How individuals' opinions influence society's resistance to epidemics: an agent-based model approach.

BACKGROUND: Protecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration. METHODS: We propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis. RESULTS: Our results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor. CONCLUSIONS: Our results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

The Impact of Gender Affirming Medical Care During Adolescence on Adult Health Outcomes Among Transgender and Gender Diverse Individuals in the United States: The Role of State-Level Policy Stigma.

Purpose: Gender affirming medical care (GAMC) aims to alleviate gender dysphoria by helping people align their physical body more closely with their gender identity. Bills seeking to limit or prohibit GAMC for trans children and adolescents have become a controversial topic. This study aimed to examine whether exposures to GAMC during adolescence are associated with adult psychological and general health outcomes, and to demonstrate the mechanism through which state-level legislation may work to moderate the association. Methods: We conducted analyses using data from the 2015 U.S. Transgender Survey, which surveyed 27,715 transgender and gender diverse (TGD) adults between August and September of 2015. The study compared the health outcomes of those who had GAMC exposures during adolescence with those who did not. Moderation analysis with propensity score matching was used to adjust for potential confounding factors. The general and psychological health outcomes measured were past-month severe psychological distress, past-year suicidal ideation, participant's general health, and past-year health care avoidance due to possible mistreatment. Results: GAMC during adolescence was negatively associated with severe psychological distress in adulthood. When examining past-year health care avoidance due to possible mistreatment, the effect sizes differed significantly between those in a trans-supportive state and those in a trans-unsupportive state. Conclusion: Our work highlights the importance of state-level policy stigma in understanding the association between GAMC and health outcomes. Findings point to the importance of enacting long-term legislative safeguards against TGD discrimination and removing barriers to access the full spectrum of care for adolescents who identify as TGD.

Demand sensing and digital tracking for maternal child health (MCH) in Uganda: a pilot study for 'E+TRA health'.

BACKGROUND: Thirteen essential maternal child health (MCH) commodities, identified by the UN Commission on Life-Saving Commodities for Women and Children, could save the lives of more than 6 million women and children in Low-and-Middle-Income Countries (LMICs) if made available at the point of care. To reduce stockout of those commodities and improve the health supply chains in LMICs, the Electronic TRAcking system for healthcare commodities (E+TRA Health), an all-in-one out-of-box solution, was developed to track and manage medical commodities at lower-level health facilities in rural areas. It aims to support real-time monitoring and decision-making to (1) reduce the time needed to prepare orders, (2) reduce stockout and overstock cases of targeted medical supplies, (3) help improve patient outcomes. In this study, we adopted an integrated approach to analyze the process of information flow, identify and address critical paths of essential supplies associated with maternal health in the Ugandan health system. METHODS: We apply system engineering principles and work with community partners in hospitals to develop care process workflow charts (based on essential services) for the lifecycle of maternal health continuum of care. Based on this chart, we develop a cloud-based offline-compatible smart sync platform named "E+TRA Health" to triangulate (1) patient admission, diagnoses, delivery information, testing reports from laboratories, (2) inventory information from main store, stores in MCH unit, and (3) lab, to identify the critical list of medical and laboratory supplies, their lead times for procurement and then generate reports and suggested procurement plans for real time decision-making. RESULTS: The E+TRA Health platform was piloted in two Healthcare Center IV facilities in Uganda over a period of 6 months. The system collected more than 5000 patient records and managed more than 500 types of medicines. The pilot study demonstrated the functionalities of E+TRA Health and its feasibility to sense demand from point of care. CONCLUSION: E+TRA Health is the first to triangulate supply and demand data from three different departments (main store, lab, and MCH) to forecast and generate orders automatically to meet patient demands. It is capable of generating reports required by Ministry of Health in real time compared to one-week lead-time using paper-based systems. This prompts frontline stakeholders to generate efficient, reliable and sustainable strategic healthcare plans with real time data. This system improves patient outcomes through better commodity availability by sensing true patient demands.

