REMEMBER FIRST 3 LETTERS OF STRoke
During a BBQ, a friend stumbled and took a little fall – she assured everyone that she was fine (they offered to call paramedics) and just tripped over a brick because of her new shoes. They got her cleaned up and got her a new plate of food – while she appeared a bit shaken up, Ingrid went about enjoying herself the rest of the evening. Ingrid’s husband called later telling everyone that his wife had been taken to the hospital – (at 6:00pm, Ingrid passed away.) She had suffered a stroke at the BBQ. Had they known how to identify the signs of a stroke, perhaps Ingrid would be with us today. Some don’t die. They end up in a helpless, hopeless condition instead.
It only takes a minute to read this. ..
Many stroke victims can be successfully treated, with some recovering completely, if the stroke is recognized, diagnosed and cared for within 3 hours from the onset of symptoms, which is not an easy accomplishment.
RECOGNIZING A STROKE
(The first 3 letters of STROKE)
Sometimes symptoms of a stroke are difficult to identify. Unfortunately, the lack of awareness spells disaster. The stroke victim may suffer severe brain damage when people nearby fail to recognize the symptoms of a stroke. Now doctors say a bystander can recognize a stroke by asking three simple questions:
S *Ask the individual to SMILE.
Both sides of the mouth should look symmetrical.
T *Ask the person to TALK . To SPEAK A SIMPLE SENTENCE . (I.e. . . “It is sunny out today.” ) The victim should be able to speak and the words should not sound slurred or be spoken in the wrong order.
R *Ask him or her to RAISE BOTH ARMS.
If he or she has trouble with ANY ONE of these tasks, call 9-1-1 immediately and describe the symptoms to the dispatcher.
If everyone who gets this e-mail sends it to 10 people, it is likely that at least one life will be saved.Read More
1 Manor Hospital, Walsall, 2 University of Central England, 3 Allergy and Respiratory Research Group, Division of Community Health Sciences, University of Edinburgh. Correspondence to: A Sheikh Aziz.Sheikh@ed.ac.uk
A hookah—also known as hubbly bubbly, shisha, or narghile—is a glass based waterpipe used for smoking. It operates by water filtration and indirect heat. Tobacco or molasses are placed in the bowl at the top of the apparatus, which is connected to the water filled base by a pipe. This bowl is then covered with perforated material, such as kitchen foil. Burning charcoal is then placed on top of the foil. During inhalation the smoke from the charcoal is pulled through the tobacco down the pipe and towards the water. After bubbling through the water, the cooled smoke surfaces and is drawn through the hose and inhaled. Some hookahs have a “choke” to control the amount of smoke inhaled. Electric burners are also available, which offer a quicker smoke than the original charcoal burners.
How common is waterpipe smoking?
Around 100 million people use a hookah daily worldwide.1 Some of these smokers are children—a study in the central region of Israel among predominantly Jewish secondary school children found that 41% had or were smoking tobacco through a hookah.2 Similarly, a US study of Arab American adolescents found that 27% had experimented with a hookah.3
Introduction of the flavoured and aromatic tobacco has helped broaden the appeal of hookahs, both in their traditional homelands and in Western Europe. In Egypt, for example, younger adults prefer fruit flavoured tobacco, whereas older people tend to prefer smoking molasses—thick treacle-like syrups that burn like tobacco leaf products but are nicotine free.
Although the hookah is commonly used for smoking herbal fruits after meals, it has recently become increasingly used for smoking tobacco, massel (aromatic tobacco), cannabis and bango (an intoxicating plant leaf).
The hookah is commonly shared among family members including children, friends, and guests. Hookah establishments are also increasingly found around university campuses, where multiple hose waterpipes are used for group smoking. Recent work indicates that relative to cigarette smoking, tobacco used in a waterpipe is characterised by more intermittent use, greater social acceptability, increased use among women, and a lower interest in quitting, probably because people are less aware of its addictive properties.4 Family attitudes towards children smoking tobacco in waterpipes are reported to be far more permissive than attitudes to cigarette smoking.5 Our observations among the children of friends, relatives, and acquaintances confirm this.
Rising numbers of children in the UK are being exposed to and experimenting with smoking hookah products. Although accurate data are lacking, children as young as 10 years old are known to smoke fruit flavoured aromatic tobacco in areas with large minority ethnic communities such as Leicester and London.
