In the molecular world, researchers act as detectives working hard to unravel the mysteries surrounding cells. One of the researchers' greatest tools in this endeavor has been Raman spectroscopy. Raman spectroscopy is a spectroscopic technique that measures the unique Raman spectra for every type of biological molecule. As such, Raman spectroscopy has the potential to provide scientists with a library of spectra that can be used to unravel the makeup of an unknown molecule. However, this technique is limited in that it is not able to manipulate particular structures without disturbing their unique environment. Recently, a novel technology that combines Raman spectroscopy with optical tweezers, termed Raman tweezers, evades this problem due to its ability to manipulate a sample without physical contact. As such, Raman tweezers has the potential to become an incredibly effective diagnostic tool for differentially distinguishing tissue, and therefore holds great promise in the field of virology for distinguishing between various virally infected cells. This review provides an introduction for a virologist into the world of spectroscopy and explores many of the potential applications of Raman tweezers in virology. In today's world of increasingly complex and refined biological analytical techniques, spectroscopy has maintained its place at the forefront. One type of spectroscopy in particular, Raman spectroscopy, has proven especially useful in providing detailed analysis of a staggering variety of biological samples. Raman spectroscopy is able to detect and analyze extremely small molecular objects with high resolution while eliminating outside interference .Recently, a derivative of Raman spectroscopy, termed Raman tweezers, has allowed for an even greater degree of analytical capability. Raman tweezers use optical tweezers to suspend and manipulate a molecule without direct contact, so that the molecule's Raman spectra may be recorded while it is in its most natural state. As such, the spectra collected are more reflective of the true nature of the molecule under study and therefore of more significance. Even with today's advances, we are only beginning to scratch the surface of a technique that holds the promise of far-reaching and highly significant future applications.One such field that stands to benefit greatly from Raman tweezers is virology. The high resolution, lack of sample preparation, and very short data collection time required make the technology ideal for use in the study of viruses and virally infected cells. However, because of the newness of the approach, this review has been written in such a manner that those unfamiliar with optical physics not become lost and lose interest in a technology that holds such incredible potential.Spectroscopy was born in 1801, when the British scientist William Wollaston discovered the existence of dark lines in the solar spectrum. Thirteen years later, Jospeh von Fraunhofer repeated Wollaston's work and hypothesized that the dark lines were caused by an absence of certain wavelengths of light . It was The end of the nineteenth and beginning of the twentieth centuries was marked by significant efforts to quantify and explain the origin of spectral phenomena. Beginning with the simplest atom, hydrogen, scientists including Johann Balmer and Johannes Rydberg developed equations to explain the atom's frequency spectrum. It was not until Niels Bohr developed his famous model in 1913 that the energy levels of the hydrogen spectrum could accurately be calculated. However, Bohr's model failed miserably when applied to other elements that had more than one electron. It took the development of quantum mechanics by Werner Heisenberg and Erwin Schrodinger in 1925 to universally explain the spectra of most elements .From the discovery of unique atomic spectra developed modern spectroscopy. The three main varieties of spectroscopy in use today are absorption, emission, and scattering spectroscopy. Absorption spectroscopy, including Infrared and Ultraviolet spectroscopy, measures the wavelengths of light that a substance absorbs to give information about its structure. Emission spectroscopy, such as fluorescence and laser spectroscopy, measures the amount of light of a certain wavelength that a substance reflects. Lastly, scattering spectroscopy, to which Raman spectroscopy belongs, is similar to emission spectroscopy but detects and analyzes all of the wavelengths that a substance reflects upon excitation .Raman spectroscopy is named after the famous Indian physicist Sir Chandrasekhara Venkata Raman who in 1928, along with K.S. Krishnan, found that when a beam of light transverses a transparent chemical compound, a small fraction of that beam will emerge from the compound at right angles to and of a different wavelength from the original beam . Raman rNormally, when a beam of light is shined through a transparent substance, the molecules of the substance that absorb those light wavelengths are excited into a partial quantum state  and emit wavelengths of equal frequency as the incoming wavelengths such that there is no net change in energy between the light and the substance. Such light wavelengths are said to be elastically scattered in a process known as Rayleigh scattering . On rareRaman spectroscopy is performed by illuminating a sample with a laser. The reflected light is collected with a lens and sent through a monochromator that typically employs holographic diffraction gratings and multiple dispersion stages to achieve a high degree of resolution of the desired wavelengths . A chargWhile initial Raman spectroscopy was unable to analyze most biological samples due to the interference from the background fluorescence of water, buffers, and/or mediums present in the sample, two new types of Raman spectroscopy have been developed that solve this problem. Both types, near-infrared (NIR) and ultraviolet (UV) Raman spectroscopy, rely on using wavelengths well away from those of fluorescence. Near-infrared Raman spectroscopy relies on long near-infrared wavelengths