Uncovering Discrepancies in IV Vancomycin Infusion Records between Pump Logs and EHR Documentation.

BACKGROUND: Infusion start time, completion time, and interruptions are the key data points needed in both area under the concentration-time curve (AUC)- and trough-based vancomycin therapeutic drug monitoring (TDM). However, little is known about the accuracy of documented times of drug infusions compared with automated recorded events in the infusion pump system. A traditional approach of direct observations of infusion practice is resource intensive and impractical to scale. We need a new methodology to leverage the infusion pump event logs to understand the prevalence of timestamp discrepancies as documented in the electronic health records (EHRs). OBJECTIVES: We aimed to analyze timestamp discrepancies between EHR documentation (the information used for clinical decision making) and pump event logs (actual administration process) for vancomycin treatment as it may lead to suboptimal data used for therapeutic decisions. METHODS: We used process mining to study the conformance between pump event logs and EHR data for a single hospital in the United States from July to December 2016. An algorithm was developed to link records belonging to the same infusions. We analyzed discrepancies in infusion start time, completion time, and interruptions. RESULTS: Of the 1,858 infusions, 19.1% had infusion start time discrepancy more than ± 10 minutes. Of the 487 infusion interruptions, 2.5% lasted for more than 20 minutes before the infusion resumed. 24.2% (312 of 1,287) of 1-hour infusions and 32% (114 of 359) of 2-hour infusions had over 10-minute completion time discrepancy. We believe those discrepancies are inherent part of the current EHR documentation process commonly found in hospitals, not unique to the care facility under study. CONCLUSION: We demonstrated pump event logs and EHR data can be utilized to study time discrepancies in infusion administration at scale. Such discrepancy should be further investigated at different hospitals to address the prevalence of the problem and improvement effort.

The pneumonia severity index: Assessment and comparison to popular machine learning classifiers.

INTRODUCTION: Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size. METHODS: In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34,720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC). RESULTS: Machine learning classifiers, such as Random Forest, provided a statistically highly(p < 0.001) significant improvement (∼33% in PR AUC and ∼6% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI). DISCUSSION: Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight.

Physical and information workflow mapping of vancomycin therapeutic drug management: A single site case study revealing potential gaps in the process.

Vancomycin is one of the most prescribed antibiotics in pediatric intensive care units (PICU) in US hospitals. However, a detailed understanding of workflow and information flow among various stakeholders regarding vancomycin treatment processes in clinical settings is lacking. We conducted direct observations and informant interviews to develop the mapping of key processes and information flow for vancomycin treatment, with an emphasis on therapeutic drug monitoring (TDM) dose adjustment decision-making. A health information technology (HIT) sociotechnical framework was used to identify EHR related safety concerns. A total of 27 vancomycin treatment activities were observed over a 60-h duration including infusion administration, infusion completion, trough concentration blood draw and therapeutic decision making processes. Workflow and information flow mappings revealed (1) deviations between the documented timestamp used for TDM decision making and the actual time the tasks executed and (2) the lack of information flow regarding infusion completion and interruption. Missing features, insufficient usability and lack of integration with workflow and communication in the EHR were deemed safety gaps that may affect the accuracy of therapeutic decisions. Our case study identified gaps in information flow among clinical team members via EHR in TDM processes to provide insights for the improvement of the EHR system for antibiotic treatment purposes. In particular, the potential harm of the missing, uncertain, and inaccurate documented TDM task times warrant further investigations.

Assessing the impact of Indiana legislation on opioid-based doctor shopping among Medicaid-enrolled pregnant women: a regression analysis.

