Modern Electronic Techniques Applied to Physics and Engineering

Contains reports on three research projects

Improving treatment plan evaluation with automation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135377/1/acm20016.pd

Prescription Opioid Use Patterns, Use Disorder Diagnoses, and Addiction Treatment Receipt after the 2014 Medicaid Expansion in Oregon

Background/Aims: Evidence suggests Medicaid beneficiaries in the USA are prescribed opioids more frequently than are people who are privately‐insured, but little is known about opioid prescribing patterns among Medicaid enrollees who gained coverage via the Affordable Care Act Medicaid expansions. This study compared the prevalence of receipt of opioid prescriptions and opioid‐use‐disorder (OUD), along with time from OUD diagnosis to medication‐assisted treatment (MAT) receipt between Oregon residents who had been continuously insured by Medicaid, were newly insured after Medicaid expansion in 2014, or returned to Medicaid coverage after expansion. Design: Cross‐sectional study using inverse‐propensity weights to adjust for differences among insurance groups. Setting: Oregon. Participants: 225,295 Oregon Medicaid adult beneficiaries insured 2014‐2015 and either: 1) newly enrolled, 2) returning in 2014 after a \u3e 12‐month gap, or 3) continuously insured between 2013 and 2015. We excluded patients in hospice care or with cancer diagnoses. Measurements: Any opioid dispensed, chronic (≥90‐day) and high dose (≥ 90 daily morphine milligram equivalence) opioid use, documented OUD diagnosis, and MAT receipt. Findings: Compared with the continuously insured, newly and returning insured enrollees were less likely to be dispensed opioids [newly: 42.3%, 95% confidence interval (95%CI) 42.0‐42.7%; returning: 49.3%, 95%CI 48.8‐49.7%; continuously: 52.5%, 95%CI 52.0‐53.0%], use opioids chronically (newly: 12.8%, 95%CI 12.4‐13.1%; returning: 11.9%, 95%CI 11.5‐12.3%, continuously: 15.8%, 95%CI 15.4‐16.2%), have OUD diagnoses (newly: 3.6%, 95%CI 3.4‐3.7%; returning: 3.9%, 95%CI 3.8‐4.1%, continuously: 4.7%, 95%CI 4.5‐4.9%), and receive MAT after OUD diagnosis [Hazard Ratio newly: 0.57, 95%CI 0.53‐0.61; Hazard Ratio returning: 0.60, 95%CI 0.56‐0.65 (REF: continuously)]. Conclusions: Residents of Oregon, USA who enrolled or re‐enrolled in Medicaid health insurance after expansion of coverage in 2014 as a result of the Affordable Care Act were less likely than those already covered to receive opioids, use them chronically, or receive medication‐assisted treatment for opioid use disorder

TrackMate: An open and extensible platform for single-particle tracking

International audienceWe present TrackMate, an open source Fiji plugin for the automated, semi-automated, and manual tracking of single-particles. It offers a versatile and modular solution that works out of the box for end users, through a simple and intuitive user interface. It is also easily scriptable and adaptable, operating equally well on 1D over time, 2D over time, 3D over time, or other single and multi-channel image variants. TrackMate provides several visualization and analysis tools that aid in assessing the relevance of results. The utility of TrackMate is further enhanced through its ability to be readily customized to meet specific tracking problems. TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment. This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces , including visualization, analysis and exporting tools. The current capabilities of TrackMate are presented in the context of three different biological problems. First, we perform Caenorhabditis-elegans lineage analysis to assess how light-induced damage during imaging impairs its early development. Our TrackMate-based lineage analysis indicates the lack of a cell-specific light-sensitive mechanism. Second, we investigate the recruitment of NEMO (NF-jB essential modulator) clusters in fibroblasts after stimulation by the cytokine IL-1 and show that photodamage can generate artifacts in the shape of TrackMate characterized movements that confuse motility analysis. Finally, we validate the use of TrackMate for quantitative lifetime analysis of clathrin-mediated endocy-tosis in plant cells

Concordance in caregiver and child sleep health metrics among families experiencing socioeconomic disadvantage: A pilot study