while ultraviolet Raman spectroscopy relies on short wavelengths to avoid interference from mid-wavelength fluorescence, as shown in figure There are four major types of Raman spectroscopy in use today: surface enhanced Raman spectroscopy (SERS), resonance Raman spectroscopy (RRS), confocal Raman microspectroscopy, and coherent anti-Stokes Raman scattering (CARS) . SERS, wThe second two types of Raman spectroscopy, confocal Raman microspectroscopy and coherent anti-Stokes Raman scattering (CARS) are not only able to analyze nearly all biological samples, but also avoid any fluorescent interference. Both confocal Raman microspectroscopy and CARS spectroscopy get around this problem of fluorescence in unique ways. Confocal Raman microspectroscopy eliminates any lingering fluorescence by measuring the Raman spectra of micro regions of a sample one at a time such that the effects of fluorescence are eliminated while high resolution is maintained . BecauseWith the issue of background fluorescence solved, Raman spectroscopic analysis has become an analytical method of choice in an extremely wide range of biological applications. Some of the more obscure applications of this technique include everything from determining the molecular structure of the skin of a 5200 year old frozen man to the analysis and authentication of foods such as olive oil and Japanese sake . One of in vivo without the need of fixatives, thereby providing extremely detailed analysis of cells in their natural state [Of even greater consequence, perhaps, has been Raman spectroscopy's contribution to detailed cellular analysis. Modern techniques have allowed for the Raman spectroscopic analysis of cells al state . Such anal state ,26. For al state -29. AddiOf particular interest has been the application of Raman spectroscopy in medicine. The technique's ability to provide detailed images of cells has allowed for the comparative analysis between numerous healthy tissues and their diseased states. Such analytical potential has been especially suited in the diagnosis of numerous cancers, including: intestinal, stomach, laryngeal, brain, breast, mouth, skin, and others -37. OtheRecently, Raman spectroscopy has been coupled with modern fiber optic technology to accurately measure tissue spectra in vivo without the need of biopsy. This method employs a small fiber optic probe that both has the capability to reach less assessable organs and only requires less than two seconds to collect spectra . As suchThe use of Raman spectroscopy in differential medicine is not limited to tissues and cells; it also has applications in virology. The technique has been put to good use in determining the structures and stereochemistry of both the protein and nucleic acid components of viruses, even going so far as to being able to distinguish between different types of right-handed DNA alpha-helixes -53. RamaThe analytical capabilities of Raman spectroscopy are limited by its inability to manipulate, and therefore thoroughly analyze the biological molecules under study without making physical contact. This limitation has been resolved by coupling Raman spectroscopy with a technology called optical tweezers. The new method, termed Raman tweezers, uses optical tweezers to manipulate a sample without contact with it so that it remains unchanged for Raman spectroscopic analysis.Raman tweezers is a relatively new technology that couples Raman spectroscopy with optical tweezers to achieve previously unheard of sample control and resolution. Optical tweezers is a system that focuses a near-infrared laser on a sample to fix it in an optical trap from which it may then be maneuvered and controlled. The technique, which was first developed by Arthur Ashkin et al. in 1986, has the ability to control objects ranging in size from 5 nm to over 100 mm, whether they be atoms, viruses, bacteria, proteins, cells, or other biological molecules ,58. Perhin vivo tissue analyses. Despite this drawback, Raman tweezers is a highly useful marriage of Raman spectroscopy and optical tweezers that further enhances Raman spectroscopy's analytical capabilities.The one major drawback of using Raman tweezers instead of Raman spectroscopy, however, is its inability to be used with fiber optic probes and therefore be applied to The potential of Raman tweezers is staggering. The technique holds all the promise of Raman spectroscopy, including the potential to identify almost any biological molecule and disease, and adds to it both a greater level of control and analytical capability as well as the capability of observing a sample in its natural state. As such, Raman tweezers is likely to surpass Raman spectroscopy in use for biological analysis.in vivo under different conditions [in vivo tissue analysis, its ability to manipulate a sample without physically coming into contact with it has allowed a degree of detailed analysis not possible with Raman spectroscopy alone.To date, only a handful of biological molecules and processes, including red blood cells, lipoproteins, cell membrane components and T cell activation, have been studied with Raman tweezers -65. Notanditions . Raman tnditions ,67-69. TRaman Tweezers, while yet far having proven itself an enlightening diagnostic tool in virology, is still in its infancy. With proper nurturing, this technique has the potential to blossom into a truly brilliant and highly useful tool in the virologist's arsenal. As the resolution of Raman spectrographs increases, so will their analytical capabilities. It is likely in the not too distant future, that this technology will allow scientists to go beyond their current capability of distinguishing infected from healthy cells to being able to distinguish between differentially infected cells. Given a detailed library of spectra, a researcher could potentially even be able to characterize an unknown virus' structure, components, and lytic or latent state of infection. Furthermore, the technique's optical tweezers would allow for the study of the more temperamental cell lines, such as 293, that die more