BACKGROUND: States have passed various legislative acts in an attempt to reduce opioid prescribing and corresponding doctor shopping, including prescription drug monitoring programs. This study seeks to determine the association between two state-based interventions enacted in Indiana and the level of doctor shopping among Medicaid-enrolled pregnant women. METHODS: Indiana Medicaid claims data over the period of January 2014 to March 2019 were used in a regression model to determine the longitudinal change in percentage of pregnant women engaged in doctor shopping based on passage of Indiana Administrative Code Title 884 in 2014 and Public Law 194 in 2018. The primary reasons for prescribing were also identified. RESULTS: There were 37,451 women that had both pregnancy and prescription opioid claims over the time horizon. Of these, 2130 women met the criteria for doctor shopping. Doctor shopping continued to increase over the time between the passage of the two interventions but decreased after passage of Public Law 194. CONCLUSION: The decrease in doctor shopping among Medicaid-enrolled pregnant women after passage of Public Law 194 points to the importance of addressing this issue across a broad set of healthcare professionals including nurse practitioners and physician assistants. It is also possible that the potential punitive component in the Law for non-compliance played a role.

Quantifying the Impact of Acute Stroke System of Care Transfer Protocols on Patient Outcomes.

BACKGROUND: We quantify the impact of implementing a stroke system of care requiring transport of individuals believed to have stroke to a primary stroke center, in rural and urban settings, based on time from symptom recognition to treatment, probability of receiving treatment within 3 hours of stroke onset, and probability of overcrowding. We use Indiana as an example. METHODS: We used discrete-event simulation to estimate outcomes for 2 scenarios: stroke system of care with enabling technology (mobile stroke unit, stroke team expansion) and stroke system of care with no enabling technology, as compared with the status quo. We considered patient flow from symptom recognition to treatment. Patient locations and stroke events were generated for the 92 Indiana counties in Indiana, subdivided into 1009 locations. We considered time from emergency medical service (EMS) arrival at onset to treatment, probability of tissue plasminogen activator administered within 3 h of onset, and percentage of patients admitted beyond the occupancy level at the comprehensive stroke center. RESULTS: Results varied by urbanicity. Under no enabling technology, having a stroke system of care improved outcomes for individuals in urban and suburban settings. However, in rural settings, the implementation of stroke system of care guidelines decreased the average rate of treatment within 3 h of stroke onset and increased the EMS arrival to treatment times compared with sending the individual to the closest provider. Enabling technologies improved outcomes regardless of setting. DISCUSSION: Geographic disparities tend to increase the number of transfers, decrease the rate of treatment within 3 h of onset, and increase transit time. This could be overcome through federal and state initiatives to reduce quality gaps in stroke care in rural settings and promote care with dedicated stroke wards.

Healthcare Professionals Risk Assessments for Alert Overrides in High-Risk IV Infusions Using Simulated Scenarios.

This study aimed to use healthcare professionals' assessments to calculate expected risk of intravenous (IV) infusion harm for simulated high-risk medications that exceed soft limits and to investigate the impact of relevant risk factors. We designed 30 infusion scenarios for four high-risk medications, propofol, morphine, insulin, and heparin, infused in adult intensive care unit (AICU) and adult medical and surgical care unit (AMSU). A total of 20 pharmacists and 5 nurses provided their assessed expected risk of harm in each scenario. Descriptive statistics, analysis of variance with least square mean, and post hoc test were conducted to test the effects of field limit type, soft (SoftMax), and hard maximum drug limit types (HardMax), and care area-medication combination on risk of harm. The results showed that overdosing scenarios with continuous and bolus dose limit types were assessed with significantly higher risks than those of bolus dose rate type. An overdose infusion in AICU over a large SoftMax was assessed to be of higher risk than over a small one, but not in AMSU. For overdose infusions with three levels of drug amount, greater drug amount in AICU and AMSU was assessed to have higher risk, except insignificant risk difference between the infusions with higher and moderate drug amount in AMSU. This study obtained expected risk for simulated high-risk IV infusions and found that different field limit and SoftMax types can affect expected risk based on healthcare professionals' perspectives. The findings will be regarded as benchmarks for validating risk quantification models in future research.

Stakeholder perceptions of smart infusion pumps and drug library updates: A multisite, interdisciplinary study.

