E-DPNCT: an enhanced attack resilient differential privacy model for smart grids using split noise cancellation.

High frequency reporting of energy consumption data in smart grids can be used to infer sensitive information regarding the consumer's life style and poses serious security and privacy threats. Differential privacy (DP) based privacy models for smart grids ensure privacy when analysing energy consumption data for billing and load monitoring. However, DP models for smart grids are vulnerable to collusion attack where an adversary colludes with malicious smart meters and un-trusted aggregator in order to get private information from other smart meters. We first show the vulnerability of DP based privacy model for smart grids against collusion attacks to establish the need of a collusion resistant privacy model. Then, we propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) which not only provides resistance against collusion attacks but also protects the privacy of the smart grid data while providing accurate billing and load monitoring. We use differential privacy with a split noise cancellation protocol with multiple master smart meters (MSMs) to achieve collusion resistance. We propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) to protect the privacy of the smart grid data using a split noise cancellation protocol with multiple master smart meters (MSMs) to provide accurate billing and load monitoring and resistance against collusion attacks. We did extensive comparison of our E-DPNCT model with state of the art attack resistant privacy preserving models such as EPIC for collusion attack. We simulate our E-DPNCT model with real time data which shows significant improvement in privacy attack scenarios. Further, we analyze the impact of selecting different sensitivity parameters for calibrating DP noise over the privacy of customer electricity profile and accuracy of electricity data aggregation such as load monitoring and billing.

Differential Impact of a Plan-Led Standardized Complex Care Management Intervention on Subgroups of High-Cost High-Need Medicaid Patients.

Interventions to better coordinate care for high-need high-cost (HNHC) Medicaid patients frequently fail to demonstrate changes in hospitalizations or emergency department (ED) use. Many of these interventions are modeled after practice-level complex care management (CCM) programs. The authors hypothesized that a national CCM program may be effective for some subgroups of HNHC patients, and the overall null effect may obfuscate subgroup-level impact. They used a previously published typology defining 6 subgroups of high-cost Medicaid patients and evaluated program impact by subgroup. The analysis used an individual-level interrupted time series with a comparison group. Intervention subjects were high-cost adult Medicaid patients who enrolled in 1 of 2 national CCM programs implemented by UnitedHealthcare (UHC) (n = 39,687). The comparators were patients who met CCM program criteria but were ineligible due to current enrollment in another UHC/Optum led program (N = 26,359). The intervention was a CCM program developed by UHC/Optum to provide "whole person care" delivering standardized interventions to address medical, behavioral, and social needs for HNHC Medicaid patients, and the outcome was probability of hospitalization or ED use in a given month, estimated at 12 months postenrollment. A reduction in risk of ED utilization for 4 of 6 subgroups was found. A reduction in risk of hospitalization for 1 of 6 subgroups was also found. The authors conclude that standardized health plan led CCM programs demonstrate effectiveness for certain subgroups of HNHC patients in Medicaid. This effectiveness is principally in reducing ED risk and may extend to the risk of hospitalization for a small number of patients.

FLDID: Federated Learning Enabled Deep Intrusion Detection in Smart Manufacturing Industries.

The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries. The detection of cyber threats against such extensive, complex, and heterogeneous smart manufacturing industries is very challenging due to the lack of sufficient attack traces. Therefore, in this work, a Federated Learning enabled Deep Intrusion Detection framework is proposed to detect cyber threats in smart manufacturing industries. The proposed FLDID framework allows multiple smart manufacturing industries to build a collaborative model to detect threats and overcome the limited attack example problem with individual industries. Moreover, to ensure the privacy of model gradients, Paillier-based encryption is used in communication between edge devices (representative of smart industries) and the server. The deep learning-based hybrid model, which consists of a Convolutional Neural Network, Long Short Term Memory, and Multi-Layer Perceptron is used in the intrusion detection model. An exhaustive set of experiments on the publically available dataset proves the effectiveness of the proposed framework for detecting cyber threats in smart industries over the state-of-the-art approaches.

Does the diabetes health plan have a differential impact on medication adherence among beneficiaries with fewer financial resources?

