Safety evaluation of directly observed treatment short course (DOTS) regimen in a tertiary care hospital, Pune

Background: Directly observed treatment short course (DOTS) is a cornerstone of Revised National Tuberculosis Control Program of India. Adverse drug reactions (ADRs) induced by this therapy is common and it causes significant morbidity and mortality. Hence, the present study was undertaken to determine the incidence and pattern of ADRs and to assess causality and severity.Methods: We conducted prospective, observational study at DOTS center of tertiary care hospital, Pune. 150 pulmonary tuberculosis patients undergoing DOTS therapy were enrolled. They were monitored weekly in an intensive phase and monthly in the continuation phase. The suspected ADRs were recorded and assessed for causality and severity by standard algorithms.Results: Incidence of ADRs due to DOTS was 19.33% & total 35 ADRs had occurred in our study. Gastrointestinal intolerance, arthralgia & itching with or without rashes were most common ADRs (incidence rates: 12.67%, 2.67% and 2.67%, respectively). On evaluation of causality by Naranjo algorithm, majority of ADRs 91.43% were “possible.” As per WHO- Uppsala Monitoring Center scale, majority of ADRs 91.43% were “possible.” As per Modified Hartwig and Siegel scale, majority of ADRs were “moderate” (48.57%) but 8.57% were “severe.” Female gender was found to be a significant risk factor for developing ADRs (odds ratio: 3.08, 95% confidence interval: 1.33-7.12. 3.33%). ADRs & hepatotoxicity was major reason for defaulting from DOTS (60%).Conclusion: ADRs induced by DOTS are common and there is need of incorporating pharmacovigilance system for this vital public health program. Counseling of patients for timely prevention, detection, and management of ADRs will help in minimizing the further occurrence of ADRs

Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.publishedVersio

Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint

Economic Distress and Voting: Evidence from the Subprime Mortgage Crisis

Roughly 7 million Americans lost homes to foreclosure during the Great Recession. Despite claims that the subprime mortgage crisis helped fuel recent political turmoil in the U.S., we lack systematic empirical evidence about the effects of this unprecedented spike in home foreclosures on American elections. We combine nationwide deed-level public records data on home foreclosures with election data and administrative voter data to examine the effects of home foreclosures on electoral outcomes and on individual voter turnout. At the aggregate level, county-level difference-in-differences estimates show that counties that suffered larger increases in foreclosures did not punish or reward members of the incumbent president's party more than less affected counties. At the individual level, merging the Ohio voter file with foreclosure data, difference-in-differences estimates reveal that Ohioans whose homes were foreclosed on were somewhat less likely to turn out to vote, particularly when foreclosures occurred close to election day. The findings cast doubt on the claim that individual-level economic distress during the Great Recession directly activated angry voters, and raise questions about the posited causal link between economic distress and the electoral punishment of incumbents

Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint