Gal Koplewitz

Bio: Gal Koplewitz is an academic researcher from Harvard University. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Golf habits among physicians and surgeons: observational cohort study

Abstract: Objectives To examine patterns of golfing among physicians: the proportion who regularly play golf, differences in golf practices across specialties, the specialties with the best golfers, and differences in golf practices between male and female physicians. Design Observational study. Setting Comprehensive database of US physicians linked to the US Golfing Association amateur golfer database. Participants 41 692 US physicians who actively logged their golf rounds in the US Golfing Association database as of 1 August 2018. Main outcome measures Proportion of physicians who play golf, golf performance (measured using golf handicap index), and golf frequency (number of games played in previous six months). Results Among 1 029 088 physicians, 41 692 (4.1%) actively logged golf scores in the US Golfing Association amateur golfer database. Men accounted for 89.5% of physician golfers, and among male physicians overall, 5.5% (37 309/683 297) played golf compared with 1.3% (4383/345 489) among female physicians. Rates of golfing varied substantially across physician specialties. The highest proportions of physician golfers were in orthopedic surgery (8.8%), urology (8.1%), plastic surgery (7.5%), and otolaryngology (7.1%), whereas the lowest proportions were in internal medicine and infectious disease ( Conclusions Golfing is common among US male physicians, particularly those in the surgical subspecialties. The association between golfing and patient outcomes, costs of care, and physician wellbeing remain unknown.

Predicting Dengue Incidence Leveraging Internet-Based Data Sources: A Case Study in 20 cities in Brazil.

Abstract: The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people’s lives and severely strain healthcare systems. In the absence of a reliable vaccine against it or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6-8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1-3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics. Author Summary As the incidence of infectious diseases like dengue continues to increase throughout the world, tracking their spread in real time poses a significant challenge to local and national health authorities. Accurate incidence data are often difficult to obtain as outbreaks emerge and unfold, both due the partial reach of serological surveillance (especially in rural areas), and due to delays in reporting, which result in post-hoc adjustments to what should have been real-time data. Thus, a range of ‘nowcasting’ tools have been developed to estimate disease trends, using different mathematical and statistical methodologies to fill the temporal data gap. Over the past several years, researchers have investigated how to best incorporate internet search data into predictive models, since these can be obtained in real-time. Still, most such models have been regression-based, and have tended to underperform in cases when epidemiological data are only available after long reporting delays. Moreover, in tropical countries, attention has increasingly turned from testing and applying models at the national level to models at higher spatial resolutions, such as states and cities. Here, we develop machine learning models based on both LASSO regression and on random forest ensembles, and proceed to apply and compare them across 20 cities in Brazil. We find that our methodology produces meaningful and actionable disease estimates at the city level with both underlying model classes, and that the two perform comparably across most metrics, although the ensemble method produces fewer outliers. We also compare model performance and the relative contribution of different data sources across diverse geographic, demographic and epidemic conditions.

2018 International Consensus Statement on Golf and Health to guide action by people, policymakers and the golf industry.

Abstract: Scientific and public interest relating to golf and health has increased recently. Players, potential players, the golf industry and facilities, and decision makers will benefit from a better understanding of how to realise potential health benefits and minimise health issues related to golf. We outline an International Consensus on Golf and Health. A systematic literature review informed the development of a survey. Utilising modified Delphi methods, an expert panel of 25 persons including public health and golf industry leaders, took part in serial surveys providing feedback on suggested items, and proposing new items. Predefined criteria for agreement determined whether each item was included within each survey round and in the final consensus. The working group identified 79 scientifically supportable statement items from literature review and discussions. Twenty-five experts (100%) completed all three rounds of surveys, rating each item, and suggesting modifications and/or new items for inclusion in subsequent surveys. After three rounds, 83 items achieved consensus with each with >75% agreement and

The need for speed: observational study of physician driving behaviors.