easily upon physical contact. All of these analytical capabilities would give the virologist a much clearer window to study viruses.One could also use this technique's capabilities to not only characterize a virus, but also monitor the efficacy of antiviral treatments and determine viral load, among other applications. While all of these potential applications can be done today through alternative means, these processes must be completed separately and are time consuming. Raman tweezers greatly simplifies this process by providing a comprehensive analytical system that is both able to collect all the necessary data at once and able to do so in a very short time, thereby making it extremely cost effective. The process is so fast in fact Table  that theIn conclusion, Raman tweezers is an extremely powerful analytical tool that provides biologists with a fingerprint of the agent they are studying and whose immense future applications are only now being fully understood. It is up to virologists, however, to realize the full scope and magnitude of these applications and to press for the development of this seemingly unrelated technology in virology.The author(s) declare that they have no competing interests.SMA conceived the idea, designed the outline, coordinated the project, and helped to draft this review. PJL, AGW, and OFD collected intellectual materials towards different sections of the review. In addition, PJL was instrumental in writing the first draft. All authors read and approved the final version of the manuscript.


The cause of chest pain in patients presenting to the emergency room often remains unclear. We present a case of essential thrombocythemia as a novel cause of atypical chest pain, which responded dramatically to a simple treatment intervention.A 54-year-old patient presenting with atypical chest pain was found to have essential thrombocythemia as a cause for her chest pain. She responded dramatically to aspirin therapy and had no recurrence of symptoms over 3 months.Essential thrombocythemia should be considered as a differential cause in patient presenting with atypical chest pain, vasomotor symptoms and high platelet counts. These symptoms are generally more bothersome than dangerous and are usually controlled by low dose aspirin therapy. Acute chest pain (CP) is one of the most common reasons for ER visits and its management involves establishing the cause while excluding potentially life-threatening conditions. It, therefore, poses a significant diagnostic challenge. Despite extensive work up to determine the cause of chest pain, the diagnosis often remains unclear leaving both the patient and the physician unsatisfied. We present a case of atypical chest pain due to a relatively unremarkable cause; Essential thrombocythemia (ET), which responded dramatically to a simple treatment intervention.9/l to 750 × 109/l over the last one year. Initial laboratory investigations in the ER revealed normal basic metabolic panel and complete blood count except for a platelet count of 935 × 109/l. Her chest pain was evaluated with three sets of cardiac enzymes and exercise stress echocardiogram which were all normal. Her chest X-ray did not reveal any infiltrates or pneumothorax. CT scan of chest with contrast showed no evidence of pulmonary embolism or aortic dissection. Her EKG revealed normal sinus rhythm. Her 24 hour continuous telemetry also did not show any arrhythmias. Her atypical, non-localizing, sensory symptoms with negative cardiopulmonary work up were subsequently attributed to vasomotor manifestations due to ET. The patient was reassured and started on a low dose aspirin therapy. At 12 weeks follow up, she reported no subsequent similar episodes.A 54-year-old Caucasian female presented to ER with episodes of CP for the last 4 hours. The pain was characterised as acute onset, sharp, non-exertional, non-radiating pain localised to left side of the chest. It was 10/10 in severity with no discernable aggravating or relieving factors with each episode lasting for 2-3 minutes. Certain sensory symptoms had preceded the onset of CP. This comprised of sudden onset numbness of the left thumb followed by a tingling sensation in the shoulders with radiation distally into both hands. Subsequently, patient also developed severe, generalized, headache along with lights flashing across her eyes. By the time she reached the ER, her symptoms had completely resolved. Her past medical history was significant only for the diagnosis of essential thrombocythemia (ET) based on a bone marrow biopsy and a positive JAK2 mutation test. She had not required any previous treatment for this condition. Her baseline platelet count had ranged between 600 × 10Essential thrombocythemia (ET) is a clonal stem cell disorder characterized by a persistent, nonreactive thrombocythemic state that is not accounted for by any of the other chronic myeloproliferative disorders .Our patient presented with classic vasomotor manifestations of ET, which may be present in approximately half of the patients with this myeloproliferative disorder. The common vasomotor symptoms experienced by these patients include headache, palpitations, atypical chest pain, distal paresthesias  or transWhereas the work up of acute chest pain to rule out potentially life threatening conditions is essential, the recognition and appropriate treatment of relatively unremarkable causes of chest pain like ET can relieve the patients' symptoms and may also reduce the excessive utilization of health care resources.ASA: acetylsalicylic acid; CP: chest pain; CT: computer tomography; ER: emergency room; ET: essential thrombocythemia; EKG: electrocardiogram.Written informed consent was obtained from the patient for publication of this case report and accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.The author(s) declare that they have no competing interests.KP assembled, analyzed and interpreted the patient data regarding the hematological disease. All authors contributed to writing the manuscript. All authors read and approved the final manuscript.


