fine-tuned to any other vocalizing species, combined with the entire functional repertoire available in PAMGuard45. The raw deep learning module also functions as a basis for further development, in order to seamlessly integrate newly trained ANIMAL-SPOT versions based on advanced network architectures such as ResNeXt62 and HrNet63. Another approach that is currently under development is the integration of other viable detection or localization methods such as Memristor based sound localization64 or FIN-PRINT65, a fully automated framework for the individual recognition of killer whales in pictures. Such an addition could introduce new multi-modal possibilities for improvements in detection, localisation and classification. This is similar to the DMMAN network described by Hu et al66, which would not only improve the performance of ORCA-SPY, but would also help with target differentiation for context dependent analysis with towed and stationary observation. ORCA-SPY generalizes in a way that it allows researchers to simulate and verify various array geometries and setups under assumed realistic real-world noise conditions, which is not just important in the field, but also in preparation for any fieldwork studies. Applying ORCA-SPY in the field, it is possible to assign and map animal-related sound events to the corresponding animals, assuming the single individuals are sufficiently far apart from each other and the SNR values of interferences are below the SNR of the target signal. In this way, individual-specific data repositories can be created to analyze dialects, speaker identification algorithms, and turn-taking events to gain deeper insights into the world of animal communication.Further work has to be done to reduce or to remove strong SNR and Dirac-impulse like interferences or develop a localizer with higher robustness against said interferences. There already exist two projects to solve the annotated problems, (1) ORCA-CLEAN46 has already been successfully implemented and tested in the 2019 fieldwork expedition, clearly demonstrating that network generalization and accuracy, in addition to real-time processing, is not only possible but also extremely promising and helpful. ORCA-SPY and all PAMGuard related code extensions, the ANIMAL-SPOT source code, will be publicly available here48.The entire ORCA-SPY framework will be part of upcoming fieldwork studies. Deep learning based killer whale detection utilizing ANIMAL-SPOTSupplementary Information.


Hyperbolic phonon polaritons (HPhPs) enable strong confinements, low losses, and intrinsic beam steering capabilities determined by the refractive index anisotropy—providing opportunities from hyperlensing to flat optics and other applications. Here, two scanning-probe techniques, photothermal induced resonance (PTIR) and scattering-type scanning near-field optical microscopy (s-SNOM), are used to map infrared  While plasmon polaritons can be engineered across a broad spectral range, the short charge carrier scattering times in metals 13 The discovery of hyperbolic phonon polaritons (HPhPs) in naturally occurring materials, such as quartz,14 hexagonal boron nitride16 (hBN), and MoO3,19 has generated significant interest due to their remarkable optical properties and long lifetimes  and longitudinal optic (LO) phonon pairs, known as the Reststrahlen bands.20Guiding and confining light to sub-wavelength dimensions is a key focus of nanophotonics since enhanced light-matter interactions at the nanoscale enable new spectroscopic,24 among other devices. Isotopic enrichment is a useful strategy to increase phonon lifetimes in materials composed of atoms with multiple naturally occurring isotopes (such as hBN) since, in addition to point defects , the scattering of optic phonons depends strongly on isotopic disorder.25 For example, intrinsic HPhP lifetimes 26 containing isotopically pure boron 24 and 7.5 ps26 compared to naturally abundant boron  samples using scattering-type near-field optical microscopy (s-SNOM). However, the measured values at room temperature 33 or through the use of nanostructures of different sizes by far-field FTIR.34 Similar to s-SNOM, PTIR30 is a scanning-probe-based technique that translates the benefits of infrared (IR) spectroscopy to the nanoscale. Rather than measuring the near-field scattered light from an atomic force microscope (AFM) tip in proximity of the sample, as in s-SNOM, PTIR yields nanoscale IR absorption spectra and maps by transducing the light-induced thermal expansion of the sample with the AFM probe tip.35 Typically, in PTIR experiments, the probe maintains contact with the sample surface (contact-mode).38 In this study, however, we leverage the recently introduced PTIR tapping-mode heterodyne measurement paradigm.40 In practice, a piezoactuator drives the cantilever oscillation in resonance with its second bending mode 41 Importantly, the fast PTIR mechanical signal transduction provides a spatial resolution 39 and up to 42) that is largely unaffected by the (slower) heat diffusion processes. This mechanism results in a much higher spatial resolution than can be obtained with temperature-sensitive AFM probes.44 The PTIR signal is directly proportional to the product of the local sample absorption coefficient47 and the local electric field,59 enabling identification of chemical groups and materials53 and providing nanoscale near-field maps.55 In the context of two-dimensional (2D) materials, PTIR has been used to determine the distribution of chemical groups52 and molecular adsorbates56 in graphene oxide, to identify the composition of polymeric contaminants trapped in 2D heterostructures,50 and to characterize HPhPs in hBN nanostructures58 and MoO319 crystals. Specifically, the detection of HPhPs in hBN frusta via PTIR has provided a more complete set of theoretically predicted modes, revealing (dark) excitations57 undetected with s-SNOM or in far-field measurements. Additional HPhP studies using PTIR could thus be beneficial to understand and engineer hyperbolic media as well as to explore the potential role of hyperbolic polaritons in thermal dissipation at fast timescales.59 Although PTIR is generally a sub-diffraction technique, here, it could also be considered as a near-field technique since the tip plays an active role in generating the polariton contrast mechanism.Until recently, most of the optical dispersion relations in hyperbolic materials have been determined experimentally using real-space s-SNOM imaging coupled with reciprocal-space data analysis60 Typically, light-scattering sites, such as flake edges or the probe tip, are necessary to launch the