BACKGROUND: The Diabetes Health Plan (DHP), a value-based insurance plan that reduces cost sharing, was previously shown to modestly increase employer-level medication adherence. It is unclear how the DHP might impact individuals with different incomes. OBJECTIVE: To examine the impact of the DHP on individual-level medication adherence, by income level. METHODS: This is a retrospective, quasiexperimental study. An employer-level propensity score match was done to identify suitable control employers, followed by individual-level propensity score weighing. These weights were applied to difference-in-difference models examining the effect of the DHP and the effect of income on changes in adherence to metformin, statins, and angiotensin-converting enzymes/angiotensin receptor blockers. The weights were then applied to a differences-in-differences-in-differences model to estimate the differential impact of DHP status on changes in adherence by income group. RESULTS: The study population included 2,065 beneficiaries with DHP and 17,704 matched controls. There were no significant differences in changes to adherence for any medications between beneficiaries enrolled in the DHP vs standard plans. However, adherence to all medications was higher among those with incomes greater than $75,000 (year 1: metformin: +7.3 percentage points; statin +4.3 percentage points; angiotensin-converting enzymes/angiotensin receptor blockers: +6.2 percentage points; P < 0.01) compared with those with incomes less than $50,000. The differences-in-differences-in-differences term examining the impact of income on the DHP effect was not significant for any comparisons. CONCLUSIONS: We did not find significant associations between the DHP and changes in individual-level medication adherence, even for low-income beneficiaries. New strategies to improve consumer engagement may be needed to translate value-based insurance designs into changes in patient behavior. DISCLOSURES: Drs Ettner and Moin received grants from the Centers for Disease Control and Prevention and National Institute of Diabetes and Digestive and Kidney Diseases (Principal Investigator: Carol Mangione). Mr Luchs received support for attending meetings and/or travel (minimal-mileage and hotel on 2 occasions). Mr Chan has an employee benefit to purchase stock for UnitedHealth Group.

Blockchain-Based Digital Twins Collaboration for Smart Pandemic Alerting: Decentralized COVID-19 Pandemic Alerting Use Case.

Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.

High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program.

BACKGROUND: High-cost high-need patients are typically defined by risk or cost thresholds which aggregate clinically diverse subgroups into a single 'high-need high-cost' designation. Programs have had limited success in reducing utilization or improving quality of care for high-cost high-need Medicaid patients, which may be due to the underlying clinical heterogeneity of patients meeting high-cost high-need designations. METHODS: Our objective was to segment a population of high-cost high-need Medicaid patients (N = 676,161) eligible for a national complex case management program between January 2012 and May 2015 to disaggregate clinically diverse subgroups. Patients were eligible if they were in the top 5 % of annual spending among UnitedHealthcare Medicaid beneficiaries. We used k-means cluster analysis, identified clusters using an information-theoretic approach, and named clusters using the patients' pattern of acute and chronic conditions. We assessed one-year overall and preventable hospitalizations, overall and preventable emergency department (ED) visits, and cluster stability. RESULTS: Six clusters were identified which varied by utilization and stability. The characteristic condition patterns were: 1) pregnancy complications, 2) behavioral health, 3) relatively few conditions, 4) cardio-metabolic disease, and complex illness with relatively 5) low or 6) high resource use. The patients varied by cluster by average ED visits (2.3-11.3), hospitalizations (0.3-2.0), and cluster stability (32-91%). CONCLUSIONS: We concluded that disaggregating subgroups of high-cost high-need patients in a large multi-state Medicaid sample identified clinically distinct clusters of patients who may have unique clinical needs. Segmenting previously identified high-cost high-need populations thus may be a necessary strategy to improve the effectiveness of complex case management programs in Medicaid.

Evaluation of a National Care Coordination Program to Reduce Utilization Among High-cost, High-need Medicaid Beneficiaries With Diabetes.

BACKGROUND: Medical, behavioral, and social determinants of health are each associated with high levels of emergency department (ED) visits and hospitalizations. OBJECTIVE: The objective of this study was to evaluate a care coordination program designed to provide combined "whole-person care," integrating medical, behavioral, and social support for high-cost, high-need Medicaid beneficiaries by targeting access barriers and social determinants. RESEARCH DESIGN: Individual-level interrupted time series with a comparator group, using person-month as the unit of analysis. SUBJECTS: A total of 42,214 UnitedHealthcare Medicaid beneficiaries (194,834 person-months) age 21 years or above with diabetes, with Temporary Assistance to Needy Families, Medicaid expansion, Supplemental Security Income without Medicare, or dual Medicaid/Medicare. MEASURES: Our outcome measures were any hospitalizations and any ED visits in a given month. Covariates of interest included an indicator for intervention versus comparator group and indicator and spline variables measuring changes in an outcome's time trend after program enrollment. RESULTS: Overall, 6 of the 8 examined comparisons were not statistically significant. Among Supplemental Security Income beneficiaries, we observed a larger projected decrease in ED visit risk among the intervention sample versus the comparator sample at 12 months postenrollment (difference-in-difference: -6.6%; 95% confidence interval: -11.2%, -2.1%). Among expansion beneficiaries, we observed a greater decrease in hospitalization risk among the intervention sample versus the comparator sample at 12 months postenrollment (difference-in-difference: -5.8%; 95% confidence interval: -11.4%, -0.2%). CONCLUSION: A care coordination program designed to reduce utilization among high-cost, high-need Medicaid beneficiaries was associated with fewer ED visits and hospitalizations for patients with diabetes in selected Medicaid programs but not others.