There is an error in the citation and copyright statement. The correct citation is: Tirado-González I, Barrientos G, Freitag N, Otto T, Thijssen VLJL, et al. (2012) Uterine NK Cells Are Critical in Shaping DC Immunogenic Functions Compatible with Pregnancy Progression. PLoS ONE 7(10): e46755. doi:10.1371/journal.pone.0046755The correct copyright is: © Tirado-González et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


The United States Public Health Service (USPHS) Guideline for Treating Tobacco Use and Dependence includes ten key recommendations regarding the identification and the treatment of tobacco users seen in all health care settings. To our knowledge, the impact of system-wide brief interventions with cigarette smokers on smoking prevalence and health care utilization has not been examined using patient population-based data.Data on clinical interventions with cigarette smokers were examined for primary care office visits of 104,639 patients at 17 Harvard Vanguard Medical Associates (HVMA) sites. An operational definition of “systems change” was developed. It included thresholds for intervention frequency and sustainability. Twelve sites met the criteria. Five did not. Decreases in self-reported smoking prevalence were 40% greater at sites that achieved systems change . On average, the likelihood of quitting increased by 2.6%  per occurrence of brief intervention. For patients with a recent history of current smoking whose home site experienced systems change, the likelihood of an office visit for smoking-related diagnoses decreased by 4.3% on an annualized basis after systems change occurred . There was no change in the likelihood of an office visit for smoking-related diagnoses following systems change among non-smokers.The clinical practice data from HVMA suggest that a systems approach can lead to significant reductions in smoking prevalence and the rate of office visits for smoking-related diseases. Most comprehensive tobacco intervention strategies focus on the provider or the tobacco user, but these results argue that health systems should be included as an integral component of a comprehensive tobacco intervention strategy. The HVMA results also give us an indication of the potential health impacts when meaningful use core tobacco measures are widely adopted. The United States Public Health Service (USPHS) Guideline for Treating Tobacco Use and Dependence includes ten key recommendations regarding the identification and the treatment of tobacco users seen in all health care settings While research clearly shows that systems-level changes can reduce smoking prevalence among enrollees of managed health care plans This study took place at Harvard Vanguard Medical Associates, a large health care provider network based in eastern Massachusetts. HVMA has more than 20 offices primarily in Boston and the surrounding suburban areas providing primary and specialty health care to more than 400,000 patients. In 2007, HVMA leadership established a clinical quality goal “to intervene” with patients who smoke. A multidisciplinary design team comprised of clinical and administrative personnel, defined “intervention” as identification of cigarette smokers at every office visit and delivery of a brief intervention to each identified smoker during that office visit. HVMA used a team approach to complete the equivalent of the PHS Guideline recommended “5A” tobacco intervention . There are many international correlates to the 5A model. For example, the National Health Service Stop Smoking program in the United Kingdom recommends a 4A model . In New Zealand, the system is titled ABC which stands for Ask, Brief advice, and Cessation support. What all these models have in common is that the recommended physician interventions are brief (<10 minutes) and that they include offers of counseling as well as prescriptions for tobacco cessation medications.The data recorded at HVMA focused exclusively on cigarette smokers instead of the broader definition of tobacco users. See In this work flow, the medical assistant was charged with recording in the EHR smoking status during each office visit and to assess readiness to quit. The clinician was responsible for advising each smoker to stop and for assisting each smoker according to his/her stage of change. Decision support tools for clinicians were embedded into the EHR to promote use of evidence-based medications to strengthen quit attempts. An option to refer smokers to a community-based, state-funded “stop-smoking service” that provides free telephone counseling could be ordered through the EHR. Advance Practice Clinicians (Nurse Practitioners and Physician Assistants) were trained to provide counseling and were educated on the use of stop-smoking medications. Many intervention sites identified a tobacco champion to lead the work locally. Feedback reports of medical assistant performance were delivered to clinical staff and administrative supervisors monthly.De-identified encounter level data for all primary care office visits for all adult patients at 17 HVMA sites was prepared by analysts from Harvard Vanguard Medical Associates (HVMA). Records covered the period from 1/1/2005 through 11/30/2010. Evaluation plans were reviewed and approved by the Institutional Review Board for the Massachusetts Department of Public Health. Harvard Vanguard obtains written consent from patients for the type of analysis conducted here. The consent form includes the following 2 statements.Harvard Vanguard may use or disclose your Health Information in order to conduct its business of providing health care. These “health care operations” may include quality assessment, training of medical students, credentialing and various other activities that are necessary to run our practice and to improve the quality and cost effectiveness of the care that we deliver to you. Some of these activities occur in conjunction and cooperation with other Atrius Health groups. Other of these business operations may be performed by outside parties (“Business Associates”) on Harvard Vanguard’s behalf. Our Business Associates must agree to maintain the