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Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population
Estimates and projections of diagnosed incidence and prevalence of atrial fibrillation (AF) in the United States have been highly inconsistent across published studies. Although it is generally acknowledged that AF incidence and prevalence are increasing due to growing numbers of older people in the U.S. population, estimates of the rate of expected growth have varied widely. Reasons for these variations include differences in study design, covered time period, birth cohort, and temporal effects, as well as improvements in AF diagnosis due to increased use of diagnostic tools and health care awareness. The objective of this study was to estimate and project the incidence and prevalence of diagnosed AF in the United States out to 2030. A large health insurance claims database for the years 2001 to 2008, representing a geographically diverse 5% of the U.S. population, was used in this study. The trend and growth rate in AF incidence and prevalence was projected by a dynamic age-period cohort simulation progression model that included all diagnosed AF cases in future prevalence projections regardless of follow-up treatment, as well as those cases expected to be chronic in nature. Results from the model showed that AF incidence will double, from 1.2 million cases in 2010 to 2.6 million cases in 2030. Given this increase in incidence, AF prevalence is projected to increase from 5.2 million in 2010 to 12.1 million cases in 2030. The effect of uncertainty in model parameters was explored in deterministic and probabilistic sensitivity analyses. Variability in future trends in AF incidence and recurrence rates has the greatest impact on the projected estimates of chronic AF prevalence. It can be concluded that both incidence and prevalence of AF are likely to rise from 2010 to 2030, but there exists a wide range of uncertainty around the magnitude of future trends.
Journal Article
Understanding Polarization
Polarization is a topic of intense interest among social scientists, but there is significant disagreement regarding the character of the phenomenon and little understanding of underlying mechanics. A first problem, we argue, is that polarization appears in the literature as not one concept but many. In the first part of the article, we distinguish nine phenomena that may be considered polarization, with suggestions of appropriate measures for each. In the second part of the article, we apply this analysis to evaluate the types of polarization generated by the three major families of computational models proposing specific mechanisms of opinion polarization.
Journal Article
Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study
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Early detection of atrial fibrillation or flutter (AF) may enable prevention of downstream morbidity. Consumer wrist-worn wearable technology is capable of detecting AF by identifying irregular pulse waveforms using photoplethysmography (PPG). The validity of PPG-based software algorithms for AF detection requires prospective assessment.
The Fitbit Heart Study (NCT04380415) is a single-arm remote clinical trial examining the validity of a novel PPG-based software algorithm for detecting AF. The proprietary Fitbit algorithm examines pulse waveform intervals during analyzable periods in which participants are sufficiently stationary. Fitbit consumers with compatible wrist-worn trackers or smartwatches were invited to participate. Enrollment began May 6, 2020 and as of October 1, 2020, 455,699 participants enrolled. Participants in whom an irregular heart rhythm was detected were invited to attend a telehealth visit and eligible participants were then mailed a one-week single lead electrocardiographic (ECG) patch monitor. The primary study objective is to assess the positive predictive value of an irregular heart rhythm detection for AF during the ECG patch monitor period. Additional objectives will examine the validity of irregular pulse tachograms during subsequent heart rhythm detections, self-reported AF diagnoses and treatments, and relations between irregular heart rhythm detections and AF episode duration and time spent in AF.
The Fitbit Heart Study is a large-scale remote clinical trial comprising a unique software algorithm for detection of AF. The study results will provide critical insights into the use of consumer wearable technology for AF detection, and for characterizing the nature of AF episodes detected using consumer-based PPG technology.
Journal Article
Diversity, Not Randomness, Trumps Ability
A number of formal models, including a highly influential model from Hong and Page, purport to show that functionally diverse groups often beat groups of individually high-performing agents in solving problems. Thompson argues that in Hong and Page's model, that the diverse groups are created by a random process explains their success, not the diversity. Here, I defend the diversity interpretation of the Hong and Page result. The failure of Thompson's argument shows that to understand the value of functional diversity, we should be clearer about how we conceive of and measure that diversity.
Journal Article
Cigarette Smoking and Risk Perceptions During the COVID-19 Pandemic Reported by Recently Hospitalized Participants in a Smoking Cessation Trial
BackgroundCigarette smoking is a risk factor for severe COVID-19 disease. Understanding smokers’ responses to the pandemic will help assess its public health impact and inform future public health and provider messages to smokers.ObjectiveTo assess risk perceptions and change in tobacco use among current and former smokers during the COVID-19 pandemic.DesignCross-sectional survey conducted in May–July 2020 (55% response rate)Participants694 current and former daily smokers (mean age 53, 40% male, 78% white) who had been hospitalized pre-COVID-19 and enrolled into a smoking cessation clinical trial at hospitals in Massachusetts, Pennsylvania, and Tennessee.Main MeasuresPerceived risk of COVID-19 due to tobacco use; changes in tobacco consumption and interest in quitting tobacco use; self-reported quitting and relapse since January 2020.Key Results68% (95% CI, 65–72%) of respondents believed that smoking increases the risk of contracting COVID-19 or having a more severe case. In adjusted analyses, perceived risk was higher in Massachusetts where COVID-19 had already surged than in Pennsylvania and Tennessee which were pre-surge during survey administration (AOR 1.56, 95% CI, 1.07–2.28). Higher perceived COVID-19 risk was associated with increased interest in quitting smoking (AOR 1.72, 95% CI 1.01–2.92). During the pandemic, 32% (95% CI, 27–37%) of smokers increased, 37% (95% CI, 33–42%) decreased, and 31% (95% CI, 26–35%) did not change their cigarette consumption. Increased smoking was associated with higher perceived stress (AOR 1.49, 95% CI 1.16–1.91). Overall, 11% (95% CI, 8–14%) of respondents who smoked in January 2020 (pre-COVID-19) had quit smoking at survey (mean, 6 months later) while 28% (95% CI, 22–34%) of former smokers relapsed. Higher perceived COVID-19 risk was associated with higher odds of quitting and lower odds of relapse.ConclusionsMost smokers believed that smoking increased COVID-19 risk. Smokers’ responses to the pandemic varied, with increased smoking related to stress and increased quitting associated with perceived COVID-19 vulnerability.
