JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts.

INTRODUCTION: Racialized population groups have worse health outcomes across the world compared with non-racialized populations. Evidence suggests that collecting race-based data should be done to mitigate racism as a barrier to health equity, and to amplify community voices, promote transparency, accountability, and shared governance of data. However, limited evidence exists on the best ways to collect race-based data in healthcare contexts. This systematic review aims to synthesize opinions and texts on the best practices for collecting race-based data in healthcare contexts. METHODS AND ANALYSES: We will use the Joanna Briggs Institute (JBI) method for synthesizing text and opinions. JBI is a global leader in evidence-based healthcare and provides guidelines for systematic reviews. The search strategy will locate both published and unpublished papers in English in CINAHL, Medline, PsycINFO, Scopus and Web of Science from 1 January 2013 to 1 January 2023, as well as unpublished studies and grey literature of relevant government and research websites using Google and ProQuest Dissertations and Theses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement methodology for systematic reviews of text and opinion will be applied, including screening and appraisal of the evidence by two independent reviewers and data extraction using JBI's Narrative, Opinion, Text, Assessment, Review Instrument. This JBI systematic review of opinion and text will address gaps in knowledge about the best ways to collect race-based data in healthcare. Improvements in race-based data collection, may be related to structural policies that address racism in healthcare. Community participation may also be used to increase knowledge about collecting race-based data. ETHICS AND DISSEMINATION: The systematic review does not involve human subjects. Findings will be disseminated through a peer-reviewed publication in JBI evidence synthesis, conferences and media. PROSPERO REGISTRATION NUMBER: CRD42022368270.

An adaptive design to screen, treat, and retain people with opioid use disorders who use methamphetamine in methadone clinics (STAR-OM): study protocol of a clinical trial.

BACKGROUND: Methamphetamine use could jeopardize the current efforts to address opioid use disorder and HIV infection. Evidence-based behavioral interventions (EBI) are effective in reducing methamphetamine use. However, evidence on optimal combinations of EBI is limited. This protocol presents a type-1 effectiveness-implementation hybrid design to evaluate the effectiveness, cost-effectiveness of adaptive methamphetamine use interventions, and their implementation barriers in Vietnam. METHOD: Design: Participants will be first randomized into two frontline interventions for 12 weeks. They will then be placed or randomized to three adaptive strategies for another 12 weeks. An economic evaluation and an ethnographic evaluation will be conducted alongside the interventions. PARTICIPANTS: We will recruit 600 participants in 20 methadone clinics. ELIGIBILITY CRITERIA: (1) age 16+; (2) Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) scores ≥ 10 for methamphetamine use or confirmed methamphetamine use with urine drug screening; (3) willing to provide three pieces of contact information; and (4) having a cell phone. OUTCOMES: Outcomes are measured at 13, 26, and 49 weeks and throughout the interventions. Primary outcomes include the (1) increase in HIV viral suppression, (2) reduction in HIV risk behaviors, and (3) reduction in methamphetamine use. COVID-19 response: We developed a response plan for interruptions caused by COVID-19 lockdowns to ensure data quality and intervention fidelity. DISCUSSION: This study will provide important evidence for scale-up of EBIs for methamphetamine use among methadone patients in limited-resource settings. As the EBIs will be delivered by methadone providers, they can be readily implemented if the trial demonstrates effectiveness and cost-effectiveness. TRIAL REGISTRATION: ClinicalTrials.gov NCT04706624. Registered on 13 January 2021. https://clinicaltrials.gov/ct2/show/NCT04706624.

Investigation of the Kinematic Upper-Limb Movement Assessment (KUMA): A Pilot Study.