Behavioral activation therapy for return to work in medication-responsive chronic depression with persistent psychosocial dysfunction.

OBJECTIVE: Chronic depression is associated with significant impairment in work functioning, relationships, and health. Such impairment often persists following medication-induced remission of depressive symptoms. We adapted and tested Behavioral Activation therapy with a goal of return to work (BA-W) in subjects with chronic depression who had responded to medication treatment but remained unemployed. METHOD: Sixteen adults aged 18-65 with DSM-IV diagnosed Dysthymic Disorder or chronic Major Depression were recruited from clinical trials taking place at the New York State Psychiatric Institute between 4/2009 and 12/2012 and enrolled in 12 weeks of individual manual-driven BA-W. Functioning was measured at intake, post-treatment and at 24 week follow-up. RESULTS: Eighty-seven percent (n=14) of subjects completed the full 12 weeks of BA-W. Hours of work related activity (p<.005, d=0.83), hours of paid work (p<.0003, d=0.54), and work productivity (p<.0004, d=-0.48) increased significantly over the study period. Earned income increased post-treatment (p=.068) with significant changes by 24 week follow-up (p=.011). Secondary outcomes including behavioral avoidance (p<.004, d=-0.56), and global functioning (p<.0003, d=1.42) were also significantly improved post-treatment. Effect sizes, including for outcomes with non-significant changes, were generally in the range of 0.5-0.8. CONCLUSIONS: This pilot study provides preliminary evidence of the efficacy of a work-targeted psychotherapy to remediate vocational impairment in subjects with chronic depression. Data suggests that further testing of BA-W using a randomized controlled trial is warranted and may represent a significant advance in treatment for the residual disability present after successful pharmacotherapy.

Combination antidepressant therapy for major depressive disorder: speed and probability of remission.

INTRODUCTION: Only about a third of patients with an episode of major depressive disorder remit with a given treatment and few remissions occur within the first weeks of treatment. This study tested whether combining escitalopram and bupropion as initial treatment would result in quicker remission and a higher remission rate than monotherapy with either drug. METHOD: Two hundred forty-five outpatients aged 18-65 having non-psychotic, non-bipolar major depression were randomly assigned to double-blind treatment with bupropion or escitalopram or the combination dosed to a maximum of bupropion 450 mg/d and/or escitalopram 40 mg/d for 12 weeks. A Montgomery-Asberg Depression Rating Scale score of 22 was required for randomization, while a Hamilton Rating Scale for Depression score ≤ 7 defined remission. We hypothesized that bupropion plus escitalopram would outperform both monotherapies in both earlier onset of remission and higher rate of remission. RESULTS: Primary analyses did not demonstrate that dual therapy outperformed both monotherapies in either timing of remission or remission rate. All three treatments were well tolerated. DISCUSSION: These results do not support initial use of bupropion plus escitalopram to speed or enhance antidepressant response. CLINICAL TRIALS REGISTRATION: NCT00519428.

Do manualized psychosocial interventions help reduce relapse among alcohol-dependent adults treated with naltrexone or placebo? A meta-analysis.

BACKGROUND: During the past decade, several novel medication treatments and psychosocial interventions have been tested. Overall, their impact on reducing alcohol use and preventing relapse has been modest. These outcomes have spurred researchers to investigate whether the addition of manualized psychosocial therapies with demonstrated efficacy to pharmacotherpy would have a synergistic effect. OBJECTIVES: We conducted a meta-analysis to test the hypothesis that the addition of manualized psychosocial therapies would reduce the frequency of relapse. RESULTS: This review analyzed data from six studies. Among studies that used random assignment to manually guided psychosocial interventions, the rates of relapse between the naltrexone plus a psychosocial intervention and naltrexone without a psychosocial intervention were very similar. Among patients allocated to placebo, similar rates of relapse were also found between Cognitive Behavioral Therapy (CBT) and non-CBT. In studies which used manualized psychotherapies as a platform, relapse rates were similar between naltrexone and placebo. In contrast, studies using Treatment As Usual psychotherapy as a platform demonstrated lower rates of relapse in the naltrexone group, compared to placebo group. CONCLUSIONS: We conclude that CBT does not appear to offer benefits beyond those derived from study medications. SCIENTIFIC SIGNIFICANCE: The benefit of the addition of CBT to medication in preventing relapse may be limited.

A randomized controlled trial of duloxetine versus placebo in the treatment of nonmajor chronic depression.