Hookahs are relatively commonplace in Middle Eastern restaurants. A session of smoking typically costs £5 to £15. For those wanting to smoke at home, a hookah costs from £30 to £300. Many are, however, brought over more cheaply from the Middle East after business or holiday travel.
Not all tobacco packaging exhibits a warning on the effects of tobacco or the content of nicotine. And it is relatively easy for children to buy tobacco for use in hookahs without many questions being asked.
Little is known about the pharmacological effects and dependency associated with smoking tobacco in a hookah. The nicotine content in hookah tobacco seems to be the same as in cigarettes.6 Hookah smoking carries a greater risk of carbon monoxide poisoning than cigarette smoking,7 particularly if smaller hookah pipes and “quick lighting” commercial charcoal are used.8
There is also some evidence that hookah smoking causes chromosomal damage.9 The concentration of cancer causing additive substances may be equivalent to that in cigarettes, but hookah smokers are additionally exposed to the carcinogenic effect of hydrocarbons and heavy metals in the charcoal. Gum disease has been reported to be five times more common in hookah smokers than in cigarette smokers.10 Shared smoking also carries a small but important risk of transmitting infectious diseases directly into the respiratory tract.
Implications of UK Health Act
When used for smoking tobacco, the hookah is included in the legislation that came into force in England in July 2007 banning smoking in public places.11 We believe that including the hookah in the legislation is appropriate since the exposure of non-smokers to tobacco fumes is considerably higher than for cigarette smoking because of the large plume of smoke that the hookah generates. It remains to be seen what effect this legislation will have on smoking non-tobacco containing products that still generate a large amount of smoke.Read More
N Engl J Med 2014; 370:54-59January 2, 2014 DOI: 10.1056/NEJMsa1204142
Distracted driving attributable to the performance of secondary tasks is a major cause of motor vehicle crashes both among teenagers who are novice drivers and among adults who are experienced drivers.
We conducted two studies on the relationship between the performance of secondary tasks, including cell-phone use, and the risk of crashes and near-crashes. To facilitate objective assessment, accelerometers, cameras, global positioning systems, and other sensors were installed in the vehicles of 42 newly licensed drivers (16.3 to 17.0 years of age) and 109 adults with more driving experience.
During the study periods, 167 crashes and near-crashes among novice drivers and 518 crashes and near-crashes among experienced drivers were identified. The risk of a crash or near-crash among novice drivers increased significantly if they were dialing a cell phone (odds ratio, 8.32; 95% confidence interval [CI], 2.83 to 24.42), reaching for a cell phone (odds ratio, 7.05; 95% CI, 2.64 to 18.83), sending or receiving text messages (odds ratio, 3.87; 95% CI, 1.62 to 9.25), reaching for an object other than a cell phone (odds ratio, 8.00; 95% CI, 3.67 to 17.50), looking at a roadside object (odds ratio, 3.90; 95% CI, 1.72 to 8.81), or eating (odds ratio, 2.99; 95% CI, 1.30 to 6.91). Among experienced drivers, dialing a cell phone was associated with a significantly increased risk of a crash or near-crash (odds ratio, 2.49; 95% CI, 1.38 to 4.54); the risk associated with texting or accessing the Internet was not assessed in this population. The prevalence of high-risk attention to secondary tasks increased over time among novice drivers but not among experienced drivers.
The risk of a crash or near-crash among novice drivers increased with the performance of many secondary tasks, including texting and dialing cell phones. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Highway Traffic Safety Administration.)