polaritons  as these material discontinuities bridge the momentum gap between photons in free-space and the strongly confined, high-momentum polaritons.61 While flake edges and the tip scatter light with a continuum of momenta, HPhPs can propagate in the hBN with only a subset of discrete momenta determined by the permittivity tensor. For example, the PTIR images in 62 This apparent discrepancy is instrumental in nature due to the tip-scanning measurement. Fourier analysis of the real-space PTIR maps [see When a hyperbolic material is nanostructured or confined in one or more directions , HPhP propagation is permitted only for specific (quantized) momenta that are dictated by the permittivity tensor of the material and its finite size.maps see  more cle63 and both angles can affect the period of the polariton fringes.65In the PTIR experiments, schematically illustrated in as β see . The ang66 the hBN crystal flakes As reported elsewhere,28 the use of p-polarization  and aligning the flake edge perpendicular and opposed to the light incident direction x-axis in y-axis in 19To experimentally determine the conditions for the highest launching efficiency, we first compared the PTIR maps obtained with different light polarization  PTIR absorption profiles to extract the frequencies associated with each HPhP mode  for the isotopically enriched samples.26 The comparison reveals multiple, thickness-dependent excitation branches, where a given free-space wavelength is compressed to shorter polariton wavelengths in each successive (higher-order) branch.67 The first branch, referred to as the fundamental mode (labeled as “Edge-1”), agrees well with the theoretical predictions for both flakes. For the thicker 26 In addition to HPhPs, fringes with very small wavenumbers  is detected with momenta between the Edge-1 and Edge-2 branches. As mentioned previously, s-SNOM data on 2D hyperbolic flakes have revealed the coexistence of both tip- and edge-launched HPhPs, with the tip-launched modes having approximately double the measured frequency of the edge-launched variety.ck flake . These r70 After calculating the genuine edge-launched wavevectors with where , α 20°,  is the iedge see . Note thtons see . We belitime see . Due to To further corroborate the PTIR analysis, we measured the ever see . The s-Sns e.g.,  to obtains e.g., . The theBy applying :1 ratio . Note thOne peculiar difference between the s-SNOM and PTIR datasets is the unexpected detection of an additional branch near the first edge-launched branch [denoted as “Edge-1′” in pace see . Notably1′ modes , suggestApplying ches see  and S9.26 revealed 17 the greater optical confinement . The long lifetimes measured here support the hypothesis that the previously reported values for these samples26 were constrained by the lateral hBN flake size, which we overcome here using flakes with lateral dimensions that significantly exceed the HPhP propagation lengths and using The resulting 64 in hBN that display low optical compression and remarkably long propagation lengths (In addition to HPhPs, the PTIR images reveal another set of branches in the hBN dispersion (labeled as “0” and “0′” in ty model . We attr lengths . Interescm−1 see . Note thThe analysis of hyperbolic polaritons in large, isotopically enriched hBN flakes (up to Supp1


Huntington’s disease (HD) is caused by the expansion of a polyglutamine (polyQ)-encoding tract in exon 1 of the huntingtin gene to greater than 35 CAG repeats. It typically has a disease course lasting 15–20 years, and there are currently no disease-modifying therapies available. Thus, there is a need for faithful mouse models of HD to use in preclinical studies of disease mechanisms, target validation, and therapeutic compound testing. A large variety of mouse models of HD were generated, none of which fully recapitulate human disease, complicating the selection of appropriate models for preclinical studies. Here, we present the urinary liquid chromatography–high-resolution mass spectrometry analysis employed to identify metabolic alterations in transgenic R6/2 and zQ175DN knock-in mice. In R6/2 mice, the perturbation of the corticosterone metabolism and the accumulation of pyrraline, indicative of the development of insulin resistance and the impairment of pheromone excretion, were observed. Differently from R6/2, zQ175DN mice showed the accumulation of oxidative stress metabolites. Both genotypes showed alterations in the tryptophan metabolism. This approach aims to improve our understanding of the molecular mechanisms involved in HD neuropathology, facilitating the selection of appropriate mouse models for preclinical studies. It also aims to identify potential biomarkers specific to HD. HTT) gene, which consists of an abnormal expansion of a gene’s CAG repeat -L-cysteine (reduced by 983 folds) and N-acetyl-aspartyl-lysine (increased by 44 folds) were found. The latter tentatively assigned based on the exact mass and MS/MS spectrum has already been detected in human urine as the nonacetylated form . Based oA total of 14 differential metabolites  were resA large portion of the altered metabolites, including N-acethylglutathione, thiodiglycolic acid (TDGA), carnosine–propanal–aspartic, S-2-carboxyethyl-L-cysteine/2-((2-Hydroxyethyl)amino)-3-mercapto-4-oxobutanoic acid, were related to oxidative stress. TDGA is a urine metabolite that increases when the redox equilibria of an organism are changed . CarnosiIn this study, we used a noninvasive HRMS-based untargeted metabolomic approach to delineate the metabolic pathways altered in the urine of two widely used HD models, R6/2 and zQ175DN knock-in mice, and the underlying biochemical changes involved in disease progression.Urine samples were collected from the different genotypes and aged-matched wild-type animals. A preliminary normalization using SG was performed to account for the variation in urine concentration due to water consumption. After normalization, the samples were simply diluted and analyzed using two orthogonal chromatographic columns (HILIC and RP). Then, HR-MS data were processed using the dedicated software for data alignment, normalization, and filtering, generating a dataset for each genotype for unsupervised and supervised statistical analysis. The separation between control and HD groups was obtained for both genotypes, and the metabolic features responsible for group separation were identified at level 1 or 2 as defined by the Metabolomics Standards Initiative. Not surprisingly, the altered metabolic pathways were different for the two HD models, consistent with genetic and phenotypic differences. R6/2 is a transgenic