confidentiality of your Health Information.Harvard Vanguard may disclose your Health Information for public health activities.The legal Department at Harvard Vanguard carefully reviewed this project and determined that the analysis fell within the realm of public health work related to quality improvement.The de-identified data set prepared by HVMA analysts contained demographics and encounter level data for 310,577 adult patients. Demographics for each patient included a randomly defined patient ID, age, race/ethnicity, marital status, town of residence, and patient’s “home” clinic site. All HVMA patients have a “home site” which is the location of their primary care provider. Since patients can change doctors and/or move from one home site to another, the “home” for this data set was the clinic site associated with the patient’s primary care provider on 11/30/2010. Data for all office visits at 17 HVMA office sites between 1/3/2005 and 11/30/2010 also were prepared. Included in the office visit encounter data were the unique patient ID, a primary and four secondary diagnoses, and recorded components of the brief intervention with cigarette smokers. For the 310,577 unique patients, there were 2,561,782 unique single-day patient encounters in the data set prepared by HVMA analysts. Nearly all (96.6%) interventions with cigarette smokers occurred at a patient’s home site.To be included in our analysis, patients had to be Massachusetts residents between the ages of 22 and 64 on 11/30/2010, who were screened for smoking status at least once. At least 3 years had to elapse between the first and last office visit. The requirement that patients be at least 22 years of age by 11/30/2010 was to ensure that all patients in our data set were at least 18 when the first HVMA site began to systematically intervene with cigarette smokers (1/1/2007). Although some children were screened for smoking status by HVMA, the intervention program for cigarette smokers was aimed almost exclusively at adults. The upper age limit was set to 64 because it was thought that older patients might be receiving a greater proportion of their care outside the primary care system. Seasonal residents and college students might also receive care outside the primary care system, but it was impossible to screen only for full-time/non-college residents with the data available.In the original data set, 104,639 of the 310,377 patients met the criteria described above. Of this total, 15,286 had some history of self-reported smoking recorded in the EHR between 1/3/2005 and 11/30/2010 while 89,353 had no recorded history of current smoking in that time.When recorded, smoking status was listed as either “Yes”, “Quit”, “Never”, “Passive” or “Not Asked.” Since the “Passive” and “Not Asked” categorizations could not be used to specifically define a patient’s use of cigarettes, these categories were ignored. Therefore, smoker identification for this study was defined as an office visit where smoking status was listed as “Yes”, “Quit”, or “Never.”The recorded smoking status was attached to the dated office visit. This date was not overwritten as is often the case when smoking status is stored in a patient’s social history. As a result, the full complement of patient encounter records would have a discontinuous but longitudinal history of smoking status. In addition to smoking status, information could be recorded about a patient’s interest in quitting, readiness to quit, smoking pattern, referrals to telephone counseling, and prescriptions for medications covered by insurance.For this study, a brief intervention with smokers was defined as any evidence at a specific visit that the conversation about cigarette smoking went beyond the identification of smoking status. This could include information about interest in quitting, readiness to quit, smoking pattern, referrals to telephone counseling, and prescriptions for medications covered by insurance. Therefore, any visit in which a patient was identified as a smoker could also include a brief intervention about smoking.To assess data quality, we focused on all office visits throughout the data set where a patient’s smoking status was recorded as “Never.” Of the three primary categories of smoking status , “Never” is the only absolute classification. Logically, no visit where status is recorded as “Never” should have any other status recorded at a prior visit. To obtain our quality assessment score, we counted the number of times that the smoking status was also recorded as “Never” where smoking status was also recorded at an earlier visit. Next, we computed the percentage of the earlier visits in which smoking status was also recorded as “Never.” For the 384,338 visits at which smoking status was recorded as “Never”, 381,917 (99.4%) also had smoking status recorded as “Never” at the earlier visit.To our knowledge there is no common standard for defining systems change using real-world office encounter records. Operationally, we defined “health systems change” as first month when more than half of all office visits at a given site included an identification for cigarette smoking. In all months following that date, the rate of cigarette smoker identifications could never drop below 50%. Furthermore, there had to be at least 12 consecutive months with rates above 50%. By this definition, 12 of the 17 HVMA sites had achieved “systems change.”Changes in self-reported smoking behavior were examined by computing the proportion of all patients who were recorded as smokers at the earliest possible visit and then comparing this to the proportion of all patients recorded as smokers at the latest possible visit. Group comparisons were made between sites that achieved systems change and those that did not.Changes in the rate of smoking-related office visits were analyzed using generalized estimating equations (GEEs) with a logistic link function and patient as the unit of analysis. The period between 1/3/2005 and 11/30/2010 was divided into 77 twenty-eight day segments for 104,639 patients –15,286 patients with histories of recent smoking and 89,353 patients with no recent