Drivers who are 15 to 20 years of age constitute 6.4% of all drivers, but they account for 10.0% of all motor vehicle traffic deaths and 14.0% of all police-reported crashes resulting in injuries.1 These rates are thought to result from a combination of young age, inexperience, and risky driving behaviors.2
One of the riskiest driving behaviors is the performance of a secondary task, and novice drivers appear to be particularly prone to this distraction.3 Distracted driving has been defined as the “diversion of attention away from activities critical for safe driving toward a competing activity.”4 Drivers engage in many competing tasks (including eating, adjusting the radio, and talking to passengers) that are not related to operating the vehicle in traffic, but the use of electronic devices such as cell phones while driving has garnered the most public and mass-media interest. An estimated 9% of all persons who drive during the day do so while dialing or talking on a cell phone or sending or receiving text messages.3
Estimates based on cell-phone records indicate that cell-phone use among all drivers increases the risk of a crash by a factor of 4.5,6 Likewise, simulator studies involving adolescent drivers indicate that texting while driving increases the frequency of deviations in a lane relative to the position from the centerline.7 Adolescents who were using a cell phone on a test track were more likely than experienced adult drivers who were using a cell phone to enter an intersection at a red or yellow light.8Simulation and test-track research on distraction among experienced drivers indicates that cell-phone use delays reaction to potential hazards,9-11 increases following distances,12 and decreases the driver’s visual scanning of the environment.13,14Performance of a secondary task can increase the risk of a crash because it is cognitively demanding (preventing the driver from devoting full attention to driving) and because it takes the driver’s eyes off the road ahead so that he or she cannot see and respond to unexpected hazards.15
Both the 100-Car Naturalistic Driving Study (hereinafter called the 100-Car Study),14 which involved experienced drivers, and the Naturalistic Teenage Driving Study (NTDS),16 which involved novice drivers, used data-recording devices installed in the participants’ vehicles to assess their behaviors while driving and during a crash or near-crash. In previous analyses of NTDS data, we reported that among newly licensed drivers, the rates of crashes or near-crashes were 3.9 times as high as the corresponding rates among their parents when they drove the same vehicles, and the rates of a gravitational-force event (e.g., hard braking or making sharp turns or an overcorrection) were 5.1 times as high.15 Here we report the results of our analysis of both studies with respect to the prevalence of engagement in a secondary task and the associated risk of a crash or near-crash among novice and experienced drivers.
The NTDS data were collected from June 2006 through September 2008, and the 100-Car Study data were collected from January 2003 through July 2004. The two studies used similar experimental methods, detailed descriptions of which have been reported previously.14,16
We used a case–cohort approach to compare the prevalence of each task in the seconds before a crash or near-crash with the prevalence of the task during randomly sampled control periods of driving. We conducted separate analyses involving novice drivers and experienced drivers.
In both studies, adults provided written informed consent, and adolescents (i.e., those under the age of 18 years) provided written informed assent. Both studies were approved by the institutional review board of Virginia Polytechnic Institute and State University.
In the NTDS, 42 newly licensed drivers (22 females and 20 males) from southwestern Virginia were recruited, and instruments were installed in their personal vehicles. At the initiation of the study, the mean (±SD) age of the participants was 16.4±0.3 years of age, and they had had a driver’s license for 3 weeks or less. They received a total of $1,800 in monthly and end-of-study compensation for participation in the 18-month study.
In the 100-Car Study, 109 participants (43 women and 66 men) between the ages of 18 and 72 years (mean age, 36.2±14.4 years) from the Washington, D.C., area were recruited. The mean length of time that participants had been driving was 20.0±14.5 years. A total of 22 participants were compensated with the use of a leased vehicle, and 87 participants drove their own vehicles; the latter group received a total of $1,800 ($125 per month plus $300 at the end of the 12-month study).
Instruments with the same data-acquisition systems (developed at the Virginia Tech Transportation Institute) were installed in vehicles in both studies. These systems included four cameras (forward view, rear view, view of the driver’s face, and view over the driver’s right shoulder) and a suite of vehicle sensors that included a multiaxis accelerometer, forward radar, a global positioning system, and a machine-vision lane tracker. Video and driving-performance data were collected continuously for the duration of the studies.15,17
Highly trained analysts used threshold values obtained through a sensitivity analysis of the vehicle-sensor data (e.g., braking at more than 65 gravitational units)16 to identify potential crashes and near-crashes. The operational definition of a crash was any physical contact between the vehicle and another object for which the driver was at fault or partially at fault. (None of the crashes involved a death or serious injury.) The operational definition of a near-crash was any circumstance requiring a last-moment physical maneuver that challenged the physical limitations of the vehicle to avoid a crash for which the driver was at fault or partially at fault.