model characterized by early disease onset and severe phenotype. Motor and cognitive deficits already start at 6–8 weeks of age, associated with muscular atrophy, decreased body weight, reduced bone mineral density, and abdominal fat accumulation. Insulin resistance and progressive alterations in the hypothalamic–pituitary–adrenal axis are also present. zQ175DN mouse is a less severe model. It is heterozygous for one wild-type HTT allele and one CAG-expanded allele and shows lower variability in gene expression levels. Motor symptoms start from 3 to 4 months of age, and brain atrophy initiates from 8 months. It also shows a significantly slower progression of the disease compared with R6/2 mice. We first compared the urine metabolome of each disease model with its respective age-matched wild-type control and identified the distinctive metabolic features associated with each genotype. We found that in R6/2 mice, the most altered metabolic pathways were corticosterone dysregulation and nonenzymatic glycation, recapitulating the alteration in the hypothalamic–pituitary–adrenal axis and the insulin resistance developed by this model. Also, an alteration in Trp metabolism and the impairment of pheromone secretion was found, underlining the profound biological changes occurring in the mouse brain during disease progression. In zQ175DN knock-in mice, perturbed pathways were mostly different from R6/2 mice. In this model, an increase in oxidative stress metabolites together with an alteration in the tryptophan metabolic pathway, suggested by the increased urine excretion of indole-3-pyruvic acid glucuronide, were the major perturbed pathways.This is the first untargeted analysis of urine metabolomic changes generated by HD progression in mice. In this study, urine metabolomic analysis revealed different pathways altered in two mouse models of HD widely used in preclinical studies. We demonstrated for the first time that urinary metabolite levels can specifically correlate with mouse HD genotypes and with disease progression. In the future, we will expand our study to other HD mouse genotypes to further validate our platform.This approach attempts to improve our understanding of the molecular mechanisms involved in HD neuropathology in R6/2 and zQ175DN mice, facilitating the selection of appropriate models for preclinical studies. It also aims to identify potential biomarkers specific to HD or the underlying neurodegenerative process.Our approach is noninvasive and could be used to profile the urine metabolome from any HD preclinical model and clinical sample. The study design was carefully evaluated in order to reduce the confounding effects induced by sample collection, handling, and storage, contributing to the acquisition of high-quality data. Diet could also be a confounder when it cannot be controlled. Nevertheless, the process of the identification of unknown altered metabolites is the most challenging and time-consuming part of an untargeted metabolomic study, especially when an authentic standard is not available, and the metabolite is not present in the spectral database. It is essential to understand the biological changes contributing to alterations in metabolic features. In this case, if sufficient sample volume is available and enrichment is possible, MS in synergy with NMR analysis could help elucidate metabolite structure.In this study, an untargeted metabolomic approach based on HILIC/RP-HRMS was used to profile urine samples from two HD mouse models (R6/2 and zQ175DN). R6/2, a transgenic model, exhibits a more severe phenotype than the knock-in zQ175DN model. Urine samples collected from the two HD mouse models and their respective age-matched wild-type animals were compared. In both models, the urinary metabolome was found to be altered, reflecting the biochemical events associated with HD pathogenesis. The major pathways altered in R6/2 mice were corticosterone dysregulation, insulin resistance, and tryptophan metabolism. The secretion of a pheromone that promotes aggression amongst males while inducing synchronized estrus in females was also blocked. In zQ175DN mice the major variations were associated with oxidative stress biomarkers and tryptophan metabolism pathways. The differences in urinary metabolome reflecting the genetic and phenotypic differences of the two models can help to select the proper one to be used in preclinical studies. Moreover, MS-based metabolomics can help to identify new potential biomarkers of disease progression or efficacy of therapy once validated.


Were 2021 Expanded Child Tax Credit (ECTC) monthly payments associated with changes in adult overall health or household food security?In this repeated cross-sectional study using nationally representative survey data from 39 479 respondents and a difference-in-differences design, eligibility for ECTC payments was associated with improved overall adult health and household food security.The results of this cross-sectional study suggest that the COVID 19–era policy of ECTC monthly payments may have been associated with improved health and nutrition in adults in eligible households. The 2021 Expanded Child Tax Credit (ECTC) provided families with children monthly payments from July 2021 to December 2021. The association of this policy with adult health is understudied.To examine changes in adult self-reported health and household food security before and during ECTC monthly payments.This repeated cross-sectional study used multivariable regression with a difference-in-differences estimator to assess adult health and food security for 39 479 respondents to the National Health Interview Survey (January 2019 to December 2021) before vs during monthly payments. Analyses were stratified by income to focus on low-income vs middle-income and upper-income households.Eligibility for ECTC monthly payments from July 2021 to December 2021.Overall self-reported adult health and household food security as binary outcomes .In this nationally representative cross-sectional study of 39 479 US adults , respondents were predominantly female (21 511 [52.4%]), employed (33 035 [86.7%]), and married (19 838 [55.7%]). Before disbursement of ECTC monthly payments, 7633 ECTC-eligible adults (60.1%) reported excellent or very good health, and 10 950 (87.8%) reported having food security. Among ECTC-ineligible adults, 10 778 (54.9%) reported excellent or very good health and 17 839 (89.1%) reported food security. Following disbursement of monthly payments, ECTC-eligible adults experienced a 3.0 percentage point (pp) greater adjusted increase  in the probability of reporting excellent or very good