OBJECTIVE: Numerous double-blind studies have assessed the efficacy of antidepressants in treating chronic depressive disorder, including dysthymic disorder, low-grade chronic depression. However, there are no double-blind, placebo-controlled studies of serotonin-norepinephrine reuptake inhibitors in chronic depressive disorder. METHOD: Outpatients with chronic depressive disorder, but without concurrent major depressive disorder (MDD), were randomly assigned to prospective double-blind duloxetine (beginning at 30 mg/d, increased to a maximum dose of 120 mg/d) versus placebo for 10 weeks. Inclusion criteria were current DSM-IV-TR diagnosis of dysthymic disorder or depression not otherwise specified, age 18-75 years, and a Hamilton Depression Rating Scale (HDRS) score ≥ 12. Exclusion criteria included current major depression. The study was conducted between August 2006 and December 2011. HDRS, Cornell Dysthymia Rating Scale (CDRS), Clinical Global Impressions (CGI), Beck Depression Inventory (BDI), Global Assessment of Functioning (GAF), Social Adjustment Scale (SAS), and other assessments were administered at each visit. We hypothesized that duloxetine would be superior to placebo in (1) 24-item HDRS total score, (2) the percentage of subjects classified as responders and remitters, and (3) secondary measures (CDRS, BDI, CGI). Response was defined as > 50% decrease in 24-item HDRS and CGI-Improvement scale score of 1 or 2 ("very much improved" or "much improved"). Remission was defined as HDRS-17 item score ≤ 4 and 0 on item 1 of the HDRS (depressed mood). RESULTS: 65 subjects were enrolled, of whom 57 began medication. They ranged in age from 19 to 70 years (mean ± SD = 41.63 ± 11.22) and included 24 women and 33 men. Baseline 24-item HDRS score (mean ± SD) for both groups was 20.75 ± 4.92. After 10 weeks, duloxetine-treated subjects had significantly lower 24-item HDRS scores than placebo-treated subjects (time-by-drug group effect on analysis of variance: F1,55 = 9.43, P = .003). Responder and remitter analyses significantly favored duloxetine treatment. The response rate was 65.5% for duloxetine versus 25.0% for placebo (χ(2)(1) = 9.43, P = .003); and the remitter rate was 55.2% for duloxetine versus 14.3% for placebo (χ(2)(1) = 10.46, P = .002). After 10 weeks, duloxetine-treated subjects did not differ significantly better from placebo-treated subjects on the SAS (time-by-drug group effect on analysis of variance: F(1,46) = 0.35, P = .555) or on the GAF (time-by-drug group effect on analysis of variance: F(1,51) = .01, P = .922). CONCLUSIONS: Results on the 24-item HDRS, CGI, and CDRS suggest that duloxetine is efficacious in acute treatment of chronic nonmajor depressive disorder. Response and remission rates also differed significantly, favoring duloxetine treatment, but BDI, GAF, and social functioning (Social Adjustment Scale) did not. Duloxetine appears to be effective in acute treatment of nonmajor chronic depression. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT00360724.

Enhancing resident training in abortion and contraception through hospital-community partnership.

OBJECTIVES: The study was undertaken to assess the efficacy of a hospital-community rotation in abortion and contraception. STUDY DESIGN: The program was developed and implemented by the Planned Parenthood League of Massachusetts (PPLM) and the Department of Obstetrics and Gynecology at Baystate Medical Center in November 2000. Abortion and contraceptive training is integrated into postgraduate year 3 and includes didactic presentations and 10 mandatory clinical sessions in first trimester abortion care. RESULTS: Five residents complete the program annually. Before rotation, residents report their competence in first-trimester abortion procedure as 2 on a scale of 1 to 5 (1=not at all comfortable, 5=very comfortable). After rotation, the reported competency rating is 4.5 on the same scale. Residents, trainers, and staff report high satisfaction with the program. CONCLUSION: A collaborative hospital-community rotation in abortion and contraception increases residents' clinical competence in these areas and is highly acceptable to residents, faculty, and staff.

Does catheter type effect pregnancy rate in intrauterine insemination cycles?

PURPOSE: To determine if a soft insemination catheter increases pregnancy rates. METHODS: Seven hundred forty-seven consecutive intrauterine insemination (IUI) cycles in 364 women in a reproductive endocrinology office between October 1998 and March 2000. Patients with even birth years were inseminated with the Tomcat catheter, and those with odd birth year with the Wallace IUI catheter. Clinical pregnancy rates were compared. RESULTS: The Wallace catheter group included 180 women for 372 cycles and an overall pregnancy rate of 16.4%. The Tomcat catheter group included 184 women for 375 cycles and an overall pregnancy rate of 18.1%. This difference is not statistically significant (P = 0.61). Potential confounders were accounted for. CONCLUSIONS: When comparing the softer Wallace catheter to the less pliable Tomcat catheter during IUI cycles, there was no significant difference in pregnancy rate when using a standard gentle technique that includes not touching the top of the fundus with the catheter.