history of self-reported smoking. The dependent variable was the presence or absence of a smoking-related office visit during the 28-day period. If any of the first five recorded ICD9 codes for any visit in a 28-day period matched the list of smoking-related diagnoses from the Surgeon General’s 2004 report on Smoking and Health To avoid biasing the results, data prior to and including the period of the first recorded visit were not included in the analysis. Patients were divided into those who had some history of self-reported smoking between 1/5/2005 and 11/30/2010 and those who had no history of self-reported smoking. Any patient who reported current smoking at any visit between 1/5/2005 and 11/30/2010 was grouped with the smokers. Longitudinal data for smokers and non-smokers were evaluated separately.There were three temporal variables in each model: time since 1/1/2005, time since the first recorded office visit, and time since systems change occurred at the patient’s home site. This last variable was the primary focus of our analysis. We hypothesized that there would be decrease in the rate of smoking-related office visits following systems change and that this effect would only be seen in patients with a recent history of self-reported smoking.Our model adjusted for the seasonality of office visits using sines and cosines As defined above, “systems change” occurred at 12 of the 17 sites between 1/1/2007 and 4/1/2009. For all 12 sites, there was a dramatic and significant increase in the identification rate of cigarette smokers after the date of achieving systems change. All but one site achieved an 80% identification rate within 9 months of that date. The median time between the date of “systems change” and an 80% identification rate was just 4.5 months. At 11 of the 12 sites, there was also a significant increase in the percentage of rate of brief intervention for identified cigarette smokers. Identification rates for the remaining 5 sites remained relatively low throughout the study period. At sites that achieved systems change, 82.5% of visits where patients were identified as smokers included evidence of a further clinical intervention. At sites that did not achieve systems change, this rate was only 59.4%. The proportion of home-site interventions was slightly higher for patients seen at the 12 sites where systems change took place (97.2% versus 93.1%).Most demographics were similar for smokers and non-smokers. However, patients with a recent history of self-reported smoking were more likely to be younger, male, of white race and live alone. Smokers had a significantly higher average number of office visits .Changes in smoking prevalence were examined by focusing on patient visits where smoking status was recorded. Of the 104,639 patients in our study group, 13,517 (12.9%) were current smokers at the first visit where smoking status was recorded. On the last visit where smoking status was recorded, 11,817 (11.3%) were current smokers. Overall, there were 1,700 (12.6%) fewer smokers at the last visit. The decrease in self-reported smoking prevalence was 40% larger at the 12 sites that achieved systems change . As one would expect given our operational definition of systems change, patients received more clinical interventions about cigarette smoking at sites that achieved systems change .The impact per encounter of brief clinical intervention on the likelihood of quitting was examined using a logistic model. The outcome variable was the final recorded smoking status for a patient . The model included as predictors the total number of office visits where the patient’s smoking status was recorded and the number of office visits where smoking status was not recorded. The analysis was restricted to 1,255 patients who had at least 4 years of between the first and last visit, at least 3 years between the first and last confirmation, and at least one visit in which smoking status was recorded as “Yes.” Each encounter where a smoker’s smoking status was recorded increased the likelihood of quitting by 2.6% .Most office visits did not include a smoking-related diagnosis code. On average, smokers had 0.90 smoking related office visits throughout the time period studied while non-smokers had an average of 0.69 visits with a smoking-related diagnosis code. Changes in the rate of smoking-related office visits were computed using generalized estimating equations (GEEs). Separate models were developed for patients with recent histories of self-reported smoking and all other patients. We refer to these groups as smokers and non-smokers. The independent variable of primary interest was the time since systems change occurred at the patient’s home site.After adjusting for temporal effects, seasonality, previous visit pattern, flu-related visits, the date of health reform, and cigarette taxes, the annualized rate of smoking-related office visits following systems change for smokers decreased by 4.3% (95% CI: 0.5%–8.1%). The difference from the unadjusted to the adjusted rates is likely due to the fact that several of the independent variables had strong positive relationships with the dependent variables . For non-smokers, there was a non-significant decrease  in the annualized rate of smoking-related office visits following systems change (95% CI: −0.4% to 2.0%). See To explore whether demographics could explain the reduction in the rate of smoking related office visits, six primary and interaction terms were added to the smoker and non-smoker models. The primary terms were the six demographic variables shown in An operational definition of systems change was established for clinical interventions with cigarette smokers. This definition included thresholds for frequency and sustainability. Based on this definition, 12 of 17 HVMA sites achieved systems change between 1/1/2007 and 12/1/2009. These 12 sites had significant increases in rates of smoker identifications and further clinical interventions with smokers. Decreases in smoking prevalence were found across all sites; however, the reduction in prevalence was 40% greater at sites achieving systems change. We