Purpose: Child and caregiver sleep occurs in a family system, with socioeconomically disadvantaged families experiencing disproportionately worse sleep health than more advantaged families. The extent to which objectively measured sleep health metrics (i.e., sleep duration, midpoint, regularity, efficiency) are concordant within disadvantaged family systems, including caregiver-child dyads, is not clear. To address this gap, this study aimed to: (1) characterize sleep health metrics and (2) identify levels of sleep health concordance among caregiver-child dyads living in families experiencing socioeconomic disadvantage. Design and methods: We enrolled 20 caregivers and 26 children in this micro-longitudinal study. Eligible primary caregivers slept in the same house as the child ≥4 nights/week and had no sleep disorders. Eligible children were aged 6-14 years and reported no medical problems. Dyads wore an actigraphy device continuously for 14 consecutive days. Sleep duration, bedtime, midpoint, and efficiency were estimated, and concordance evaluated using linear mixed modeling (R v.3.5.2). Results: Most caregivers were female (85%), Non-Hispanic Black (80%), and aged 40.45 years (SD=11.82). On average, caregivers were not meeting national recommendations for sleep duration and efficiency. Similarly, sleep duration recommendations were not met by child participants. Bivariate results showed that bedtime =0.19, p\u3c.001), sleep efficiency (=0.24, p\u3c.001), and sleep midpoint (=0.39, p\u3c.001), were concordant between child and caregiver. Multivariable models showed that caregiver bedtime was predictive of child sleep midpoint (b=0.16, p\u3c.05), and caregiver sleep midpoint was predictive of child bedtime (b=0.29, p\u3c.01) and child sleep midpoint (b=0.31, p\u3c.001). Conclusion: Objectively estimated caregiver sleep may be connected to the sleep timing of their children. Improving child sleep may require addressing caregiver sleep habits too. Practice Implications: Results highlight the importance of providers considering caregiver sleep health when assessing child sleep health during well child visits

Medicaid coverage accuracy in electronic health records

Health insurance coverage facilitates access to preventive screenings and other essential health care services, and is linked to improved health outcomes; therefore, it is critical to understand how well coverage information is documented in the electronic health record (EHR) and which characteristics are associated with accurate documentation. Our objective was to evaluate the validity of EHR data for monitoring longitudinal Medicaid coverage and assess variation by patient demographics, visit types, and clinic characteristics. We conducted a retrospective, observational study comparing Medicaid status agreement between Oregon community health center EHR data linked at the patient-level to Medicaid enrollment data (gold standard). We included adult patients with a Medicaid identification number and ≥1 clinic visit between 1/1/2013–12/31/2014 [>1 million visits (n = 135,514 patients)]. We estimated statistical correspondence between EHR and Medicaid data at each visit (visit-level) and for different insurance cohorts over time (patient-level). Data were collected in 2016 and analyzed 2017–2018. We observed excellent agreement between EHR and Medicaid data for health insurance information: kappa (>0.80), sensitivity (>0.80), and specificity (>0.85). Several characteristics were associated with agreement; at the visit-level, agreement was lower for patients who preferred a non-English language and for visits missing income information. At the patient-level, agreement was lower for black patients and higher for older patients seen in primary care community health centers. Community health center EHR data are a valid source of Medicaid coverage information. Agreement varied with several characteristics, something researchers and clinic staff should consider when using health insurance information from EHR data. Keywords: Electronic health records, Medicaid, Health policy, Health insuranc

Electronic health record tools to assist with children’s insurance coverage: a mixed methods study

Abstract Background Children with health insurance have increased access to healthcare and receive higher quality care. However, despite recent initiatives expanding children’s coverage, many remain uninsured. New technologies present opportunities for helping clinics provide enrollment support for patients. We developed and tested electronic health record (EHR)-based tools to help clinics provide children’s insurance assistance. Methods We used mixed methods to understand tool adoption, and to assess impact of tool use on insurance coverage, healthcare utilization, and receipt of recommended care. We conducted intent-to-treat (ITT) analyses comparing pediatric patients in 4 intervention clinics (n = 15,024) to those at 4 matched control clinics (n = 12,227). We conducted effect-of-treatment-on-the-treated (ETOT) analyses comparing intervention clinic patients with tool use (n = 2240) to intervention clinic patients without tool use (n = 12,784). Results Tools were used for only 15% of eligible patients. Qualitative data indicated that tool adoption was limited by: (1) concurrent initiatives that duplicated the work associated with the tools, and (2) inability to obtain accurate insurance coverage data and end dates. The ITT analyses showed that intervention clinic patients had higher odds of gaining insurance coverage (adjusted odds ratio [aOR] = 1.32, 95% confidence interval [95%CI] 1.14–1.51) and lower odds of losing coverage (aOR = 0.77, 95%CI 0.68–0.88), compared to control clinic patients. Similarly, ETOT findings showed that intervention clinic patients with tool use had higher odds of gaining insurance (aOR = 1.83, 95%CI 1.64–2.04) and lower odds of losing coverage (aOR = 0.70, 95%CI 0.53–0.91), compared to patients without tool use. The ETOT analyses also showed higher rates of receipt of return visits, well-child visits, and several immunizations among patients for whom the tools were used. Conclusions This pragmatic trial, the first to evaluate EHR-based insurance assistance tools, suggests that it is feasible to create and implement tools that help clinics provide insurance enrollment support to pediatric patients. While ITT findings were limited by low rates of tool use, ITT and ETOT findings suggest tool use was associated with better odds of gaining and keeping coverage. Further, ETOT findings suggest that use of such tools may positively impact healthcare utilization and quality of pediatric care. Trial registration ClinicalTrials.gov, NCT02298361; retrospectively registered on November 5, 2014