On the basis of prespecified criteria, we excluded events in which the driver was considered to be not at fault (108 events in the NTDS and 190 events in the 100-Car Study) and in which the driver was observed to be drowsy or under the potential influence of drugs or alcohol (7 events in the NTDS and 113 events in the 100-Car Study). The analyses included 31 crashes and 136 near-crashes among novice drivers and 42 crashes and 476 near-crashes among experienced drivers. Previous analyses have shown that near-crashes are reliable surrogates for crashes.18
Randomly sampled control periods that consisted of 6-second time segments during which the vehicle was moving faster than 5 mph were selected to represent typical or “normal” daily driving conditions. For each driver, sampling for control periods was stratified according to the number of miles the vehicle had traveled (in the NTDS) or the number of hours the person had driven (in the 100-Car Study). Thus, the number of control periods for each driver was proportional to either the distance of travel (e.g., one sample per 50 vehicle miles traveled) or the duration of travel (e.g., two samples per hour driven).17
Two analysts viewed the video footage before each confirmed crash or near-crash and identified and coded secondary tasks. Analysts also viewed the video footage of the randomly sampled control periods and recorded the performance of secondary tasks. The identified secondary tasks were organized according to the 10 categories listed in Table 1TABLE 1Secondary Tasks Observed in the Studies..15Operational definitions of the tasks were identical in the two studies; texting was assessed only in the NTDS, since the 100-Car Study was performed before this activity was widely used.
A secondary task was included if it occurred within the 6-second duration of each sampled control period or within 5 seconds before or 1 second after the onset of the crash or near-crash. Coding continued for 1 second after the onset of the crash or near-crash to capture behaviors that continued because the driver was not aware of the onset of the crash or near-crash.
It was not considered feasible for analysts to be unaware of whether a crash or near-crash occurred, but they were unaware of the purpose of the analyses and recorded many variables in addition to performance of secondary tasks. Any disagreements among analysts were adjudicated by a senior researcher. Interrater reliability, which was determined by comparing the analysts’ assessments of the performance of secondary tasks during control periods with the assessments of a senior researcher, was 88.4% in the 100-Car Study17 and 93.3% in the NTDS (see Tables S1 and S2 of Appendix 1 in the Supplementary Appendix, available with the full text of this article at NEJM.org).
We used a mixed-effects logistic-regression analysis to estimate odds ratios for a crash or near-crash associated with each category of distracting task. We conducted separate regression analyses involving novice drivers and experienced drivers. A random intercept was assigned to each driver to incorporate within-driver correlations.
The prevalence of engagement in a secondary task was calculated per 3-month interval as the percentage of control conditions in which any recorded secondary task was observed. A mixed-effects linear-regression model was used to assess trends for performance of a secondary task over time by both novice and experienced drivers.
The odds ratios and corresponding confidence intervals for a crash or near-crash associated with each secondary task are shown in Table 2TABLE 2Odds Ratio for a Motor Vehicle Crash or Near-Crash Associated with Performance of a Secondary Task.. Among novice drivers, dialing or reaching for a cell phone, texting, reaching for an object other than a cell phone, looking at a roadside object such as a vehicle in a previous crash, and eating were all associated with a significantly increased risk of a crash or near-crash. Among experienced drivers, only cell-phone dialing was associated with an increased risk. Table 1 of Appendix 2 in the Supplementary Appendix shows the prevalence of engagement in secondary tasks as a percentage of crashes and near-crashes and as a percentage of control periods.
As shown in Figure 1FIGURE 1Performance of High-Risk Secondary Tasks among Novice and Experienced Drivers., the prevalence of engagement in a secondary task was estimated as the percentage of randomly sampled control periods in which they occurred. The incidence of high-risk performance of secondary tasks did not change significantly over time among the experienced drivers (P=0.61 for trend). Novice drivers engaged in secondary tasks more frequently over time (P<0.05 for trend). However, overall mean rates of performance of secondary tasks were similar among novice and experienced drivers (9.9% and 10.9%, respectively).
Our analysis showed that the performance of secondary tasks, including dialing or reaching for a cell phone, texting, reaching for an object other than a cell phone, looking at a roadside object, and eating, was associated with a significantly increased risk of a crash or near-crash among novice drivers. Among experienced drivers, only dialing a cell phone was associated with an increased risk; data on secondary tasks performed by experienced drivers were collected before the widespread use of texting. The secondary tasks associated with the risk of a crash or near-crash all required the driver to look away from the road ahead. The prevalence of high-risk performance of secondary tasks was similar overall in the two groups, although it increased among young drivers over the 18-month study period, possibly because of increased confidence in driving over time.