Journal Article
The Unmet Health Care Needs of Homeless Adults: A National Study
Objectives. We assessed the prevalence and predictors of past-year unmet needs for 5 types of health care services in a national sample of homeless adults. Methods. We analyzed data from 966 adult respondents to the 2003 Health Care for the Homeless User Survey, a sample representing more than 436 000 individuals nationally. Using multivariable logistic regression, we determined the independent predictors of each type of unmet need. Results. Seventy-three percent of the respondents reported at least one unmet health need, including an inability to obtain needed medical or surgical care (32%), prescription medications (36%), mental health care (21%), eyeglasses (41%), and dental care (41%). In multivariable analyses, significant predictors of unmet needs included food insufficiency, out-of-home placement as a minor, vision impairment, and lack of health insurance. Individuals who had been employed in the past year were more likely than those who had not to be uninsured and to have unmet needs for medical care and prescription medications. Conclusions. This national sample of homeless adults reported substantial unmet needs for multiple types of health care. Expansion of health insurance may improve health care access for homeless adults, but addressing the unique challenges inherent to homelessness will also be required.
Journal Article
Patients’ time in therapeutic range on warfarin among US patients with atrial fibrillation: Results from ORBIT-AF registry
Time in therapeutic range (TTR) of international normalized ratio (INR) of 2.0 to 3.0 is important for the safety and effectiveness of warfarin anticoagulation. There are few data on TTR among patients with atrial fibrillation (AF) in community-based clinical practice.
Using the US Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF), we examined TTR (using a modified Rosendaal method) among 5,210 patients with AF on warfarin and treated at 155 sites. Patients were grouped into quartiles based on TTR data. Multivariable logistic regression modeling with generalized estimating equations was used to determine patient and provider factors associated with the lowest (worst) TTR.
Overall, 59% of the measured INR values were between 2.0 and 3.0, with an overall mean and median TTR of 65% ± 20% and 68% (interquartile range [IQR] 53%-79%). The median times below and above the therapeutic range were 17% (IQR 8%-29%) and 10% (IQR 3%-19%), respectively. Patients with renal dysfunction, advanced heart failure, frailty, prior valve surgery, and higher risk for bleeding (ATRIA score) or stroke (CHA2DS2-VASc score) had significantly lower TTR (P < .0001 for all). Patients treated at anticoagulation clinics had only slightly higher median TTR (69%) than those not (66%) (P < .0001).
Among patients with AF in US clinical practices, TTR on warfarin is suboptimal, and those at highest predicted risks for stroke and bleeding were least likely to be in therapeutic range.
Journal Article
Out-of-system Care and Recording of Patient Characteristics Critical for Comparative Effectiveness Research
BACKGROUND:It is unclear how out-of-system care or electronic health record (EHR) discontinuity (i.e., receiving care outside of an EHR system) may affect validity of comparative effectiveness research using these data. We aimed to compare the misclassification of key variables in patients with high versus low EHR continuity.
METHODS:The study cohort comprised patients ages ≥65 identified in electronic health records from two US provider networks linked with Medicare insurance claims data from 2007 to 2014. By comparing electronic health records and claims data, we quantified EHR continuity by the proportion of encounters captured by the EHRs (i.e., “capture proportion”). Within levels of EHR continuity, for 40 key variables, we quantified misclassification by mean standardized differences between coding based on EHRs alone versus linked claims and EHR data.
RESULTS:Based on 183,739 patients, we found that mean capture proportion in a single electronic health record system was 16%–27% across two provider networks. Patients with highest level of EHR continuity (capture proportion ≥ 80%) had 11.4- to 17.4-fold less variable misclassification, when compared with those with lowest level of EHR continuity (capture proportion< 10%). Capturing at least 60% of the encounters in an EHR system was required to have reasonable variable classification (mean standardized difference <0.1). We found modest differences in comorbidity profiles between patients with high and low EHR continuity.
CONCLUSIONS:EHR discontinuity may lead to substantial misclassification in key variables. Restricting comparative effectiveness research to patients with high EHR continuity may confer a favorable benefit (reducing information bias) to risk (losing generalizability) ratio.
Journal Article