PURPOSE: Results of a questionnaire-based study to evaluate smart infusion pump end users' perceptions and understanding of the drug library update process are reported. METHODS: The Indianapolis Coalition for Patient Safety, Inc., in partnership with the Regenstrief Center for Healthcare Engineering, conducted a 33-item electronic, cross-sectional survey across 5 Indiana health systems from May through November 2017. Interdisciplinary participants identified for survey distribution included nurses, pharmacists, biomedical engineers, administrators, and medication safety officers. The survey assessed the following domains: patient safety, the drug library update process, knowledge of drug libraries and the update process, and end-user perceptions. RESULTS: A total of 778 submitted surveys were included in the data analysis, with a large majority of responses (90.2%) provided by nurses. The use of drug libraries for ensuring patient safety was deemed extremely important or important by 88% of respondents, but 36% indicated that they were unsure of whether drug libraries are updated on a routine basis in their health system. Approximately two-thirds agreed that the current update process improves quality of care (65.0%) and patient safety (68.1%). Moreover, 53.3% agreed that the current drug library update process was effective. However, less than 10% responded correctly when asked about the steps required to update the drug library. Furthermore, only 18% correctly indicated that when a pump is on it may not necessarily contain the most up-to-date version of the drug library. CONCLUSION: A survey of 5 health systems in Indianapolis identified several end-user knowledge gaps related to smart pump drug library updates. The results suggest that these gaps were most likely due to a combination of the 2-step update process and the fact that the current drug library version is not easy to find and/or user-friendly and it is unclear when an update is pending.

Tracking the Progress of Wireless Infusion Pump Drug Library Updates- A Data-Driven Analysis of Pump Update Delays.

Modern smart infusion pumps are wirelessly connected to a network server for easy data communications. The two-way communication allows uploading of infusion data and downloading of drug library updates. We have discovered significant delays in library updates. This research aimed at studying the drug library update process of one vendor pump and the contributing factors of pump update delays. Our data included BD Alaris™ pump status and infusion reports of two hospital systems (92 and 80 days, respectively, in 2015). We analyzed drug library update progressions at the individual device and fleet levels. To complete a library update, a pump goes through two status transitions: from noncurrent to a new library pending, and from pending to current. On average it took five to nine days for 50% of a pump fleet to become current after a new drug library was disseminated. We confirmed factors that affect noncurrent-to-pending time to include time to first power-on and total power-on time. We also found that high pump utilization promotes shorter pending-to-current time. Two distinctive and important steps of a drug library update on Alaris™ pumps are pending a new library and completing the library installation. To avoid potential patient harm caused by infusion pumps without appropriate drug limits due to update delays, hospitals should monitor the progression of a drug library update on its pump fleet. Potential ways to improve drug library updates on a fleet of pumps include better technologies, improved pump user-interface design, and more staff training.

Unintended Patient Safety Risks Due to Wireless Smart Infusion Pump Library Update Delays.

OBJECTIVE: Our previous study showed that the issue of drug library update delays on wireless intravenous (IV) infusion pumps of one major vendor was widespread and significant. However, the impact of such a delay was unclear. The objective of this study was to quantify the impact of pump library update delays on patient safety in terms of missed and false infusion programming alerts. METHODS: The study data sets included infusion logs and drug libraries from three hospitals of one health system from January 2015 to December 2016. We identified limit setting changes of any two consecutive drug library versions. We quantified the impact of using outdated drug limit settings by missed and false infusion programming alerts. RESULTS: Twenty-five updates of the drug library were released within the health system during the 2-year period with an average interval of 28.8 days. After a new library version was issued, it took at least 6 days for 50% of all pumps to become up-to-date and 15 days or more to reach 80%. All three hospitals had at least 16% of all IV infusions programmed with outdated libraries. This resulted in 18%, 24.4%, and 27% of false alerts in the three hospitals, respectively. We identified two cases of missed alert infusions of high-risk medications, propofol, and potassium chloride, which could have negatively impacted patient safety. CONCLUSIONS: These findings support our assumption that potential serious harm can happen when IV infusions are administered with outdated drug limit settings due to delays in drug library updates on the pump.

Life Cycle Assessment of Neodymium-Iron-Boron Magnet-to-Magnet Recycling for Electric Vehicle Motors.

Neodymium-iron-boron (NdFeB) magnets offer the strongest magnetic field per unit volume, and thus, are widely used in clean energy applications such as electric vehicle motors. However, rare earth elements (REEs), which are the key materials for creating NdFeB magnets, have been subject to significant supply uncertainty in the past decade. NdFeB magnet-to-magnet recycling has recently emerged as a promising strategy to mitigate this supply risk. This paper assesses the environmental footprint of NdFeB magnet-to-magnet recycling by directly measuring the environmental inputs and outputs from relevant industries and compares the results with production from "virgin" materials, using life cycle assessments. It was found that magnet-to-magnet recycling lowers environmental impacts by 64-96%, depending on the specific impact categories under investigation. With magnet-to-magnet recycling, key processes that contribute 77-95% of the total impacts were identified to be (1) hydrogen mixing and milling (13-52%), (2) sintering and annealing (6-24%), and (3) electroplating (6-75%). The inputs from industrial sphere that play key roles in creating these impacts were electricity (24-93% of the total impact) and nickel (5-75%) for coating. Therefore, alternative energy sources such as wind and hydroelectric power are suggested to further reduce the overall environmental footprint of NdFeB magnet-to-magnet recycling.

Impact of an Onsite Clinic on Utilization of Preventive Services.

OBJECTIVE: To assess impact of an onsite clinic on healthcare utilization of preventive services for employees of a public university and their dependents. METHOD: Descriptive statistics, logistic regression and classification tree techniques were used to assess health claim data to identify changes in patterns of healthcare utilization and factors impacting usage of onsite clinic. RESULT: Utilization of preventive services significantly increased for women and men employees by 9% and 14% one year after implementation of the onsite clinic. Hourly-paid employees, employees without diabetes, employees with spouse opt out or no coverage were more likely to go to the onsite clinic. CONCLUSION: Adapted framework for assessing performance of onsite clinics based on usage of health informatics would help to identify health utilization patterns and interaction between onsite clinic and offsite health providers.

Drivers and Barriers for Adopting Accreditation at Local Health Departments for Their Performance Improvement Effort.

CONTEXT: A national system of voluntary public health accreditation for state, local, and tribal health departments (local health departments [LHDs]) is part of a movement that aims to improve public health performance with ultimate impact on population health outcomes. Indiana is a good setting for the study of LHD accreditation adoption because several LHDs reported de-adopting accreditation in a recent statewide survey and because 71% of Indiana counties serve populations of 50 000 or less. DESIGN: A systematic method of analyzing qualitative data based on the Performance Improvement Model framework to expand our understanding of de-adoption of public health accreditation. SETTING/PARTICIPANTS: In 2015, we conducted a key informant interview study of the 3 LHDs that decided to delay their engagement in the accreditation based on findings from an Indiana survey on LHD accreditation adoption. The study is an exploration of LHD accreditation de-adoption and of the contributions made to its understanding by the Performance Improvement Model. RESULT: The study found that top management team members are those who champion accreditation adoption, and that organizational structure and culture facilitate the staff's embracing of the change. The Performance Improvement Model was found to enhance the elucidation of the inner domain elements of Consolidated Framework for Implementation Research in the context of de-adoption of public health accreditation. CONCLUSION: Governing entities' policies and priorities appear to mediate whether the LHDs are able to continue accreditation pursuit. Lacking any of these driving forces appears to be associated with decisions to de-adoption of accreditation. Further work is necessary to discern specific elements mediating decisions to pursue accreditation. This study demonstrates the added knowledge of Performance Improvement Model (PIM) to the CFIR framework. A large scale study is called to further clarify and discern supports of specific to the needs of individual LHDs for their performance improvement effort.

Cost comparison between home telemonitoring and usual care of older adults: a randomized trial (Tele-ERA).