health compared with ECTC-ineligible adults. Additionally, ECTC-eligible adults experienced a 1.9 pp greater adjusted increase  in the probability of food security than ECTC-ineligible adults. In income-stratified analyses, the association between ECTC eligibility and overall health was concentrated among middle-income and upper-income households . Conversely, the association between ECTC eligibility and food security was concentrated among low-income adults .The results of this cross-sectional study suggest that monthly ECTC payments were associated with improved adult overall health and food security. Cash transfer programs may be effective tools in improving adult health and household nutrition. This cross-sectional study examines changes in adult self-reported health and household food insecurity before and during Expanded Child Tax Credit monthly payments. These monthly payments were associated with a temporary 40–percentage point reduction in the rate of poverty in households with children.13 Unlike prior iterations of the Child Tax Credit, these payments were fully refundable, meaning that all low-income households were eligible to receive the full value of the credit as a direct deposit or check.14 Since expiring at the end of 2021, the Child Tax Credit reverted to its prior design.16 As one of the largest economic policy interventions in recent history , it is vital to understand how the Expanded Child Tax Credit (ECTC) was associated with changes in health to inform the design of future cash transfer programs.In response to the COVID-19 pandemic, the American Rescue Plan of 2021 made several changes to the Child Tax Credit, including increasing the annual value of the credit to $3600 for each child 5 years or younger and $3000 for each child aged 6 to 17 years.19 To our knowledge, no study has examined the association of the ECTC with adult overall health status.To date, to our knowledge, only 2 studies have assessed the association between the ECTC and adult well-being, with 1 study finding a reduction in depression and anxiety symptoms among low-income adults and the other finding no association between the ECTC and changes in adult life satisfaction or mental health.22 The only study to address food security, which is often measured with the US Department of Agriculture’s (USDA) 10-question survey, recruited participants from a single medical center and may have limited generalizability.23 To our knowledge, no study has assessed the association between the ECTC and food security using a nationally representative sample. Using nationally representative data from the National Health Interview Survey (NHIS), we investigated the association between ECTC eligibility and self-reported overall adult health and secondarily evaluated food security and its potential as a mediator of this association.Four studies have investigated the association between the ECTC and household nutrition. However, 3 of these studies only assessed food insufficiency, which is a narrow, single-question measure of household nutrition.24 This study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies.25 This study was deemed exempt by the University of California, Los Angeles institutional review board, which also waived informed consent due to use of deidentified data.We used NHIS responses from January 1, 2019, to December 31, 2021. The NHIS is an annual nationally representative, cross-sectional household survey of the civilian noninstitutionalized population living in the US. Households are selected using geographically clustered sampling, with annual response rates ranging from 48.9% to 59.1%. One adult is randomly interviewed per household and surveyed on health, health care access, and sociodemographic characteristics.12 We derived income using the household income to poverty ratio, household size, and the US Census Bureau’s 2020 poverty thresholds  vs those surveyed during disbursement of monthly payments from July 2021 to December 2021 .We separated income-eligible adults into 2 exposure groups based on the presence of any child 17 years or younger in their household, which was consistent with prior studies.27 We elected to use the average health of the sample as the cut point for the dichotomization. In the sample, the “average health” state fell between very good and good health. This was consistent with prior studies comparing self-reported overall health with more expansive health utility scales.29We evaluated adult self-reported overall health from the survey question, “Would you say your health in general is excellent, very good, good, fair, or poor?” We collapsed responses into a binary variable of excellent or very good health vs good, fair, or poor health, consistent with prior studies.Food security is defined as having “access to enough food for an active and healthy life,” whereas food insecurity is defined as “limited availability of nutritionally adequate and safe foods, or limited ability to acquire acceptable foods in socially acceptable ways.”31 Per USDA scoring recommendations, households with a raw score of 0 have high food security, a score of 1 to 2 represents marginal food security, 3 to 5 represents low food security, and 6 to 10 represents very low food security. These can be further collapsed into a binary outcome, with the former 2 categories comprising food security and the latter 2 categories comprising food insecurity.30Household food security was determined using a series of 10 questions designed by the USDA. 32We designed a conceptual model before statistical analyses to identify socioeconomic and demographic covariates that may alter overall health or food insecurity. We adjusted for the following characteristics in the regression models: age, sex, race and ethnicity, highest household educational attainment, household employment status (any household member employed), marital status, burden of chronic disease, anxiety or depression diagnosis, health insurance, urbanicity, number of household adults, and receipt of Supplemental Nutrition Assistance Program (SNAP) benefits. Race and ethnicity categories included Hispanic, non-Hispanic American Indian or Alaska Native, non-Hispanic Asian, non-Hispanic Black, non-Hispanic White, and non-Hispanic multiracial or Other. Race and ethnicity were self-reported by participants. The NHIS did not describe the racial and ethnic groups included in the category of “Other.” We included receipt of SNAP as a covariate given changes in the benefit associated with the COVID-19 pandemic and to remain consistent with similar prior studies.t tests and χ2 tests. We did not adjust the significance level for multiple