Use of near-real-time medical claims data to generate timely vaccine coverage estimates in the US: The dynamics of PCV13 vaccine uptake

Vaccine ◽  
2013 ◽  
Vol 31 (50) ◽  
pp. 5983-5988 ◽  
Author(s):  
Cynthia Schuck-Paim ◽  
Robert Taylor ◽  
David Lindley ◽  
Keith P. Klugman ◽  
Lone Simonsen
Keyword(s):  
Real Time ◽  
Claims Data ◽  
Vaccine Coverage ◽  
Vaccine Uptake ◽  
Medical Claims ◽  
The Us

Abstract 15772: Cardiovascular Hospitalizations and Procedures Plummet Nationwide During Covid-19 - an Analysis of US Medical Claims

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Monica R Shah ◽  
Tanya F Partridge ◽  
Xiaoqing Xue ◽  
Justin L Gregg
Keyword(s):  
Claims Data ◽  
Private Insurance ◽  
National Level ◽  
Regional Studies ◽  
Bayesian Hierarchical ◽  
Medical Claims ◽  
Equivalent Time ◽  
Time Period ◽  
The Us ◽  
National Trends

Introduction: Regional studies have reported a decline in cardiovascular (CV) hospitalizations and procedures with the onset of the coronavirus disease-2019 (COVID-19) pandemic. Factors may include patient reluctance to seek care and de-prioritization of approvals for CV admissions by hospitals. We wanted to assess these observations at a national level. Hypothesis: To examine national trends in CV hospitalizations for acute myocardial infarction (AMI), unstable angina (USA), and heart failure (HF), as well as left heart catheterizations (LHC), using US medical claims data. Methods: We interrogated IQVIA US Claims data, a verified source, from Jan 2019 to May 2020 (214 million patients; 76% private insurance claims, 19% Medicare claims, 5% Medicaid claims). Since confirmed COVID-19 cases in the US began rising in Mar 2020, this was used as reference point to identify cohorts for comparison. Trends in volumes of hospitalizations for key CV events (AMI, USA, and HF) and LHC were compared from Mar 1 to May 8, 2020 to the equivalent time period in 2019. We used a Bayesian hierarchical model to assess trends. Results: From Mar to May 2020, compared to 2019, there were significantly fewer hospitalizations for: key CV events (1,110,492 vs. 1,487,558; p=0.0016); AMI (277,615 vs. 412,235; p=0.0002); USA (1,007 vs. 1,688, p=0.1245); and, HF (831,870 vs. 1,073,635; p=0.0036). There were significantly fewer LHC (118,393 vs. 221,701; p=0.0002). As shown in the Figure, there was a significant decline in CV hospitalizations in 2020 compared to 2019. Conclusions: During the COVID-19 pandemic, CV hospitalizations have declined significantly in the US. We observed an ~25% drop in CV hospitalizations and an ~50% drop in LHC. To the best of our knowledge, this is the first national evaluation of trends in CV care during COVID-19 and validate concerns that acute CV care in the US has been delayed or deferred, potentially foreshadowing a surge of CV complications in the future.

scholarly journals Demonstrating the Use of High-Volume Electronic Medical Claims Data to Monitor Local and Regional Influenza Activity in the US

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e102429 ◽  
Author(s):  
Cécile Viboud ◽  
Vivek Charu ◽  
Donald Olson ◽  
Sébastien Ballesteros ◽  
Julia Gog ◽  
...  
Keyword(s):  
Claims Data ◽  
High Volume ◽  
Medical Claims ◽  
The Us ◽  
Influenza Activity

scholarly journals Real-time geospatial analysis identifies gaps in COVID-19 vaccination in a minority population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cici Bauer ◽  
Kehe Zhang ◽  
Miryoung Lee ◽  
Michelle Jones ◽  
Arturo Rodriguez ◽  
...  
Keyword(s):  
Real Time ◽  
Vaccine Coverage ◽  
Vaccination Campaign ◽  
Herd Immunity ◽  
Health Department ◽  
Geospatial Analysis ◽  
Minority Population ◽  
Public Health Department ◽  
The Us ◽  
Mexico Border

