Race, Sex, and Neighborhood Socioeconomic Disparities in Ablation of Ventricular Tachycardia Within a National Medicare Cohort
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Background Ventricular tachycardia (VT) ablation significantly improves our ability to control VT, yet little is known about whether disparities exist in delivery of this technology. Methods and Results Using a national 100% Medicare inpatient data set of beneficiaries admitted with VT from January 1, 2014, through November 30, 2014, multivariable logistic regression techniques were used to examine the sociodemographic and clinical characteristics associated with receiving ablation. Census block group-level neighborhood socioeconomic disadvantage was measured for each patient by the Area Deprivation Index, a composite measure of socioeconomic disadvantage consisting of education, income, housing, and employment factors. Among 131 645 patients admitted with VT, 2190 (1.66%) received ablation. After adjustment for comorbidities, hospital characteristics, and sociodemographics, female sex (odds ratio [OR], 0.75 [95% CI, 0.67-0.84]), identifying as Black race (OR, 0.75 [95% CI, 0.62-0.90] compared with identifying as White race), and living in a highly socioeconomically disadvantaged neighborhood (national Area Deprivation Index percentile of >85%) (OR, 0.81 [95% CI, 0.69-0.95] versus Area Deprivation Index ≤85%) were associated with significantly lower odds of receiving ablation. Conclusions Female patients, patients identifying as Black race, and patients living in the most disadvantaged neighborhoods are 19% to 25% less likely to receive ablation during hospitalization with VT. The cause of and solutions for these disparities require further investigation.Keywords:
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The use of large‐tip ablation catheters in both animal and clinical studies has been documented in work conducted over the last 10 years. These studies have demonstrated the safety and efficacy of the use of 8‐ and 10‐mm tip ablation catheters in the treatment of type I isthmus‐dependent atrial flutter. Shorter procedure times are reported with the larger‐tip ablation catheters compared to standard 4‐ or 5‐mm tip ablation catheters, with comparable or greater efficacy, no significant increase in complications, and an improved quality of life. Larger‐tip ablation electrodes do require the use of higher‐power radiofrequency generators up to 100 W. The benefits of large‐tip ablation catheters are thus well documented for the treatment of type I isthmus‐dependent atrial flutter.
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Objectives: The goal of this study was to describe hospitalizations of infants during the first year of life according to week of gestational age (GA). We hypothesized that odds of any hospitalization would generally decrease with increasing GA, with late preterm infants experiencing additional increased risk of specific hospitalizations, such as hyperbilirubinemia. Methods: Birth certificates for >6.6 million infants born in California hospitals between 1993 and 2005 and surviving to discharge were linked to hospital discharge records during the first year of life. Odds of any hospitalization and any hospitalization for specific diagnoses during the first year of life were determined for infants 23 to 44 weeks’ GA. Further analysis determined odds of any hospitalization within 14, 30, and 90 days of birth discharge, and observed odds were compared with expected odds obtained through quadratic modeling. Results: Odds of any hospitalization within the first year of life decreased with advancing GA, but observed odds of any hospitalization exceeded expected odds for 35-, 36-, and 37-week GA infants for all time periods after discharge. Odds of any hospitalization for hyperbilirubinemia were greatest for infants 33 to 38 weeks’ GA (peak odds ratio at 36 weeks’ GA: 2.86 [95% confidence interval: 2.73–3.00]), and a relative peak in odds of any hospitalization for specific infections was observed among infants 33 to 36 weeks’ GA. Conclusions: Odds of any hospitalization during the first year of life exceeded expected odds of hospitalization for 35-, 36-, and 37-week GA infants. GAs at risk overlapped with, but were not identical to, GAs identified as late preterm infants.
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In reporting results of case-control studies, odds ratios are useful methods of reporting findings. However, odds ratios are often misinterpreted in the literature and by general readers.We searched all original articles which were published in the Korean Journal of Family Medicine from 1980 to May 2011 and identified those that report "odds ratios." Misinterpretation of odds ratios as relative risks has been identified. Estimated risk ratios were calculated when possible and compared with odds ratios.One hundred and twenty-eight articles using odds ratios were identified. Among those, 122 articles were analyzed for the frequency of misinterpretation of odds ratios as relative risks. Twenty-two reports out of these 122 articles misinterpreted odds ratios as relative risks. The percentage of misinterpreting reports decreased over years. Seventy-seven reports were analyzed to compare the estimated risk ratios with odds ratios. In most of these articles, odds ratios were greater than estimated risk ratios, 60% of which had larger than 20% standardized differences.In reports published in the Korean Journal of Family Medicine, odds ratios are frequently used. They were misinterpreted in part of the reports, although decreasing trends over years were observed.
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Categorical scientific data consisting of counts of events are frequently reported as risk or odds figures. Given a certain set of data, odds always differs upwards from risk. The relation between two risk figures may be expressed in various ways, one of which is the risk ratio. In the case of two odds figures, the choice is almost always the odds ratio. With a certain set of data from two groups, the odds ratio is not identical to the risk ratio (except when both are equal to 1). The odds ratio always magnifies the intergroup difference. When assessing published data, one must take care to observe whether reported ratio figures denote risk ratios or odds ratios.
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The odds for the occurrence of an event (e.g. a particular disease) is the ratio of the probability that the event will occur and the probability that the event will not occur. The odds ratio for an event is the ratio of two odds for the occurrence of that event (e.g. for persons who have been exposed to a particular risk factor versus persons who have not been exposed). The odds ratio is a frequently applied measure of association in case control research. However, the odds ratio is not readily understandable. In case of a rare event (e.g. a rare disease) the odds ratio can be interpreted as a relative risk which is easier to understand (i.e. the factor with which the risk of disease increases in people exposed to a certain condition).
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In the world of public health and medicine, researchers are often trying to discover new ways of understanding and preventing diseases and other negative health outcomes. When public health researchers want to examine the relationship between some sort of exposure, like smoking, and a disease, such as lung cancer, they will often start by calculating what is called an odds ratio. An odds ratio is a comparison of odds between people who were exposed and people who were not exposed. However, odds ratios can be tricky to understand, even for experienced researchers. In this article, we will break down the odds ratio by reviewing the concepts and calculations of probability and odds. We will also discuss how to interpret an odds ratio, and how these ratios can be useful in real-world applications.
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Odds ratios are commonly presented in the medical literature, including dermatology journals. Even when used appropriately, odds ratios are often difficult to interpret. This article illustrates this problem using an example from the recent dermatology literature. It then reviews the definitions of odds and odds ratio, as well as how odds and odds ratio relate to probability and relative risk. The divergence of odds ratios from relative risks when events are common (occurring in > or =10% of a sample) is explained. Methods to convert odds ratios to relative risks (and the reasons why conversion should be considered) are discussed.
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In Brief Odds and odds ratios are hard for many clinicians to understand. Odds are the probability of an event occurring divided by the probability of the event not occurring. An odds ratio is the odds of the event in one group, for example, those exposed to a drug, divided by the odds in another group not exposed. Odds ratios always exaggerate the true relative risk to some degree. When the probability of the disease is low (for example, less than 10%), the odds ratio approximates the true relative risk. As the event becomes more common, the exaggeration grows, and the odds ratio no longer is a useful proxy for the relative risk. Although the odds ratio is always a valid measure of association, it is not always a good substitute for the relative risk. Because of the difficulty in understanding odds ratios, their use should probably be limited to case-control studies and logistic regression, for which odds ratios are the proper measures of association. Odds ratios are difficult to understand and are often inappropriately used as proxies for relative risks when describing research results.
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