Previous research5,6 involving experienced drivers indicated that cell-phone use (both dialing and talking) was associated with an increase in the risk of a crash by a factor of 4. Our analysis, which separated talking and dialing tasks, showed that talking on a cell phone was not associated with a significant increase in the risk of a crash among novice or experienced drivers, whereas dialing was associated with an increased risk in both groups. In contrast to dialing and other high-risk tasks such as texting and reaching for a cell phone or other object, talking on a cell phone does not require the driver to look away from the road ahead. However, our findings should not be interpreted to suggest that there is no risk associated with this activity, since previous simulation and test-track research has shown that talking on a cell phone reduces attention to visible road hazards and degrades driving performance.10-12 Also, talking on a cell phone can rarely be accomplished without reaching for it and dialing the phone or answering calls, all of which are likely to take the driver’s eyes off the road.
The limitations of our analysis included the relatively small regional samples of study participants. Although the same data-collection methods were used in the two studies, the 100-Car Study data were collected in 2003–2004 in the Washington, D.C., area (where traffic density and crash rates are relatively high) and the NTDS data were collected in 2006–2008 in southwestern Virginia. The methods for sampling the control conditions in the NTDS and 100-Car Study were very similar, but they were not identical. Also, in both studies, the majority of events were near-crashes rather than crashes. In addition, the coding of secondary tasks was subject to possible human error and bias. However, the coding procedures and reliability tests were designed to ensure the most accurate data possible, and the standard for coding secondary tasks before a crash or near-crash required 100% accuracy between two expert analysts, thereby minimizing inconsistencies. Another limitation is that actual crashes were relatively rare and the samples were small; thus, confidence intervals were relatively wide, even with the combination of crashes and near-crashes. Previous research has indicated that combining crash and near-crash events, as compared with the use of crash events alone, produces conservative estimates of risk associated with various behaviors.19
Considerable policy attention that has been directed toward young drivers has primarily resulted in graduated driver licensing. Graduated licensing has been adopted in all 50 states, but there is considerable variation in these state programs. Our finding of the association of several secondary tasks with a significantly increased risk of a crash or near-crash among young drivers provides support for policies limiting the performance of these tasks through graduated licensing requirements or other policy initiatives.
In conclusion, our findings indicate that secondary tasks requiring drivers to look away from the road ahead, such as dialing and texting, are significant risk factors for crashes and near-crashes, particularly among novice drivers.
Supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Highway Traffic Safety Administration.
Drs. Klauer and Guo contributed equally to this article.
We thank Jennifer Mullen for her assistance with data collection and Julie McClafferty, M.S., for her assistance with data coding and reduction.
From the Virginia Tech Transportation Institute (S.G.K., F.G., S.E.L., T.A.D.) and the Department of Statistics, Virginia Polytechnic Institute and State University (F.G.) — both in Blacksburg; the Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD (B.G.S.-M.); and the University of Sherbrooke, Sherbrooke, QC, Canada (M.C.O.).
Most cases of Parkinson’s disease are considered sporadic and idiopathic, although there is evidence of familial aggregation, and several monogenic forms have been identified.1 Recently, several pathogenic mutations in the highly conserved leucine-rich repeat kinase 2 gene (LRRK2) have been associated with autosomal dominant, late-onset Parkinson’s disease.1 Of these, the G2019S substitution is the most frequently reported.2 It occurs in about 1 percent of unselected cases and 3 to 6 percent of familial cases of Parkinson’s disease in persons primarily of European ancestry2 but in 7 of 17 cases of familial disease in persons from North Africa (41 percent).3 At most, 5 carriers among 10,000 persons without Parkinson’s disease have been reported.2-4
We screened 120 unrelated Ashkenazi Jewish patients with Parkinson’s disease in the outpatient setting of the Department of Neurology at the Beth Israel Medical Center in New York City. Specialists in movement disorders performed clinical assessments, and all subjects met stringent diagnostic criteria for Parkinson’s disease.5 Ancestry was determined according to the patients’ self-descriptions, and all but one patient (who reported being 50 percent Sephardic) reported that both parents were Ashkenazic. The Unified Parkinson’s Disease Rating Scale, the Hoehn–Yahr scale, and a diagnostic checklist were completed, and peripheral blood or a cheek swab for DNA analysis was obtained with written informed consent. An Ashkenazi Jewish control group of 317 persons consisted of 113 parents from unrelated families with DYT1 dystonia and 16 from families with dysautonomia, and 188 unrelated Ashkenazi Jewish subjects from the Einstein Aging Study. All were of Ashkenazi Jewish ancestry according to self-report, were examined, and did not have Parkinson’s disease at the time blood was drawn. The institutional review boards of both the Beth Israel Medical Center and the Albert Einstein College of Medicine approved these studies.
