Hysterectomy Volume Among Recent Obstetrics and Gynecology Residency Graduates.

OBJECTIVE: Although guidelines recommend hysterectomy be performed vaginally whenever possible, recent trainees have decreased exposure to vaginal hysterectomy given the availability of laparoscopic hysterectomy, nonsurgical management, and falling volume nationwide. We sought to estimate hysterectomy volume in the 5 years after residency. Our secondary objective was to compare vaginal hysterectomy utilization between recent graduates and senior surgeons. METHODS: Retrospective, statewide data from 2005 to 2014 was obtained from the Massachusetts Center for Health Information Analysis. All hysterectomies performed in Massachusetts, regardless of payer type, were included. Surgeon identifiers were cross-referenced to another data set with provider demographics. Hysterectomies performed in the first 5 years after graduation were compared with a group 21 to 25 years after residency. RESULTS: Data from inpatient and outpatient databases revealed 87,846 hysterectomies performed by 1967 physicians, including 3146 simple hysterectomies by 192 recent graduates. Recent graduates chose abdominal hysterectomy (44.2%) most commonly, followed by laparoscopic (29.4%), vaginal (16.1%), and laparoscopically assisted vaginal (10.4%). Recent graduates performed a median of 3 to 4 hysterectomies in each of the first 5 years with no increase over time (P = 1). The median number of vaginal or laparoscopic hysterectomies was 0 in these 5 years (interquartile ranges, 0-1 and 0-2, respectively). Members of the senior cohort performed a median of 8 to 9 hysterectomies annually, completing them vaginally more often (24.7% vs 16.1%, P < 0.01). When controlling for patient age and hysterectomy indication, this effect dissipated. CONCLUSIONS: Recent graduates perform 3 to 4 (interquartile range, 1-7) hysterectomies annually, predominantly by laparotomy. Although senior surgeons perform vaginal hysterectomy more often, this is explained by patient characteristics.

Medicare Patient Referral Networks to Female Pelvic Medicine and Reconstructive Surgeons Across the United States.

OBJECTIVE: The source of urogynecology patient referrals remains poorly understood. We used novel methods to identify referral networks to female pelvic medicine and reconstructive surgeons (FPMRS) and to determine factors associated with physician connections. METHODS: A retrospective analysis of Centers for Medicare and Medicaid Services data with physician sharing relationships spanning 180 days during 2015 was performed. All patients studied were Medicare beneficiaries. Provider patient-sharing networks were modeled using social network analytics. To visualize the resulting flow of patients from referring providers to FPMRS, we encoded the node and edge data and mapped the data to a map of the United States. RESULTS: We studied 206,568 Medicare beneficiaries who were seen by 618 different board-certified FPMRS. Internal medicine physicians followed by nurse practitioners referred the most patients to FPMRS. Over half of referrals were made locally, with patients traveling less than 5 miles from the referring provider to the female pelvic surgeon. The median number of incoming Medicare patient referrals per FPMRS provider was 15 (interquartile range, 12-20) over a 6-month period. The high modularity of the referral network indicates that most providers refer their patients to a few female pelvic surgeons. CONCLUSIONS: Medicare patient referrals to FPMRS are primarily and proportionally the highest from local internal medicine physicians.

Social Cost of Blindness Due to AMD and Diabetic Retinopathy in the United States in 2020.

BACKGROUND AND OBJECTIVE: To estimate the social cost of blindness due to wet age-related macular degeneration (wAMD), diabetic macular edema (DME), and proliferative diabetic retinopathy (PDR) in the United States in 2020. PATIENTS AND METHODS: Excess costs that occur because of blindness were estimated as the difference in costs in blind versus non-blind individuals. Per-patient costs were aggregated using the number of cases of blindness due to wAMD, DME, and PDR projected in 2020. RESULTS: Associated annual excess direct costs, indirect costs, and quality-adjusted life year loss per blind individual were $4,944, $54,614, and 0.214, respectively. Combining estimates with 246,423 projected cases of blindness due to wAMD, DME, and PDR translated to total societal costs of $20 billion in 2020, estimated to triple by 2050. CONCLUSION: Excess social costs associated with blindness in individuals with wAMD, DME, and PDR are substantial, with more than half of the burden attributed to indirect costs. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:S6-S14.].