Abstract: Objective To determine whether fast driving, luxury car ownership, and leniency by police officers differ across medical specialties. Design Observational study. Setting Florida, USA. Participants 5372 physicians and a sample of 19 639 non-physicians issued a ticket for speeding during 2004-17. Main outcome measures Observed rates of extreme speeding (defined as driving >20 mph above the speed limit), luxury car ownership, and leniency of the speeding ticket by police officers, by physician specialty, after adjustment for age and sex. Results The sample included 5372 physicians who received 14 560 speeding tickets. The proportion of drivers who were reported driving at speeds greater than 20 mph was similar between physicians and a sample of 19 639 non-physicians who received a ticket for speeding (26.4% v 26.8% of tickets, respectively). Among physicians who received a ticket, psychiatrists were most likely to be fined for extreme speeding (adjusted odds ratio of psychiatry compared with baseline specialty of anesthesia 1.51, 95% confidence interval 1.07 to 2.14). Among drivers who received a ticket, luxury car ownership was most common among cardiologists (adjusted proportion of ticketed cardiologists who owned a luxury car 40.9%, 95% confidence interval 35.9% to 45.9%) and least common among physicians in emergency medicine, family practice, pediatrics, general surgery, and psychiatry (eg, adjusted proportion of luxury car ownership among family practice physicians 20.6%, 95% confidence interval 18.2% to 23.0%). Speed discounting, a marker of leniency by police officers in which ticketed speed is recorded at just below the threshold at which a larger fine would otherwise be imposed, was common, but rates did not differ by specialty and did not differ between physicians and a sample of non-physicians. Conclusions Rates of extreme speeding were highest among psychiatrists who received a ticket, whereas cardiologists were the most likely to be driving a luxury car when ticketed. Leniency by police officers was similar across specialties and between physicians and non-physicians.

Plasma metabolomics analyses highlight the multifaceted effects of noise exposure and the diagnostic power of dysregulated metabolites for noise-induced hearing loss in steel workers

Abstract: Noise exposure can lead to various kinds of disorders. Noise-induced hearing loss (NIHL) is one of the leading disorders confusing the noise-exposed workers. It is essential to identify NIHL markers for its early diagnosis and new therapeutic targets for its treatment. In this study, a total of 90 plasma samples from 60 noise-exposed steel factory male workers (the noise group) with (NIHL group, n = 30) and without NIHL (non-NIHL group, n = 30) and 30 male controls without noise exposure (control group) were collected. Untargeted human plasma metabolomic profiles were determined with HPLC-MS/MS. The levels of the metabolites in the samples were normalized to total peak intensity, and the processed data were subjected to multivariate data analysis. The Wilcoxon test and orthogonal partial least square-discriminant analysis (OPLS-DA) were performed. With the threshold of p < 0.05 and the variable importance of projection (VIP) value >1, 469 differential plasma metabolites associated with noise exposure (DMs-NE) were identified, and their associated 58 KEGG pathways were indicated. In total, 33 differential metabolites associated with NIHL (DMs-NIHL) and their associated 12 KEGG pathways were identified. There were six common pathways associated with both noise exposure and NIHL. Through multiple comparisons, seven metabolites were shown to be dysregulated in the NIHL group compared with the other two groups. Through LASSO regression analysis, two risk models were constructed for NIHL status predication which could discriminate NIHL from non-NIHL workers with the area under the curve (AUC) values of 0.840 and 0.872, respectively, indicating their efficiency in NIHL diagnosis. To validate the results of the metabolomics, cochlear gene expression comparisons between susceptible and resistant mice in the GSE8342 dataset from Gene Expression Omnibus (GEO) were performed. The immune response and cell death-related processes were highlighted for their close relations with noise exposure, indicating their critical roles in noise-induced disorders. We concluded that there was a significant difference between the metabolite’s profiles between NIHL cases and non-NIHL individuals. Noise exposure could lead to dysregulations of a variety of biological pathways, especially immune response and cell death-related processes. Our results might provide new clues for noise exposure studies and NIHL diagnosis.

Operateure sind die besseren Golfer.

Abstract: Jedes Kind weis: Arzte sollten golfen, sonst leidet ihr Sozialprestige. Nun uberrascht eine Studie mit dem Ergebnis, dass nur wenige Mediziner vom Golfvirus befallen sind. Es gibt aber einige Disziplinen, die sich besonders hervortun.

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study

Abstract: Background Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. Objective The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. Methods Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. Results The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of −3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of −2 weeks. The most correlated query from Google Trends was the “Dengue” topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of −3 weeks. Conclusions Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.