DNA was extracted from white cells or buccal cells with the use of standard techniques. The G2019S mutation in LRRK2 (G6055A single-nucleotide polymorphism [SNP] in exon 41), two other coding SNPs, rs1427263 and rs11564148, and five microsatellite markers were genotyped (see the table in the Supplementary Appendix, available with the full text of this letter at www.nejm.org).
Among 120 Ashkenazi Jewish patients with Parkinson’s disease, the LRRK2 G2019S mutation was detected in 22 (18.3 percent; 95 percent confidence interval, 11.9 to 26.4 percent). Of 317 Ashkenazi Jewish controls, 4 were identified as carrying the mutation (1.3 percent; 95 percent confidence interval, 0.34 to 3.2 percent) (odds ratio among the patients, 17.6; 95 percent confidence interval, 5.9 to 52.2; P<0.001). The mutation was present in 11 of 37 subjects with a familial pattern, defined by having at least one affected first-degree, second-degree, or third-degree relative (29.7 percent), and 11 of 83 subjects with no family history of Parkinson’s disease (13.3 percent) (P=0.03). These rates are 15 to 20 times as high as those in most prior reports involving European subjects.2
A common founder mutation has been reported in the European and North African populations.6,7We evaluated allelic association at individual markers surrounding and within the LRRK2 gene.8 For six of the seven markers, the associated allele was the same as reported for the common European–North African haplotype, indicating a common ancestral origin. The apparently high frequency among North African patients with Parkinson’s disease and controls,7 particularly those of Arab ancestry as well as among Ashkenazi Jewish subjects as shown here, suggests a likely Middle Eastern origin for the G2019S mutation. It also establishes once again the Middle Eastern origin of Ashkenazim.
Lifetime penetrance of the G2019S mutation in the Ashkenazi Jewish population was estimated in two ways. First, we used the observed frequency of carriers of the mutation among the patients with Parkinson’s disease and the controls. The relative penetrance for carriers as compared with noncarriers could then be calculated as the odds ratio (17.6). If we assume a lifetime risk of Parkinson’s disease of 2 percent, the lifetime penetrance for those who carried the mutation would be approximately 2×17.6, or 35.2 percent. An alternative estimate of penetrance is obtained by examining the risk of Parkinson’s disease among the parents of the carriers. We found 7 of 44 parents to be so affected. If we assume that half of the parents also carried the mutation, and the 7 cases occurred among these carriers (probably because Parkinson’s disease is uncommon in noncarriers), we obtain a lifetime penetrance of 7÷22, or 31.8 percent. These figures are substantially lower than previous estimates that were calculated on the basis of multigenerational pedigrees.6
The G2019S mutation appears to be an important cause of both familial and sporadic Parkinson’s disease in this group of Ashkenazi Jewish subjects. There was no evidence in the literature that prevalence or familial aggregation of Parkinson’s disease is increased in the Ashkenazi Jewish population as compared with non-Ashkenazi subjects; further epidemiologic study of this population is warranted.
Laurie J. Ozelius, Ph.D.
Geetha Senthil, Ph.D.
Albert Einstein College of Medicine, Bronx, NY 10461
Rachel Saunders-Pullman, M.D., M.P.H.
Erin Ohmann, B.S.
Amanda Deligtisch, M.D.
Michele Tagliati, M.D.
Ann L. Hunt, D.O.
Beth Israel Medical Center, New York, NY 10003
Christine Klein, M.D.
University of Lübeck, 23538 Lübeck, Germany
Beth Israel Medical Center, New York, NY 10003
Susan M. Hailpern, M.S., M.P.H.
Richard B. Lipton, M.D.
Albert Einstein College of Medicine, Bronx, NY 10461
Jeannie Soto-Valencia, B.A.
Beth Israel Medical Center, New York, NY 10003
Neil Risch, Ph.D.
University of California at San Francisco, San Francisco, CA 94143
Susan B. Bressman, M.D.
Beth Israel Medical Center, New York, NY 10003
Dr. Tagliati reports having received consulting fees from Schwarz Pharma, Boehringer Ingelheim, and Novartis and lecture fees from Medtronic Neurological, Novartis, GlaxoSmithKline, and Boehringer Ingelheim; and Dr. Klein, lecture fees from Boehringer Ingelheim.Read More