BACKGROUND: From 1992 to 2008, older adults in the United States incurred more healthcare expense per capita than any other age group. Home telemonitoring has emerged as a potential solution to reduce these costs, but evidence is mixed. The primary aim of the study was to evaluate whether the mean difference in total direct medical cost consequence between older adults receiving additional home telemonitoring care (TELE) (n=102) and those receiving usual medical care (UC) (n=103) were significant. Inpatient, outpatient, emergency department, decedents, survivors, and 30-day readmission costs were evaluated as secondary aim. MATERIALS AND METHODS: Multivariate generalized linear models (GLMs) and parametric bootstrapping method were used to model cost and to determine significance of the cost differences. We also compared the differences in arithmetic mean costs. RESULTS: From the conditional GLMs, the estimated mean cost differences (TELE versus UC) for total, inpatient, outpatient, and ED were -$9,537 (p=0.068), -$8,482 (p =0.098), -$1,160 (p=0.177), and $106 (p=0.619), respectively. Mean postenrollment cost was 11% lower than the prior year for TELE versus 22% higher for UC. The ratio of mean cost for decedents to survivors was 2.1:1 (TELE) versus 12.7:1 (UC). CONCLUSIONS: There were no significant differences in the mean total cost between the two treatment groups. The TELE group had less variability in cost of care, lower decedents to survivors cost ratio, and lower total 30-day readmission cost than the UC group.

A predictive model of longitudinal, patient-specific colonoscopy results.

We suggest a model framework, in which an individual patient's risk for colonic neoplasia varies based on findings from his previous colonoscopies, to predict longitudinal colonoscopy results. The neoplasia natural history model describes progression through four neoplasia development states with patient age. Multiple natural history model parameter sets are assumed to act concurrently on the colon and parameter set prevalence combinations, whose a priori likelihoods are a function of patient sex, provide a basis set for patient-level predictions. The novelty in this approach is that after a colonoscopy, both the parameter set combination likelihoods and their model predictions can adjust in a Bayesian manner based on the results and conditions of the colonoscopy. The adjustment of model predictions operationalizes the clinical knowledge that multiple or advanced neoplasia at baseline colonoscopy is an independent predictor of multiple or advanced neoplasia at follow-up colonoscopy--and vice versa for negative colonoscopies--and the adjustment of parameter set combination likelihoods accounts for the possibility that patients may have different neoplasia development rates. A model that accurately captures serial colonoscopy results could potentially be used to design and evaluate post-colonoscopy treatment strategies based on the risk of individual patients. To support model identification, observational longitudinal colonoscopy results, procedure details, and patient characteristics were collected for 4084 patients. We found that at least two parameter sets specific to each sex with model adjustments was required to capture the longitudinal colonoscopy data and inclusion of multiple possible parameter set combinations, which account for random variations within the population, was necessary to accurately predict the second-time colonoscopy findings for patients with a history of advanced adenomas. Application of this model to predict CRC risks for patients adhering to guideline recommended follow-up colonoscopy intervals found that there are significant differences in risk with patient age, gender, and preparation quality and demonstrates the need for a more rigorous investigation into these recommendations.

Category-specific uncertainty modeling in clinical laboratory measurement processes.

BACKGROUND: A statement of measurement uncertainty describes the quality of a clinical assay analysis result, and uncertainty models of clinical assays can be used to evaluate and optimize laboratory protocols designed to minimize the measurement uncertainty associated with an assay. In this study, we propose a methodology to lend systematic structure to the uncertainty modeling process. METHODS: Clinical laboratory assays are typically classified based on the chemical reaction involved, and therefore, based on the assay analysis methodology. We use this fact to demonstrate that uncertainty models for assays within the same category are structurally identical in all respects except for the values of certain model parameters. This is accomplished by building uncertainty models for assays belonging to two categories--substrate assays based on optical absorbance analysis of endpoint reactions, and ion selective electrode (ISE) assays based on potentiometric measurements of electromotive force. RESULTS: Uncertainty models for the substrate assays and the ISE assays are built, and for each category, a general mathematical framework for the uncertainty model is developed. The parameters of the general framework that vary from assay to assay for each category are identified and listed. CONCLUSIONS: Estimates of measurement uncertainty from the models were compared with estimates of uncertainty from quality control data from the clinical laboratory. We demonstrate that building a general modeling framework for each assay category and plugging in parameter values for each assay is sufficient to generate uncertainty models for an assay within a given category.