estimate that each clinical intervention with a smoker increased the likelihood of quitting by 2.4%. The likelihood of an office visit for a smoking-related diagnosis also decreased but only for smokers at sites that achieved systems change (4.3%). Among non-smokers, there was no significant change in the rate of office visits for smoking-related office visits following systems change. Patient demographics did not appear to strongly affect the likelihood of a smoking related office visit following systems change.The health care system should be viewed as central to the any tobacco intervention strategy. As recommended in the USPH Guideline, health care administrators like practice managers and chief medical officers, as much as individual clinicians, must be responsible for ensuring that tobacco interventions become an integrated component of health care delivery. Yet, despite the well-known consequences of tobacco use and conclusive research on the effectiveness of tobacco treatment, many healthcare facilities still lack the policies and clinical systems needed to achieve consistent and effective treatment. However, this landscape is changing rapidly. Recent federal legislation, including PPACA, ARRA (American Recovery and Reinvestment), and HITECH  include provisions that incentivize physician providers and hospitals increasingly to identify tobacco users, assess use, and conduct interventions The HVMA data support and shine the spotlight on strategies in healthcare that focus on the system, rather than the individual clinician. In the case with HVMA, data captured in the EHR is retrieved and reported back monthly. Administrators and clinicians  are informed about their own performance with comparisons to other sites. This analysis also demonstrates that data sharing with clinicians may go beyond the rate of brief tobacco interventions and enter the realm of behavioral change and improvements of patient population health.In contrast to strategies that target only the clinician or the tobacco user, with systems strategies, tobacco use interventions are likely to become a fully integrated and routine part of patient care. Bolstered increasingly by meaningful use of EHRs, they may become easier to perform than not. If results such as those realized within HVMA can be replicated across the primary care delivery system, significant strides can be made towards reducing tobacco use prevalence and improving health.A number of limitations should be noted. Although we endeavored to assess data quality, no independent measure of quality was available and thus the inaccuracy of the electronic medical records may lead to variability as well as potential bias in the analysis. The size of this potential issue cannot be known. However, our test of internal consistency showed that patients who were recorded as never having smoked were also listed as never smokers 99.7% of the time at prior visits. Furthermore, the sites that had increased rates of brief intervention also had simultaneous decreases in the number of smokers and the likelihood of office visits for smokers. This is consistent with literature showing relationships between likelihood of quitting and tobacco use interventions with medical doctors. Had the data quality been poor, it is unlikely that this relationship would have existed in the HVMA patient histories.This analysis also relied on patient self-report smoking status that is subject to reporting bias, especially among certain populations like pregnant women. The self-report bias may have affected the estimates of smoking prevalence, but unlikely to have affected the estimates of pre-post changes. The percentage of women with ICD9 diagnosis codes (V22) for pregnancies varied across sites from 2% to 5%. There was no discernible pattern between these percentages and sites that achieved systems change.Without any measure of continuity of care, we also can’t know whether patients visited non-HVMA providers for any period of time. We attempted to deal with the limitation by requiring that at least 3 years elapse between the first and last visit for all patients in our study group. We have no reason to assume that patients who sought routine care elsewhere and then returned at a later date to receive care at HVMA would bias the results in any way.Similarly, the likelihood of office visits for smoking-related diagnoses could be impacted by patients seeing non-HVMA providers for their care. Smokers, in particular, have more health problems and may require care of specialists or more ED or hospital visits for smoking-related diagnoses. Furthermore, there is extensive literature on what has been called the “ill-quitter effect” or “quitting while sick” Future research in this area should also examine data sets collected in settings other than the primary care setting. Without a better understanding of changes in hospitalization rates, it would be impossible to claim that there have been significant health improvements or to develop adequate return on investment estimates for brief tobacco intervention in real world settings. Nonetheless, the success of the HVMA program of brief tobacco use interventions demonstrates the value of system-wide adoption of MU core tobacco measures. When systems routinely meet the MU criteria, there will be real opportunities to improve healthcare quality, among them tailored feedback systems to motivate clinicians, new ways to identify and address health disparities, and development of payment systems that tie bonuses to reliable measures of improving population health.The forces driving healthcare in the United States to adopt a systems approach to tobacco interventions are quite large. These include significant federal legislation , tobacco related meaningful use rules, and the move toward Accountable Care Organizations, Alternative Quality Contracts, and Value Based Purchasing. With these tailwinds, the rates of tobacco interventions in the United States are likely to increase significantly in the coming years, ultimately leading to substantial savings from the decreased utilization of health care services related to tobacco use.