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PURPOSE: The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. METHOD: Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. RESULTS: The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. CONCLUSION: Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland.

Improved predictive mapping of indoor radon concentrations using ensemble regression trees based on automatic clustering of geological units.

PURPOSE: According to estimations around 230 people die as a result of radon exposure in Switzerland. This public health concern makes reliable indoor radon prediction and mapping methods necessary in order to improve risk communication to the public. The aim of this study was to develop an automated method to classify lithological units according to their radon characteristics and to develop mapping and predictive tools in order to improve local radon prediction. METHOD: About 240 000 indoor radon concentration (IRC) measurements in about 150 000 buildings were available for our analysis. The automated classification of lithological units was based on k-medoids clustering via pair-wise Kolmogorov distances between IRC distributions of lithological units. For IRC mapping and prediction we used random forests and Bayesian additive regression trees (BART). RESULTS: The automated classification groups lithological units well in terms of their IRC characteristics. Especially the IRC differences in metamorphic rocks like gneiss are well revealed by this method. The maps produced by random forests soundly represent the regional difference of IRCs in Switzerland and improve the spatial detail compared to existing approaches. We could explain 33% of the variations in IRC data with random forests. Additionally, the influence of a variable evaluated by random forests shows that building characteristics are less important predictors for IRCs than spatial/geological influences. BART could explain 29% of IRC variability and produced maps that indicate the prediction uncertainty. CONCLUSION: Ensemble regression trees are a powerful tool to model and understand the multidimensional influences on IRCs. Automatic clustering of lithological units complements this method by facilitating the interpretation of radon properties of rock types. This study provides an important element for radon risk communication. Future approaches should consider taking into account further variables like soil gas radon measurements as well as more detailed geological information.

Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: an application to Switzerland.

PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.

Major influencing factors of indoor radon concentrations in Switzerland.

PURPOSE: In Switzerland, nationwide large-scale radon surveys have been conducted since the early 1980s to establish the distribution of indoor radon concentrations (IRC). The aim of this work was to study the factors influencing IRC in Switzerland using univariate analyses that take into account biases caused by spatial irregularities of sampling. METHODS: About 212,000 IRC measurements carried out in more than 136,000 dwellings were available for this study. A probability map to assess risk of exceeding an IRC of 300 Bq/m(3) was produced using basic geostatistical techniques. Univariate analyses of IRC for different variables, namely the type of radon detector, various building characteristics such as foundation type, year of construction and building type, as well as the altitude, the average outdoor temperature during measurement and the lithology, were performed comparing 95% confidence intervals among classes of each variable. Furthermore, a map showing the spatial aggregation of the number of measurements was generated for each class of variable in order to assess biases due to spatially irregular sampling. RESULTS: IRC measurements carried out with electret detectors were 35% higher than measurements performed with track detectors. Regarding building characteristics, the IRC of apartments are significantly lower than individual houses. Furthermore, buildings with concrete foundations have the lowest IRC. A significant decrease in IRC was found in buildings constructed after 1900 and again after 1970. Moreover, IRC decreases at higher outdoor temperatures. There is also a tendency to have higher IRC with altitude. Regarding lithology, carbonate rock in the Jura Mountains produces significantly higher IRC, almost by a factor of 2, than carbonate rock in the Alps. Sedimentary rock and sediment produce the lowest IRC while carbonate rock from the Jura Mountains and igneous rock produce the highest IRC. Potential biases due to spatially unbalanced sampling of measurements were identified for several influencing factors. CONCLUSIONS: Significant associations were found between IRC and all variables under study. However, we showed that the spatial distribution of samples strongly affected the relevance of those associations. Therefore, future methods to estimate local radon hazards should take the multidimensionality of the process of IRC into account.