comparisons for the 2 prespecified outcomes given that the secondary outcome of food security was a confirmatory analysis included to evaluate its potential as a mediator of changes in adult health. The results should be interpreted accordingly.34Analyses were performed using Stata, version 17.0 (StataCorp), accounting for the complex sampling design and weighting scheme of the NHIS. All tests were 2-sided, with an α level of .05. We described sociodemographic characteristics by ECTC eligibility and by pre-ECTC vs during-ECTC status. We conducted bivariate analyses between groups using 2-sample 34 As we hypothesized that food security may be a potential mechanism by which the ECTC was associated with overall health, we evaluated food security as a mediator via the Sobel method  to 1642 (63.1%). This was a 3.7 percentage point (pp) greater unadjusted increase than that experienced by ECTC-ineligible adults . Among lIn adjusted difference-in-differences analyses, ECTC eligibility was associated with an increase in excellent and very good health , failingIn unadjusted analyses, following disbursement of monthly payments, ECTC-eligible households reported an increase in food security from 10 950 (87.8%) to 2421 (91.4%), a 1.7-pp greater unadjusted increase than ECTC-ineligible households . In incoIn adjusted difference-in-differences analyses, ECTC eligibility was associated with an increase in overall food security , this study measured food security, a more descriptive 4-level construct validated by the USDA.51 The COVID-19 pandemic exacerbated many socioeconomic and racial health disparities, disproportionately affecting low-income Americans compared with other income groups.54 Given the unprecedented and disproportionate level of health-related stress experienced by low-income adults during the study period, the ECTC may have been insufficient to change their overall health trajectory or buffer all the increased health threats they faced. Second, it is estimated that fewer than half of eligible low-income families received monthly payments.56 Given that this study assessed ECTC eligibility and not receipt, this study may have been underpowered to show changes in health for low-income adults who actually received the payments. Third, our model used difference-in-differences analyses; therefore, improvements in health experienced by the control group, low-income ECTC-ineligible adults, diminished the magnitude of the positive association between ECTC eligibility and health. We hypothesize that the expansion of COVID19–related social support programs, including unemployment, SNAP benefits, economic impact payments, and health insurance expansions, may have had a larger association with outcomes for ECTC-ineligible households given that they are inherently comprised of only working-age adults who would have each been eligible to benefit from these programs.58 While we attempted to control for these additional benefits, particularly receipt of SNAP and changes in health insurance, these policies may have disproportionately affected low-income ineligible adults, thus attenuating our ability to measure the ECTC’s association with health in low-income households.The association of ECTC eligibility with health was most pronounced in middle-income and upper-income households and not low-income households. We hypothesize several reasons for this surprising finding. First, it is possible that the additional income provided by the ECTC may have been insufficient to reverse the negative health effects of long-standing economic hardship.59 The ECTC temporarily increased the generosity of the benefit to more closely reflect that of other high-income countries and represents the US government’s largest recent attempt at an unconditional cash transfer program for families with children.6 This study provides evidence that this program may have improved adult health and household food security. As future antipoverty measures are being designed, unconditional cash transfer programs like the ECTC should be considered as a potentially effective method to improve population health. Furthermore, in light of research indicating that many low-income families failed to receive monthly payments, future investigation should focus on the implementation of cash transfer programs to ensure the equitable disbursement of future payments.56 Physicians and allied health professionals can serve a critical role in improving the likelihood of patients receiving these benefits.60While other countries have adopted generous permanent monthly cash transfer programs for families, such as the Canada Child Benefit, the US has favored cash transfer programs tied to employment and tax refunds, such as the Child Tax Credit and Earned Income Tax Credit.58 We attempted to address this by using a difference-in-differences model that used childless households as a control to account for temporal changes associated with the pandemic. Additionally, given that the NHIS did not specifically report receipt of the ECTC, we derived eligibility from income and family composition, which likely did not perfectly reflect receipt of the ECTC. As such, the analysis represents an intent-to-treat approach, which might underestimate the magnitude of associations for those who received the ECTC. In addition, the NHIS does not uniformly provide the relationship of the sample adult to the household child/children. Because of this, the findings of this study cannot speak to the ECTC’s effect on a specific individual within a household unit  but instead evaluates the effect of ECTC eligibility on any adult living in a household with a child. Lastly, the dichotomization of ordinal variables can be followed by a loss of information.61 Fortunately, in sensitivity analyses, the models using binary outcomes demonstrated consistency with those using the ordinal specification.This study was conducted during the COVID-19 pandemic; thus, it may have been subject to unmeasured confounding from associated job losses, changes in health, and expansions to social programs, including unemployment and SNAP benefits.In this cross-sectional study using nationally representative survey data, we found that ECTC eligibility was associated with improved self-reported overall health and improved household food security. The study findings potentially provide evidence that unrestricted monthly cash transfer programs like the ECTC are not only effective antipoverty measures but also may be powerful population health tools. Future studies should continue to examine this policy’s association with health, barriers to its receipt, and its cost-effectiveness.