Purpose: Upper limb movement disorders are common after stroke and can severely impact activities of daily living. Available clinical measures of these disorders are subjective and may lack the sensitivity needed to track a patient's progress and to compare different therapies. Kinematic analyses can provide clinicians with more objective measures for evaluating the effects of rehabilitation. We present a novel method to assess the quality of upper limb movement: the Kinematic Upper-limb Movement Assessment (KUMA). This assessment uses motion capture to provide three kinematic measures of upper limb movement: active range of motion, speed, and compensatory trunk movement. The researchers sought to evaluate the ability of the KUMA to distinguish motion in the affected versus unaffected limb. Method: We used the KUMA with three participants with stroke to assess three single-joint movements in: wrist flexion and extension, elbow flexion and extension, and shoulder flexion/extension and abduction/adduction. Participants also completed the Modified Ashworth Scale and the Chedoke-McMaster Stroke Assessment, two clinical measures of functional ability. Results: The KUMA distinguished between affected and unaffected upper limb motion. Conclusions: The KUMA provides clinicians with supplementary objective information for motion characterization that is not available through clinical measures alone. The KUMA can complement existing clinical measures such as the MAS and CMSA and can be helpful for monitoring patient progress.

Objectif : les troubles des mouvements de membres supérieurs sont courants après un accident vasculaire cérébral et peuvent nuire fortement aux activités de la vie quotidienne. Les mesures cliniques disponibles pour ces troubles sont subjectives et ne possèdent peut-être pas la sensibilité nécessaire pour suivre le progrès d'un patient et comparer les diverses thérapies. Les analyses de cinématique peuvent fournir aux cliniciens des mesures plus objectives pour évaluer les effets de la réadaptation. Les auteurs présentent une nouvelle méthode pour évaluer la qualité des mouvements des membres supérieurs : l'évaluation cinématique des mouvements des membres supérieurs (KUMA, pour Kinematic Upper-limb Movement Assessment ). Cette évaluation fait appel à la capture des mouvements pour fournir trois mesures cinématiques des mouvements des membres supérieurs : l'amplitude de mouvements actifs, la vitesse et le mouvement compensatoire du tronc. Les chercheurs ont cherché à évaluer la capacité de la KUMA à distinguer le mouvement du membre touché par rapport au membre non touché. Méthodologie : les chercheurs ont utilisé la KUMA auprès de trois participants ayant subi un accident vasculaire cérébral pour évaluer trois mouvements monoarticulaires : flexion et extension du poignet, flexion et extension du coude, et flexion et extension, abduction et adduction de l'épaule. Les participants ont également utilisé l'échelle modifiée d'Ashworth (MAS) et l'évaluation Chedoke-McMaster de l'accident vasculaire cérébral (AVC), deux mesures cliniques de la capacité fonctionnelle. Résultats : la KUMA distinguait le mouvement du membre supérieur atteint de celui qui ne l'était pas. Conclusions : La KUMA fournit aux cliniciens de l'information objective supplémentaires pour caractériser les mouvements d'une manière qui n'est pas disponible par les seules mesures cliniques. La KUMA peut compléter les mesures cliniques en place comme l'échelle modifiée d'Ashworth et l'évaluation Chedoke-McMaster de l'AVC et peut être utile pour surveiller le progrès des patients.

Age-dependence of healthcare interventions for COVID-19 in Ontario, Canada.