AbstractCOVID-19 vaccination is being rapidly rolled out in the US and many other countries, and it is crucial to provide fast and accurate assessment of vaccination coverage and vaccination gaps to make strategic adjustments promoting vaccine coverage. We reported the effective use of real-time geospatial analysis to identify barriers and gaps in COVID-19 vaccination in a minority population living in South Texas on the US-Mexico Border, to inform vaccination campaign strategies. We developed 4 rank-based approaches to evaluate the vaccination gap at the census tract level, which considered both population vulnerability and vaccination priority and eligibility. We identified areas with the highest vaccination gaps using different assessment approaches. Real-time geospatial analysis to identify vaccination gaps is critical to rapidly increase vaccination uptake, and to reach herd immunity in the vulnerable and the vaccine hesitant groups. Our results assisted the City of Brownsville Public Health Department in adjusting real-time targeting of vaccination, gathering coverage assessment, and deploying services to areas identified as high vaccination gap. The analyses and responses can be adopted in other locations.

scholarly journals Assessing the distribution and drivers of vaccine hesitancy using medical claims data

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Sandra Goldlust ◽  
Elizabeth Lee ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Claims Data ◽  
The United States ◽  
Vaccine Uptake ◽  
Vaccine Hesitancy ◽  
Modeling Framework ◽  
Medical Claims ◽  
Vaccine Refusal ◽  
The Social ◽  
Disparate Data

ObjectiveThe purpose of this study was to investigate the use of large-scalemedical claims data for local surveillance of under-immunizationfor childhood infections in the United States, to develop a statisticalframework for integrating disparate data sources on surveillance ofvaccination behavior, and to identify the determinants of vaccinehesitancy behavior.IntroductionIn the United States, surveillance of vaccine uptake for childhoodinfections is limited in scope and spatial resolution. The NationalImmunization Survey (NIS) - the gold standard tool for monitoringvaccine uptake among children aged 19-35 months - is typicallyconstrained to producing coarse state-level estimates.1In recent years,vaccine hesitancy (i.e., a desire to delay or refuse vaccination, despiteavailability of vaccination services)2has resurged in the United States,challenging the maintenance of herd immunity. In December 2014,foreign importation of the measles virus to Disney theme parks inOrange County, California resulted in an outbreak of 111 measlescases, 45% of which were among unvaccinated individuals.3Digitalhealth data offer new opportunities to study the social determinantsof vaccine hesitancy in the United States and identify finer spatialresolution clusters of under-immunization using data with greaterclinical accuracy and rationale for hesitancy.4MethodsOur U.S. medical claims data comprised monthly reports ofdiagnosis codes for under-immunization and vaccine refusal(Figure 1). These claims were aggregated to five-digit zip-codes bypatient age-group from 2012 to 2015. Spatial generalized linear mixedmodels were used to generate county-level maps for surveillanceof under-immunization and to identify the determinants of vaccinehesitancy, such as income, education, household size, religious grouprepresentation, and healthcare access. We developed a Bayesianmodeling framework that separates the observation of vaccinehesitancy in our data from true underlying rates of vaccine hesitancyin the community. Our model structure also enabled us to borrowinformation from neighboring counties, which improves predictionof vaccine hesitancy in areas with missing or minimal data. Estimatesof the posterior distributions of model parameters were generated viaMarkov chain Monte Carlo (MCMC) methods.ResultsOur modeling framework enabled the production of county-levelmaps of under-immunization and vaccine refusal in the UnitedStates between 2012-2015, the identification of geographic clustersof under-immunization, and the quantification of the associationbetween various epidemiological factors and vaccination status.In addition, we found that our model structure enabled us to accountfor spatial variation in reporting vaccine hesitancy, which improvedour estimation.ConclusionsOur work demonstrate the utility of using large-scale medicalclaims data to improve surveillance systems for vaccine uptake andto assess the social and ecological determinants of vaccine hesitancy.We describe a flexible, hierarchical modeling framework forintegrating disparate data sources, particularly for data collectedthrough different measurement processes or at different spatial scales.Our findings will enhance our understanding of the causes of under-immunization, inform the design of vaccination policy, and aid inthe development of targeted public health strategies for optimizingvaccine uptake.Figure 1. Instances of vaccine refusal (per 100,000 population) for UnitedStates counties in 2014 as observed in medical claims data.