Pharmacy cost-saving programs often aim to reduce costs for members and payers by encouraging use of lower-tier or generic medications and lower-cost sales channels. In 2010, a national U.S. health plan began a novel pharmacy program directed at reducing pharmacy expenditures for targeted medications, including pregabalin. The program provided multiple options to avoid higher cost sharing: use mail order pharmacy or switch to a lower-cost alternative medication via mail order or retail. Members who did not choose any option eventually paid the full retail cost of pregabalin.To evaluate the impact of the pharmacy program on pregabalin and alternative medication use, health care costs, and health care utilization.This retrospective analysis of claims data included adult commercial health plan members with a retail claim for pregabalin in the first 13 months of the pharmacy program . Members whose benefit plan included the pharmacy program were assigned to the program cohort; all others were assigned to the nonprogram cohort. The program cohort index date was the first retail pregabalin claim during the ID period and after the program start; the nonprogram cohort index date was the first retail pregabalin claim during the ID period. All members were continuously enrolled for 12 months pre- and post-index and had at least 1 inpatient claim or ≥ 2 ambulatory visit claims for a pregabalin-indicated condition.Cohorts were propensity score matched (PSM) 1:1 with logistic regression on demographic and pre-index characteristics, including mail order and pregabalin use, comorbidity, health care costs, and health care utilization. Pregabalin, gabapentin and other alternative medication use, health care costs, and health care utilization were measured. The program cohort was also divided into 2 groups: members who changed to gabapentin post-index and those who did not.A difference-in-differences (DiD) analysis was used to compare the between-cohort change in pregabalin and alternative medication use patterns, health care costs, and health care resource utilization from pre- to post-index. The within-cohort change from pre- to post-index was analyzed by McNemar’s test  or paired t-test (continuous variables). The Rao-Scott chi-square test  and general estimating equations (continuous) were used to analyze between-cohort differences at each time point. Differences in program member characteristics of those who changed versus those who did not change to gabapentin post-index were assessed by traditional chi-square test  or two-sample t-test (continuous variables).P < 0.001). Pregabalin mail order use increased from 3.1% to 48.1% of program members versus 2.8% to 9.4% of nonprogram members . Program members were also more likely to change to the anticonvulsant gabapentin post-index than were nonprogram members . Mean total health care costs were similar between cohorts, and the pre- to post-index change did not differ between cohorts . However, mean total pharmacy costs rose from pre-index to post-index by $820 and $790 in the program and nonprogram cohorts, respectively (both P < 0.001); the increase was similar between cohorts . Program members who changed to gabapentin had a higher mean comorbidity score (P = 0.001) and greater post-index use of opioids, alternative medications, and health care resources (P < 0.050) than program members who did not change to gabapentin.A total of 1,218 members in each cohort were PSM. Mean age was 51 years, 76.7% were women, and the most common pregabalin-indicated condition was fibromyalgia (77.6%). After the program start, the mean number of pregabalin claims from mail order and retail combined decreased in the program cohort from 4.7 pre-index to 3.8 post-index, and increased in the nonprogram cohort from 4.7 pre-index to 6.2 post-index (DiD, The pharmacy program increased mail order use of pregabalin but reduced pregabalin claims from any venue. Program members were more likely to change to gabapentin than were nonprogram members, and those who changed had higher comorbidity, use of alternative medication, and health care resources. Despite increased mail order use for pregabalin and greater change to gabapentin by program members, the pharmacy program was not cost saving with respect to mean pharmacy or total health care costs. What is already known about this subjectRecent pharmacy programs aimed at controlling rising expenditures have focused on reducing copayments through formulary tier changes.Most programs have used a single intervention and have focused primarily on medications for cardiac and diabetic care for which a generic alternative is available.These programs have been widely studied and, while many have been effective in improving medication adherence, they have been largely cost neutral.What this study addsThis study evaluated the impact of a novel pharmacy program that provides members with multiple options to avoid higher cost sharing of targeted medications: use mail order pharmacy or switch to a lower-cost alternative medication via mail order or retail.Members who changed from pregabalin to gabapentin had a higher disease burden, use of alternative medications, and health care utilization.Despite a shift in pregabalin use from retail to mail order and greater use of gabapentin by program members, pharmacy costs increased after the program start. Total health care spending did not change.1 pharmacy benefit managers and health plans have developed strategies to reduce or offset expenditures. Most cost-containment programs include interventions such as switching from retail to mail order pharmacy,3 prior authorization, 4 step therapy,5 or formulary tier modifications.8 These programs are intended to reduce pharmacy costs for both members and payers by encouraging the use of lower-tier or generic medications and lower-cost sales channels. However, pharmacy cost savings typically are not evaluated in the context of overall health care spending.With rising prescription drug spending,9 While health plan savings in pharmacy costs have been realized when patient cost sharing increased,10 total health plan spending either did not decrease or it increased.14Pharmacy programs that contain a cost-sharing component have been the most widely evaluated.15More recently, pharmacy programs that provide financial incentives, rather than penalties, to patients have been introduced. The goal of incentive programs is to increase medication adherence by providing lower patient cost-sharing options, and consequently, improving health outcomes with the expectation of lower overall spending.19 Most studies of mandatory or incentivized pharmacy programs have focused on commonly used medications for diabetes and cardiac care for which a generic medication is also available. There is scarce information for other conditions, including those for which a generic medication is not available.Although reducing or eliminating prescription copayments has been associated with improved medication adherence for covered drugs, these programs have been largely cost neutral to the health plan.21 Lyrica, or branded pregabalin,22 is an anticonvulsant approved in the United States for neuropathic pain associated with DPN, PHN and spinal cord injury, fibromyalgia, and adjunctive therapy for adult partial onset seizures. However, some health plans have restricted access to pregabalin via prior authorization24 and step therapy.25 Although restricted access decreased use of pregabalin and increased use of some alternative medications, there was no impact on disease-specific24 or all-cause costs.25 Programs that offer patients multiple options to avoid higher cost sharing, including branded pregabalin, have not been investigated.Chronic pain conditions and disorders, such as painful diabetic peripheral neuropathy (DPN), postherpetic neuralgia (PHN), and fibromyalgia, share common pharmacologic treatments that include opioids, antidepressants, and anticonvulsants.22 which was the focus of this study. Health plan members who fill medications targeted by the pharmacy program at retail pharmacies are provided two “grace fills,” after which they may change to mail order delivery of the targeted medication  or change to a less costly alternative (generic or brand) medication via mail order or retail. Members who do not elect one of these options pay the full cost of the targeted medications charged by the retail pharmacy. Members are notified of these options at the point of sale and by letter and/or phone call after the first and second grace fills.In 2010, a national health plan began a novel cost-saving pharmacy program targeting selected tier 3 medications, including pregabalin,We hypothesized that the pharmacy program would encourage members to change from retail pharmacies to mail order for their pregabalin fills or change to alternative lower-cost medications such as gabapentin (either through mail order or retail venues). Our objective was to examine changes in pregabalin and alternative medication use patterns after the pharmacy program began and how these changes translated into total health care and pharmacy costs. Specifically, we used a large administrative claims database to examine the impact of this novel pharmacy benefit program on pregabalin and alternative medication use patterns, health care costs, and health care resource utilization.International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes, site of service codes, and health plan- and patient-paid amounts from providers and facilities. Pharmacy claims encompass National Drug Code numbers for filled prescriptions, days supply, quantity of drug supplied, drug strength, and health plan- and patient-paid amounts. All study data were accessed using techniques compliant with the Health Insurance Portability and Accountability Act; therefore, a separate institutional review board approval was not sought.This retrospective study used claims data from the Optum Research Database (ORD). ORD contains medical and pharmacy claims and enrollment information from a large U.S. health plan with national coverage. Patient- and provider-reported outcomes are not available in ORD. Medical claims data include The sample included commercial health plan members who were ≥ 19 years in 2010 and had at least 1 retail pharmacy claim for pregabalin during the identification (ID) period . Benefit information was used to identify members in the pharmacy program, including the program start date for each member . Members with a retail pregabalin claim during the ID period and after the pharmacy program start date were considered the “program” cohort; the date of the first retail pregabalin claim after the program start date was the index date. Members were subject to the program design immediately upon program start date; the program did not include a washout period.Members whose benefit plan did not include the pharmacy program were assigned to the nonprogram cohort; their index date was the first retail pregabalin claim during the ID period. Members in both cohorts were continuously enrolled in the health plan for 12 months pre-index and 12 months post-index and had at least 1 inpatient claim or ≥ 2 ambulatory visit claims with an ICD-9-CM diagnosis code for DPN, fibromyalgia, PHN, or partial onset seizure (22) at any time were excluded. As an exploratory analysis, 2 subgroups within the program cohort were defined: members who changed from pregabalin to gabapentin during the post-index period, and members who did not. Inclusion in the change group required at least 1 claim for gabapentin post-index. Prior gabapentin use was not a study exclusion. Although specific alternative medications were not prescribed by the program, gabapentin was selected for this comparison because of its similar mechanism of action, generic (low cost) availability, and common use in step-therapy programs.Members whose pharmacy program status changed post-index or whose estimated average dose of pregabalin was > 600 milligrams per day , Charlson Comorbidity Index score,26 and the presence/absence of comorbid conditions,27 defined as at least 1 claim for the condition at any site of service  and the proportion of members using mail order for pregabalin fills. Use of alternative medications was defined by at least 1 claim for drug classes that may be prescribed for pain conditions: gabapentin, opioids (short- and long-acting), serotonin and norepinephrine reuptake inhibitors (SNRIs), selective serotonin reuptake inhibitors (SSRIs), and tricyclic antidepressants , ER visits, and inpatient admissions.Costs, both medical and pharmacy, were computed as the combined health plan- and member-paid amounts and categorized as medical , pharmacy, and total costs . All costs were inflation-adjusted to 2012 dollars.We also defined post-index discontinuation of pregabalin as the first gap in pregabalin days supply > 90 days computed from each prescription claim date and the number of days supply on the claim. A 30-day gap was a secondary discontinuation measure. The proportion of members filling a prescription for any medication by mail order in the pre-index and post-index periods was also assessed. As an exploratory analysis, we also examined the pre- and post-index characteristics of program members who changed from pregabalin to gabapentin in the post-index period.29 Independent variables considered for inclusion in the propensity score were characteristics where the between-cohort P value was ≤ 0.250 in pre-PSM comparisons or likely to have meaningful impact on primary outcome variables. The final logistic regression controlled for age , gender, U.S. Census region, pre-index Charlson Comorbidity Index score , binary indicators of pre-index fibromyalgia and DPN , count of pre-index pregabalin pharmacy claims , a binary indicator of pre-index mail order pregabalin use, proportion of total pre-index pharmacy claims obtained via mail order , a binary indicator of pre-index inpatient hospitalization, pre-index all-cause medical costs, and pre-index all-cause pharmacy costs (30 and required matching within 0.2 of the standard deviation of the logit of the propensity score.32Members in the nonprogram cohort were propensity score matched (PSM) 1:1 to program cohort members.33 Between-cohort matched comparisons were assessed by the Rao-Scott chi-square test34 for categorical variables and general estimating equations for continuous variables .Between-cohort unmatched comparisons were assessed with traditional chi-square tests for categorical variables and two-sample t-tests using Satterthwaite adjustment for unequal variances where appropriate for continuous variables.33 for categorical variables and paired t-tests for continuous variables.Within-cohort between time period comparisons (pre-index vs. post-index) were assessed using McNemar’s testWithin the program cohort, differences between members who changed to gabapentin and those who did not were computed by traditional chi-square test for categorical variables and two-sample t-test for continuous variables.35 to compare the between-cohort change from pre-index to post-index. The DiD analyses adjusted for period (pre-index vs. post-index), cohort (program vs. nonprogram), and period by cohort interaction, and used robust errors to account for the correlation between multiple records per subject. The P value of interest in these analyses corresponds to the period by cohort interaction. A gamma distribution was used for cost measures, a negative binomial distribution was used for count variables , and a logistic distribution was used for binary measures. A log link was used for all measures. Independent working correlation