BACKGROUND: Patient age is one of the most salient clinical indicators of risk from COVID-19. Age-specific distributions of known SARS-CoV-2 infections and COVID-19-related deaths are available for many regions. Less attention has been given to the age distributions of serious medical interventions administered to COVID-19 patients, which could reveal sources of potential pressure on the healthcare system should SARS-CoV-2 prevalence increase, and could inform mass vaccination strategies. The aim of this study is to quantify the relationship between COVID-19 patient age and serious outcomes of the disease, beyond fatalities alone. METHODS: We analysed 277,555 known SARS-CoV-2 infection records for Ontario, Canada, from 23 January 2020 to 16 February 2021 and estimated the age distributions of hospitalizations, Intensive Care Unit admissions, intubations, and ventilations. We quantified the probability of hospitalization given known SARS-CoV-2 infection, and of survival given COVID-19-related hospitalization. RESULTS: The distribution of hospitalizations peaks with a wide plateau covering ages 60-90, whereas deaths are concentrated in ages 80+. The estimated probability of hospitalization given known infection reaches a maximum of 27.8% at age 80 (95% CI 26.0%-29.7%). The probability of survival given hospitalization is nearly 100% for adults younger than 40, but declines substantially after this age; for example, a hospitalized 54-year-old patient has a 91.7% chance of surviving COVID-19 (95% CI 88.3%-94.4%). CONCLUSIONS: Our study demonstrates a significant need for hospitalization in middle-aged individuals and young seniors. This need is not captured by the distribution of deaths, which is heavily concentrated in very old ages. The probability of survival given hospitalization for COVID-19 is lower than is generally perceived for patients over 40. If acute care capacity is exceeded due to an increase in COVID-19 prevalence, the distribution of deaths could expand toward younger ages. These results suggest that vaccine programs should aim to prevent infection not only in old seniors, but also in young seniors and middle-aged individuals, to protect them from serious illness and to limit stress on the healthcare system.

Development of an Oncology Acute Care Risk Prediction Model.

PURPOSE: Acute care utilization (ACU), including emergency department (ED) visits or hospital admissions, is common in patients with cancer and may be preventable. The Center for Medicare & Medicaid Services recently implemented OP-35, a measure in the Hospital Outpatient Quality Reporting Program focused on ED visits and inpatient admissions for 10 potentially preventable conditions that arise within 30 days of chemotherapy. This new measure exemplifies a growing focus on preventing unnecessary ACU. However, identifying patients at high risk of ACU remains a challenge. We developed a real-time clinical prediction model using a discrete point allocation system to assess risk for ACU in patients with active cancer. METHODS: We performed a retrospective cohort analysis of patients with active cancer from a large urban academic medical center. The primary outcome, ACU, was evaluated using a multivariate logistic regression model with backward variable selection. We used estimates from the multivariate logistic model to construct a risk index using a discrete point allocation system. RESULTS: Eight thousand two hundred forty-six patients were included in the analysis. ED utilization in the last 90 days, history of chronic obstructive pulmonary disease, congestive heart failure or renal failure, and low hemoglobin and low neutrophil count significantly increased risk for ACU. The model produced an overall C-statistic of 0.726. Patients defined as high risk (achieving a score of 2 or higher on the risk index) represented 10% of total patients and 46% of ACU. CONCLUSION: We developed an oncology acute care risk prediction model using a risk index-based scoring system, the REDUCE (Reducing ED Utilization in the Cancer Experience) score. Further efforts to evaluate the effectiveness of our model in predicting ACU are ongoing.

On the basic reproduction number in semi-Markov switching networks.

Basic reproduction number R0 in network epidemic dynamics is studied in the case of stochastic regime-switching networks. For generality, the dependence between successive networks is considered to follow a continuous time semi-Markov chain. R0 is the weighted average of the basic reproduction numbers of deterministic subnetworks. Its position with respect to 1 can determine epidemic persistence or extinction in theories and simulations.

Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak.

A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number [Formula: see text]-the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of [Formula: see text] during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates of [Formula: see text] across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates of [Formula: see text] for the SARS-CoV-2 outbreak, showing that many [Formula: see text] estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of [Formula: see text], including the shape of the generation-interval distribution, in efforts to estimate [Formula: see text] at the outset of an epidemic.

Experienced homophobia and gene expression alterations in Black and Latino men who have sex with men in Los Angeles County.

Men who have sex with men (MSM) experience high rates of homophobic victimization, which is linked to myriad chronic physical and mental health disparities. Social adversity such as rejection, isolation, and racial discrimination can induce a conserved transcriptional response to adversity (CTRA) involving upregulation of proinflammatory genes and downregulation of type I interferon and antibody synthesis genes. This study specifically examines whether homophobic victimization is associated with expression of CTRA profiles in Black and Latino MSM living in Los Angeles. Analyses linked behavioral survey data with quantified RNA from leukocytes from blood samples of 70 participants over 12 months. CTRA gene expression was increased by 3.1-fold in MSM who experienced homophobic victimization while adjusting for major leukocyte subsets and sociodemographics. Accounting for all these factors, CTRA gene expression was significantly enhanced in MSM who identified as Black compared to Latino. Our findings identify experiences of homophobic victimization as drivers of inflammatory and type I interferon gene expression profiles, which can contribute to physical and mental health challenges in Black and Latino MSM.

Patterns of seasonal and pandemic influenza-associated health care and mortality in Ontario, Canada.

BACKGROUND: Mathematical and statistical models are used to project the future time course of infectious disease epidemics and the expected future burden on health care systems and economies. Influenza is a particularly important disease in this context because it causes annual epidemics and occasional pandemics. In order to forecast health care utilization during epidemics-and the effects of hospitalizations and deaths on the contact network and, in turn, on transmission dynamics-modellers must make assumptions about the lengths of time between infection, visiting a physician, being admitted to hospital, leaving hospital, and death. More reliable forecasts could be be made if the distributions of times between these types of events ("delay distributions") were known. METHODS: We estimated delay distributions in the province of Ontario, Canada, between 2006 and 2010. To do so, we used encrypted health insurance numbers to link 1.34 billion health care billing records to 4.27 million hospital inpatient stays. Because the four year period we studied included three typical influenza seasons and the 2009 influenza pandemic, we were able to compare the delay distributions in non-pandemic and pandemic settings. We also estimated conditional probabilities such as the probability of hospitalization within the year if diagnosed with influenza. RESULTS: In non-pandemic [pandemic] years, delay distribution medians (inter-quartile ranges) were: Service to Admission 6.3 days (0.1-17.6 days) [2.4 days (-0.3-13.6 days)], Admission to Discharge 3 days (1.4-5.9 days) [2.6 days (1.2-5.1 days)], Admission to Death 5.3 days (2.1-11 days) [6 days (2.6-13.1 days)]. (Service date is defined as the date, within the year, of the first health care billing that included a diagnostic code for influenza-like-illness.) Among individuals diagnosed with either pneumonia or influenza in a given year, 19% [16%] were hospitalized within the year and 3% [2%] died in hospital. Among all individuals who were hospitalized, 10% [12%] were diagnosed with pneumonia or influenza during the year and 5% [5%] died in hospital. CONCLUSION: Our empirical delay distributions and conditional probabilities should help facilitate more accurate forecasts in the future, including improved predictions of hospital bed demands during influenza outbreaks, and the expected effects of hospitalizations on epidemic dynamics.

A feature learning approach based on XGBoost for driving assessment and risk prediction.

This study designs a framework of feature extraction and selection, to assess vehicle driving and predict risk levels. The framework integrates learning-based feature selection, unsupervised risk rating, and imbalanced data resampling. For each vehicle, about 1300 driving behaviour features are extracted from trajectory data, which produce in-depth and multi-view measures on behaviours. To estimate the risk potentials of vehicles in driving, unsupervised data labelling is proposed. Based on extracted risk indicator features, vehicles are clustered into various groups labelled with graded risk levels. Data under-sampling of the safe group is performed to reduce the risk-safe class imbalance. Afterwards, the linkages between behaviour features and corresponding risk levels are built using XGBoost, and key features are identified according to feature importance ranking and recursive elimination. The risk levels of vehicles in driving are predicted based on key features selected. As a case study, NGSIM trajectory data are used in which four risk levels are clustered by Fuzzy C-means, 64 key behaviour features are identified, and an overall accuracy of 89% is achieved for behaviour-based risk prediction. Findings show that this approach is effective and reliable to identify important features for driving assessment, and achieve an accurate prediction of risk levels.

Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation.