scholarly journals Methodology Using Pharmacy And Medical Claims Data To Evaluate Real-World Outcomes And Costs Of Ivf Treatment In The Us

2015 ◽  
Vol 18 (3) ◽  
pp. A16
Author(s):  
F.A. Corvino ◽  
A. Surinach ◽  
J.C. Locklear ◽  
A.M. Howe ◽  
B. Hayward ◽  
...  
Keyword(s):  
Real World ◽  
Claims Data ◽  
Medical Claims ◽  
The Us

Influenza vaccination coverage in a population-based cohort of Australian-born Aboriginal and non-Indigenous older adults

2019 ◽  
Vol 43 ◽  
Author(s):  
Amalie Dyda ◽  
Surendra Karki ◽  
Marlene Kong ◽  
Heather F Gidding ◽  
John M Kaldor ◽  
...  
Keyword(s):  
Influenza Vaccine ◽  
Influenza Vaccination ◽  
Vaccination Coverage ◽  
Torres Strait Islander ◽  
Vaccine Coverage ◽  
Vaccine Uptake ◽  
P Value ◽  
Healthy Weight ◽  
Medical Risk ◽  
Torres Strait

Background: There is limited information on vaccination coverage and characteristics associated with vaccine uptake in Aboriginal and/or Torres Strait Islander adults. We aimed to provide more current estimates of influenza vaccination coverage in Aboriginal adults. Methods: Self-reported vaccination status (n=559 Aboriginal and/or Torres Strait Islander participants, n=80,655 non-Indigenous participants) from the 45 and Up Study, a large cohort of adults aged 45 years or older, was used to compare influenza vaccination coverage in Aboriginal and/or Torres Strait Islander adults with coverage in non-Indigenous adults. Results: Of Aboriginal and non-Indigenous respondents aged 49 to <65 years, age-standardised influenza coverage was respectively 45.2% (95% CI 39.5–50.9%) and 38.5%, (37.9–39.0%), p-value for heterogeneity=0.02. Coverage for Aboriginal and non-Indigenous respondents aged ≥65 years was respectively 67.3% (59.9–74.7%) and 72.6% (72.2–73.0%), p-heterogeneity=0.16. Among Aboriginal adults, coverage was higher in obese than in healthy weight participants (adjusted odds ratio (aOR)=2.38, 95%CI 1.44–3.94); in those aged <65 years with a medical risk factor than in those without medical risk factors (aOR=2.13, 1.37–3.30); and in those who rated their health as fair/poor compared to those who rated it excellent (aOR=2.57, 1.26–5.20). Similar associations were found among non-Indigenous adults. Conclusions: In this sample of adults ≥65 years, self-reported influenza vaccine coverage was not significantly different between Aboriginal and non-Indigenous adults whereas in those <65 years, coverage was higher among Aboriginal adults. Overall, coverage in the whole cohort was suboptimal. If these findings are replicated in other samples and in the Australian Immunisation Register, it suggests that measures to improve uptake, such as communication about the importance of influenza vaccine and more effective reminder systems, are needed among adults.

Improving Operational Efficiency Using Automated Time Analysis for Multi-Well Pad Fracturing

2021 ◽  
Author(s):  
Fahd Siddiqui ◽  
Mohammadreza Kamyab ◽  
Michael Lowder
Keyword(s):  
Real Time ◽  
Operational Efficiency ◽  
Permian Basin ◽  
Potential Cost ◽  
Time Period ◽  
Cloud Server ◽  
The Us ◽  
Single Well ◽  
Complete Automation