structures were used in all generalized estimating equation models.The primary outcomes measures  were analyzed using a difference-in-differences (DiD) approachThere were 4,528 members  who met the inclusion criteria . After PP = 0.912), and 99% of pregabalin claims in both cohorts were filled at retail pharmacies . The proportion of all medication fills from mail order was also similar between cohorts.P < 0.001). At the retail level, the mean number of pregabalin claims decreased in the program cohort from 4.60 to 2.46 and increased in the nonprogram cohort from 4.61 to 5.96 . Only 30.4% of program cohort members had retail pharmacy claims for pregabalin after the 2 grace fills provided under the program compared with 66.8% of the nonprogram cohort . Program members were also more likely to discontinue pregabalin (from any venue) than were nonprogram members: 64% of program members versus 52% of nonprogram members discontinued based on a 90-day supply gap threshold and 84% versus 74% using a 30-day supply gap .After program implementation, the mean number of claims for pregabalin from any venue decreased in the program cohort  and increased in the nonprogram cohort . The DiD analysis for the between-cohort change was significant (P < 0.001). The mean number of mail order pregabalin claims also increased in both cohorts, but the rise was more pronounced among program (0.06 to 1.34) than nonprogram members (0.07 to 0.20) and the between-cohort change was significant .The proportion of program members using mail-order pharmacy for pregabalin rose from 3.1% pre-index to 48.1% post-index compared with an increase from 2.8% to 9.4% among nonprogram members  and make the change to gabapentin more quickly than nonprogram members . P ≥ 0.162). SSRI use declined modestly in the program cohort and increased slightly in the nonprogram cohort .After the program start, program members were more likely to change from pregabalin to gabapentin than were nonprogram members  and total costs  between cohorts was not significant. Although mean pharmacy costs rose significantly from pre-index to post-index by $820 and $790 in the program and nonprogram cohorts (P < 0.001), respectively, the increase in pharmacy costs was similar between cohorts .All-cause total medical and pharmacy costs were similar for both cohorts in both the pre-index and post-index periods . The preP = 0.004) as a result of a post-index decline in the likelihood of inpatient service use among program members and a slight increase among nonprogram members with inpatient service use. The pre-index to post-index change in the count of resource utilizations was significant only for office visits  as a result of a larger post-index decline among nonprogram members compared with program members.Pre-index resource utilization was similar between cohorts for both the proportion of members using each service category and the mean count of encounters, with the exception of the mean count of pre-index office visits, which was higher in the nonprogram cohort. The pre- to post-index change in the proportion of members using inpatient services was significant . Members who changed also had higher mean pre-index Charlson Comorbidity Index scores  and greater pre-index total health care costs . After the pharmacy program began, program members who changed to gabapentin were more likely to use short- or long-acting opioids (P = 0.041), TCAs (P = 0.018), and SSRIs (P = 0.029) than were program members who did not change. The likelihood of outpatient, ER, and inpatient resource utilization and the mean count of office and outpatient visits were also higher among pharmacy program members who changed to gabapentin (P ≤ 0.020).Pharmacy program members who changed to gabapentin were more likely to have a pre-index diagnosis of DPN and less likely to have a pre-index diagnosis of partial onset seizure than were program members who did not change  after instituting a reduced-cost pharmacy program for medications commonly used in chronic conditions; yet the insurer’s $5.7 million decline in nondrug spending was not fully offset by the $6.4 million increase in drug spending.17 with the exception of a plan that coupled reduced cost sharing with disease management among diabetic patients and demonstrated a positive return on investment.36Studies of single employer plans that provided lower member cost-sharing programs via reduced copayments have also been cost neutral for total spending,37 When the pharmacy program cohort was stratified by members who changed to gabapentin versus those who did not, we found that members who changed had higher comorbidity scores; were more likely to use opioids, TCAs, or SSRIs; were higher health care users; and had higher costs. Further study is warranted to understand the circumstances that cause the higher-using/sicker members in the program to change from pregabalin to gabapentin.Program members were more likely to discontinue pregabalin and change to gabapentin, which is consistent with previous studies of higher cost-sharing pharmacy programs.Ultimately, changes in pharmacy benefit design must be judged in the context of both clinical and economic outcomes. More than 90% of the members in this study had a condition for which patient-reported outcomes, such as pain severity, should be a consideration for clinical management of the condition. These measures and the patient and physician rationale for switching to alternative medication use should be included in future studies to provide a more comprehensive assessment of the impact of pharmacy program changes that provide cost savings to members for alternative medication use.The ability of claims data to accurately capture an individual’s medical and pharmacy history can be limited. Health care services that occurred before or after the study were not evaluated. We tracked utilization and costs for 12 months; cost savings may be realized with longer examination periods if the 12-month post-index period were extended well beyond the 60- to 90-day post-implementation window when most members were expected to select a cost-saving option.This program was available only to commercially insured plans. The results may not be generalizable to public plans such as Medicare or Medicaid.Additionally, benefit packages are often customized to specific employer groups. Our study used a general benefit package identifier to determine pharmacy program eligibility. Some employer groups could have had customized agreements that were not captured in the available data, resulting in incorrect attribution to a study cohort. We expect this miscapture to happen infrequently and to be equally applicable to the miscapture of members in either of the study cohorts; it would therefore not be expected to bias the outcomes. Additional unobservable differences between the study cohorts may have influenced the findings that could not be included in this analysis.Finally, this was an independent analysis of a pharmacy program implemented by a large health plan. The study investigators had no role in the design or execution of the pharmacy program. The metrics by which the relative success or failure of the pharmacy program were judged were not available to the investigators of this study.A novel pharmacy cost-saving program affected use of pregabalin but did not change overall health care costs. As the literature suggests, this program was effective in increasing use of mail order pregabalin and use of gabapentin as an alternative medication. Members who changed from pregabalin to gabapentin were sicker and higher health care users. While the overall objective of the pharmacy program was to reduce costs by changing member use of retail pregabalin to less expensive options, no cost savings were observed in the first year after the pharmacy program was implemented.