BACKGROUND: Despite the effectiveness of levodopa for treatment of Parkinson's disease (PD), prolonged usage leads to development of motor complications, most notably levodopa-induced dyskinesia (LID). Persons with PD and their physicians must regularly modify treatment regimens and timing for optimal relief of symptoms. While standardized clinical rating scales exist for assessing the severity of PD symptoms, they must be administered by a trained medical professional and are inherently subjective. Computer vision is an attractive, non-contact, potential solution for automated assessment of PD, made possible by recent advances in computational power and deep learning algorithms. The objective of this paper was to evaluate the feasibility of vision-based assessment of parkinsonism and LID using pose estimation. METHODS: Nine participants with PD and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating Scale (UPDRS). Movement trajectories of individual joints were extracted from videos of PD assessment using Convolutional Pose Machines, a pose estimation algorithm built with deep learning. Features of the movement trajectories (e.g. kinematic, frequency) were used to train random forests to detect and estimate the severity of parkinsonism and LID. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. RESULTS: For LID, the communication task yielded the best results (detection: AUC = 0.930, severity estimation: r = 0.661). For parkinsonism, leg agility had better results for severity estimation (r = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741 and 0.530, respectively. CONCLUSION: The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Convenient and objective assessment of PD symptoms will facilitate more frequent touchpoints between patients and clinicians, leading to better tailoring of treatment and quality of care.

Intersecting burdens: Homophobic victimization, unstable housing, and methamphetamine use in a cohort of men of color who have sex with men.

BACKGROUND: Men who have sex with men with histories of homophobic victimization bear heightened risk of unstable housing and methamphetamine use. However, it is unclear whether unstable housing explains the link between homophobic victimization and methamphetamine use in this group. The present study aims to test associations between homophobic victimization, unstable housing, and recent methamphetamine use across 24 months in a cohort of men of color who have sex with men (MoCSM). METHODS: Our analysis stems from data of 1342 person-visits from 401 MoCSM participating in an ongoing cohort study. We performed a lagged multilevel negative binominal regression to test the association between past homophobic victimization and recent unstable housing, and a lagged multilevel ordered logistic regression to test the association between past homophobic victimization recent methamphetamine use. We then performed a path analysis to test whether recent unstable housing mediates the association between past homophobic victimization and recent methamphetamine use. RESULTS: Findings showed homophobic victimization associated significantly with increased odds of unstable housing (IRR = 1.70, 95% CI [1.35, 2.14], p < 0.001) and recent methamphetamine use (OR = 1.40, 95% CI [1.15, 1.71], p = 0.001). Mediation analysis indicated that past homophobic victimization was indirectly associated with recent methamphetamine use via unstable housing (OR = 1.06 (95% CI [1.01, 1.11], p = 0.010). CONCLUSION: Our findings suggest that homophobic victimization and unstable housing should be addressed alongside treatment and prevention of methamphetamine use in MoCSM.

Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features.

INTRODUCTION: Technological solutions for quantifying Parkinson's disease (PD) symptoms may provide an objective means to track response to treatment, including side effects such as levodopa-induced dyskinesia. Vision-based systems are advantageous as they do not require physical contact with the body and have minimal instrumentation compared to wearables. We have developed a vision-based system to quantify a change in dyskinesia as reported by patients using 2D videos of clinical assessments during acute levodopa infusions. METHODS: Nine participants with PD completed a total of 16 levodopa infusions, where they were asked to report important changes in dyskinesia (i.e. onset and remission). Participants were simultaneously rated using the UDysRS Part III (from video recordings analyzed post-hoc). Body joint positions and movements were tracked using a state-of-the-art deep learning pose estimation algorithm applied to the videos. 416 features (e.g. kinematics, frequency distribution) were extracted to characterize movements. The sensitivity and specificity of each feature to patient-reported changes in dyskinesia severity was computed and compared with physician-rated results. RESULTS: Features achieved similar or superior performance to the UDysRS for detecting the onset and remission of dyskinesia. The best AUC for detecting onset of dyskinesia was 0.822 and for remission of dyskinesia was 0.958, compared to 0.826 and 0.802 for the UDysRS. CONCLUSIONS: Video-based features may provide an objective means of quantifying the severity of levodopa-induced dyskinesia, and have responsiveness as good or better than the clinically-rated UDysRS. The results demonstrate encouraging evidence for future integration of video-based technology into clinical research and eventually clinical practice.

Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches.

Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).

Two approaches to forecast Ebola synthetic epidemics.

We use two modelling approaches to forecast synthetic Ebola epidemics in the context of the RAPIDD Ebola Forecasting Challenge. The first approach is a standard stochastic compartmental model that aims to forecast incidence, hospitalization and deaths among both the general population and health care workers. The second is a model based on the renewal equation with latent variables that forecasts incidence in the whole population only. We describe fitting and forecasting procedures for each model and discuss their advantages and drawbacks. We did not find that one model was consistently better in forecasting than the other.

Direct health-care cost utilization in Hong Kong inflammatory bowel disease patients in the initial 2 years following diagnosis.

BACKGROUND AND AIM: There are scanty data on the health-care utilization from Asia where the incidence of inflammatory bowel disease (IBD) is rising rapidly. We aim to determine the direct health-care costs in the first 2 years of diagnosis in an IBD cohort from Hong Kong and the factors associated with high cost outliers. METHODS: This is a retrospective cohort study that included patients newly diagnosed with IBD in a territory-wide IBD registry. Patients' clinical information, hospitalization records, investigations, and IBD treatments were retrieved for up to 2 years following diagnosis of IBD. RESULTS: Four hundred and thirty-five newly diagnosed IBD patients were included: 198 with Crohn's disease and 237 with ulcerative colitis. Total direct medical expenditure for this cohort 2 years after the IBD diagnosis was $7 072 710: hospitalizations (33%), 5-aminosalicylic acid (23%), imaging and endoscopy (17%), outpatient visits (10%), surgery (8%), and biologics (6%). Mean direct medical costs per patient-year were significantly higher for Crohn's disease ($9918) than ulcerative colitis ($6634; P, 0.001). The total direct health-care cost decreased significantly after transition to the second year (P < 0.01). High cost (> 90th percentile) outliers were associated with surgery (OR 7.1, 95% CI 2.9-17.2) and low hemoglobin on presentation (OR 0.83, 95% CI 0.70-0.96). CONCLUSIONS: Hospitalization and 5-aminosalicylic acid usage accounted for 56% of total direct medical costs in the first 2 years of our newly diagnosed IBD patients. Direct health-care costs were higher in the first year compared with the second year of diagnosis. Surgery and low hemoglobin on presentation were associated with high cost outliers.

Impact of Inpatient Harms on Hospital Finances and Patient Clinical Outcomes.

OBJECTIVE: The aim of this study was to determine the impact of all-cause inpatient harms on hospital finances and patient clinical outcomes. RESEARCH DESIGN: A retrospective analysis of inpatient harm from 24 hospitals in a large multistate health system was conducted during 2009 to 2012 using the Institute of Healthcare Improvement Global Trigger Tool for Measuring Adverse Events. Inpatient harms were detected and categorized into harm (F-I), temporary harm (E), and no harm. RESULTS: Of the 21,007 inpatients in this study, 15,610 (74.3%) experienced no harm, 2818 (13.4%) experienced temporary harm, and 2579 (12.3%) experienced harm. A patient with harm was estimated to have higher total cost ($4617 [95% confidence interval (CI), $4364 to 4871]), higher variable cost ($1774 [95% CI, $1648 to $1900]), lower contribution margin (-$1112 [95% CI, -$1378 to -$847]), longer length of stay (2.6 d [95% CI, 2.5 to 2.8]), higher mortality probability (59%; odds ratio, 1.4 [95% CI, 1.0 to 2.0]), and higher 30-day readmission probability (74.4%; odds ratio, 2.9 [95% CI, 2.6 to 3.2]). A patient with temporary harm was estimated to have higher total cost ($2187 [95% CI, $2008 to $2366]), higher variable cost ($800 [95% CI, $709 to $892]), lower contribution margin (-$669 [95% CI, -$891 to -$446]), longer length of stay (1.3 d [95% CI, 1.2 to 1.4]), mortality probability not statistically different, and higher 30-day readmission probability (54.6%; odds ratio, 1.2 [95% CI, 1.1 to 1.4]). Total health system reduction of harm was associated with a decrease of $108 million in total cost, $48 million in variable cost, an increase of contribution margin by $18 million, and savings of 60,000 inpatient care days. CONCLUSIONS: This all-cause harm safety study indicates that inpatient harm has negative financial outcomes for hospitals and negative clinical outcomes for patients.