Effects of home telemonitoring on transitions between frailty states and death for older adults: a randomized controlled trial.

BACKGROUND: Two primary objectives when caring for older adults are to slow the decline to a worsened frailty state and to prevent disability. Telemedicine may be one method of improving care in this population. We conducted a secondary analysis of the Tele-ERA study to evaluate the effect of home telemonitoring in reducing the rate of deterioration into a frailty state and death in older adults with comorbid health problems. METHODS: This trial involved 205 adults over the age of 60 years with a high risk of hospitalization and emergency department visits. For 12 months, the intervention group received usual medical care and telemonitoring case management, and the control group received usual care alone. The primary outcome was frailty, which was based on five criteria, ie, weight loss, weakness, exhaustion, low activity, and slow gait speed. Participants were classified as frail if they met three or more criteria; prefrail if they met 1-2 criteria; and not frail if they met no criteria. Both groups were assessed for frailty at baseline, and at 6 and 12 months. Frailty transition analyses were performed using a multiple logistic regression method. Kaplan-Meier and Cox proportional hazards methods were used to evaluate each frailty criteria for mortality and to compute unadjusted hazard ratios associated with being telemonitored, respectively. A retrospective power analysis was computed. RESULTS: During the first 6 months, 19 (25%) telemonitoring participants declined in frailty status or died, compared with 17 (19%) in usual care (odds ratio 1.41, 95% confidence interval [CI] 0.65-3.06, P = 0.38). In the subsequent 6 months, there was no transition to a frailty state, but seven (7%) participants from the telemonitoring and one (1%) from usual care group died (odds ratio 5.94, 95% CI 0.52-68.48, P = 0.15). Gait speed (hazards ratio 3.49, 95% CI 1.42-8.58) and low activity (hazards ratio 3.10, 95% CI 1.25-7.71) were shown to predict mortality. CONCLUSION: This study did not provide sufficient evidence to show that the telemonitoring group did better than usual care in reducing the decline of frailty states and death. Transitions occurred primarily in the first 6 months.

Application of mathematical models of system uncertainty to evaluate the utility of assay calibration protocols.

BACKGROUND: Laboratory protocols used to calibrate commercial clinical chemistry systems affect test result quality. Mathematical models of system uncertainty can be developed using performance parameters provided by the manufacturer for various subsystems. These models can be used to evaluate protocols for specific laboratory operations. METHODS: A mathematical model was developed to estimate the uncertainty inherent in the Roche Diagnostics P-Modular system, and included uncertainties associated with the sample and reagent pipettes, spectrometer and the calibration process. The model was then used to evaluate various alternate calibration protocols: calibration based on mean of replicate measurements (n=1-6) and calibration based on conditional acceptance when the following quality control specimen was within one standard deviation of target. The effect of calibrator concentrations on assay measurement uncertainty was also studied, and calibrator concentrations that minimize uncertainty at a specific concentration were identified. RESULTS: The simulation model produced uncertainty estimates of 3.5% for the serum cholesterol assay and identified sample pipette (40%) and spectrometer (21%) as the largest contributors to measurement uncertainty. Each additional replicate calibrator measurements result in diminishing reductions in measurement uncertainty, with maximum reductions (19%) achieved with five replicate measurements. The conditional acceptance of calibration only when the control was within 1s resulted in an 18% reduction. CONCLUSIONS: The model can be used to evaluate the utility of laboratory protocols and establish realistic assay performance targets. The model also can help instrument manufacturers and laboratorians identify major contributors to assay measurement uncertainty, which helps improve performance in future assay systems.