Abstract The economic success of unconventional reservoirs relies on driving down completion costs. Manually measuring the operational efficiency for a multi-well pad can be error-prone and time-prohibitive. Complete automation of this analysis can provide an effortless real-time insight to completion engineers. This study presents a real-time method for measuring the time spent on each completion activity, thereby enabling the identification and potential cost reduction avenues. Two data acquisition boxes are utilized at the completion site to transmit both the fracturing and wireline data in real-time to a cloud server. A data processing algorithm is described to determine the start and end of these two operations for each stage of every well on the pad. The described method then determines other activity intervals (fracturing swap-over, wireline swap-over, and waiting on offset wells) based on the relationship between the fracturing and wireline segments of all the wells. The processed data results can be viewed in real-time on mobile or computers connected to the cloud. Viewing the full operational time log in real-time helps engineers analyze the whole operation and determine key performance indicators (KPIs) such as the number of fractured stages per day, pumping percentage, average fracture, and wireline swap-over durations for a given time period. In addition, the performance of the day and night crews can be evaluated. By plotting a comparison of KPIs for wireline and fracturing times, trends can be readily identified for improving operational efficiency. Practices from best-performing stages can be adopted to reduce non-pumping times. This helps operators save time and money to optimize for more efficient operations. As the number of wells increases, the complexity of manual generation of time-log increases. The presented method can handle multi-well fracturing and wireline operations without such difficulty and in real-time. A case study is also presented, where an operator in the US Permian basin used this method in real-time to view and optimize zipper operations. Analysis indicated that the time spent on the swap over activities could be reduced. This operator set a realistic goal of reducing 10 minutes per swap-over interval. Within one pad, the goal was reached utilizing this method, resulting in reducing 15 hours from the total pad time. The presented method provides an automated overview of fracturing operations. Based on the analysis, timely decisions can be made to reduce operational costs. Moreover, because this method is automated, it is not limited to single well operations but can handle multi-well pad completion designs that are commonplace in unconventionals.

scholarly journals Internet of medical things – integrated, ultrasound-based respiration monitoring system for incubators

2020 ◽  
Author(s):  
◽  
Tareq Abdulqader
Keyword(s):  
Real Time ◽  
Respiration Rate ◽  
Surface Measurement ◽  
Time Interval ◽  
Portable Devices ◽  
Important Indicator ◽  
The Us ◽  
Respiratory Signal ◽  
Central Apnoea ◽  
Respiration Signal

The study's aim was to develop a non-contact, ultrasound (US) based respiration rate and respiratory signal monitor suitable for babies in incubators. Respiration rate indicates average number of breaths per minute and is higher in young children than adults. It is an important indicator of health deterioration in critically ill patients. The current incubators do not have an integrated respiration monitor due to complexities in its adaptation. Monitoring respiratory signal assists in diagnosing respiration rated problems such as central Apnoea that can affect infants. US sensors are suitable for integration into incubators as US is a harmless and cost-effective technology. US beam is focused on the chest or abdomen. Chest or abdomen movements, caused by respiration process, result in variations in their distance to the US transceiver located at a distance of about 0.5 m. These variations are recorded by measuring the time of flight from transmitting the signal and its reflection from the monitored surface. Measurement of this delay over a time interval enables a respiration signal to be produced from which respiration rate and pauses in breathing are determined. To assess the accuracy of the developed device, a platform with a moving surface was devised. The magnitude and frequency of its surface movement were accurately controlled by its signal generator. The US sensor was mounted above this surface at a distance of 0.5 m. This US signal was wirelessly transmitted to a microprocessor board to digitise. The recorded signal that simulated a respiratory signal was subsequently stored and displayed on a computer or an LCD screen. The results showed that US could be used to measure respiration rate accurately. To cater for possible movement of the infant in the incubator, four US sensors were adapted. These monitored the movements from different angles. An algorithm to interpret the output from the four US sensors was devised and evaluated. The algorithm interpreted which US sensor best detected the chest movements. An IoMT system was devised that incorporated NodeMcu to capture signals from the US sensor. The detected data were transmitted to the ThingSpeak channel and processed in real-time by ThingSpeak’s add-on Matlab© feature. The data were processed on the cloud and then the results were displayed in real-time on a computer screen. The respiration rate and respiration signal could be observed remotely on portable devices e.g. mobile phones and tablets. These features allow caretakers to have access to the data at any time and be alerted to respiratory complications. A method to interpret the recorded US signals to determine respiration patterns, e.g. intermittent pauses, were implemented by utilising Matlab© and ThingSpeak Server. The method successfully detected respiratory pauses by identifying lack of chest movements. The approach can be useful in diagnosing central apnoea. In central apnoea, respiratory pauses are accompanied by cessation of chest or abdominal movements. The devised system will require clinical trials and integration into an incubator by conforming to the medical devices directives. The study demonstrated the integration of IoMT-US for measuring respiration rate and respiratory signal. The US produced respiration rate readings compared well with the actual signal generator's settings of the platform that simulated chest movements.

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