Incorporating periodic variability in hidden Markov models for animal movement.

BACKGROUND: Clustering time-series data into discrete groups can improve prediction and provide insight into the nature of underlying, unobservable states of the system. However, temporal variation in probabilities of group occupancy, or the rates at which individuals move between groups, can obscure such signals. We use finite mixture and hidden Markov models (HMMs), two standard clustering techniques, to model long-term hourly movement data from Florida panthers (Puma concolor coryi). Allowing for temporal heterogeneity in transition probabilities, a straightforward but little-used extension of the standard HMM framework, resolves some shortcomings of current models and clarifies the movement patterns of panthers. RESULTS: Simulations and analyses of panther data showed that model misspecification (omitting important sources of variation) can lead to overfitting/overestimating the underlying number of movement states. Models incorporating temporal heterogeneity identify fewer underlying states, and can make out-of-sample predictions that capture observed diurnal and autocorrelated movement patterns exhibited by Florida panthers. CONCLUSION: Incorporating temporal heterogeneity improved goodness of fit and predictive capability as well as reducing the selected number of movement states closer to a biologically interpretable level, although there is further room for improvement here. Our results suggest that incorporating additional structure in statistical models of movement can allow more accurate assessment of appropriate model complexity.

Adherence, persistence, healthcare utilization, and cost benefits of guideline-recommended hepatitis B pharmacotherapy.

OBJECTIVE: To compare pharmacotherapy adherence, persistence, and healthcare utilization/costs among US patients with chronic hepatitis B (CHB) initiated on an oral antiviral monotherapy recommended as first-line treatment by current national (US) guidelines vs an oral antiviral not recommended as first-line monotherapy. RESEARCH DESIGN AND METHODS: In this retrospective cohort study, patients aged 18-64 with medical claims for CHB who initiated an oral antiviral monotherapy for CHB between 07/01/05 and 01/31/10 were identified from a large US commercial health insurance claims database. Patients were continuously enrolled for a 6-month baseline period and ≥90 days follow-up. They were assigned to 'currently recommended first-line therapy' (RT: entecavir or tenofovir) or 'not currently recommended first-line therapy' (NRT: lamivudine, telbivudine, or adefovir) cohorts. MAIN OUTCOME MEASURES: Multivariate analyses were conducted to compare treatment adherence, persistence, healthcare utilization, and costs for RT vs NRT cohorts. RESULTS: Baseline characteristics were similar between RT (n=825) and NRT (n=916) cohorts. In multivariate analyses, RT patients were twice as likely as NRT patients to be adherent (OR=2.09; p<0.01) and persistent (mean: RT=361 days, NRT=298 days; p<0.01) and half as likely to have an inpatient stay (OR=0.527; p<0.01). Between the two oral antivirals recommended as first-line treatment, even though pharmacy cost was higher for entecavir, mean total healthcare costs for entecavir and tenofovir were similar ($1214 and $1332 per patient per month, respectively). Similar results were also observed with regard to adherence, persistence, and healthcare use for entecavir and tenofovir. CONCLUSIONS: A limitation associated with analysis of administrative claims data is that coding errors can be mitigated but are typically not fully eradicated by careful study design. Nevertheless, the current findings clearly indicate the benefits of initiating CHB treatment with an oral antiviral monotherapy recommended as first-line treatment by current guidelines.