Depression increases the risk of disability pension and represents a health related strain that pushes people out of the labour market. Although early voluntary retirement is an important alternative to disability pension, few studies have examined whether depressive symptoms incur early voluntary retirement. This study examined whether depressive symptoms and changes in depressive symptoms over time were associated with early retirement intentions.We used a cross-sectional (n = 4041) and a prospective (n = 2444) population from a longitudinal study on employees of the Danish eldercare sector. Depressive symptoms were measured by the Major Depression Inventory and the impact of different levels of depressive symptoms  and changes in depressive symptoms  on early retirement intentions were analysed with multinomial logistic regression.In the cross-sectional analysis all levels of depressive symptoms were significantly associated with retirement intentions before the age of 62 years. Similar associations were found prospectively. Depressive symptoms and worsened depressive symptoms in the two year period from baseline to follow-up were also significantly associated with early retirement intentions before age 62. The prospective associations lost statistical significance when controlling for early retirement intentions at baseline.The whole spectrum of depressive symptoms represents a health related strain that can incur intentions to retire early by early voluntary retirement. In order to change the intentions to retire early, the work related consequences of depressive symptoms should be addressed as early in the treatment process as possible. The ageing work force challenges the economy of most Western countries and calls for new strategies to maintain people in the work force. Depression is a common mental disorder , 2 and iEVR can be defined as early retirement by either self-financed or publicly financed non-illness based pensions. Publicly financed EVR is available in a number of Western countries and is generally available at the age of 60 years or earlier . Publicle.g., being female and having a spouse that is retired have been associated with EVR [e.g., experienced health and medical problems [Both ‘pull’ and ‘push’ factors can help explain people’s incentives to retire early . Factorswith EVR . Factorsproblems , 23, incproblems  or unempproblems , 20. In i.e., people experiencing symptoms not severe enough to reach the threshold of a clinical depression.Depression is mostly characterised by an episodic course, but there is a high risk of relapse . The cori.e., people with depression were more likely to retire early than people without depression. By contrast, a study of within-person changes would measure whether changes in depression in the same person from one point in time to another affected early retirement [These prospective studies were based on between-group differences, tirement . Within-Early retirement plans are likely to change over the course of one’s life and can be viewed as a transitional process. A process which often begins with considerations and early retirement intentions then proceeds to a decision to retire early and eventually ends with actual retirement , 36. MosIs the severity of depressive symptoms associated with immediate early retirement intentions?Is the severity of depressive symptoms associated with early retirement intentions over a two-year period?Are changes in depressive symptoms associated with early retirement intentions over a two-year period?Do depressive symptoms affect changes in retirement intentions over time?This study examined the association between depressive symptoms and early retirement intentions in four research questions:This study used data from a prospective study of eldercare workers health and work characteristics . QuestioIn the cross-sectional study population, we included participants who responded at T2 (n = 8431). We excluded those participants who were below 45 or above 59 years at T2 (n = 3490) and those who had missing values (n = 145), or who answered “I don’t know” to the question about early retirement intentions (n = 755). The final cross-sectional population amounted to 4041. We did not have information on actual retirement, but in order to account for the possible bias of people who left the population because of EVR the participants were excluded at age 60 at T2. In Denmark, most employees can receive publicly financed EVR at the age of 60 (Danish: Efterløn) and the publicly financed old age pension at age 65 (Danish: Folkepension). Due to high Danish taxes on income self-financed retirement before the age of 60 is rare.i.e., above 59 years at T2, n = 2336) and who had turned 60 years at T2 (n = 11). We also excluded participants, who had missing values (n = 25), or answered “I don’t know” (n = 390) to the question about early retirement intentions. The final prospective population consisted of 2444 employees.In the prospective study population, we included those participants who responded at both T1 and T2 (n = 5206). We excluded participants who were below 45 or above 57 years old at T1 . The individual responses to the items are summed up to a severity scale that ranges from 0 to 50 - the higher the score the more severe the depressive symptoms. A cut-off score of ≥20 indicate a probable major depression. We categorised the MDI scores at T1 into five different categories indicating the severity of depressive symptoms:‘Severe depressive symptoms’ (scores ≥20);‘Moderately severe depressive symptoms’ (scores between 15 and 19);‘Moderate depressive symptoms’ (scores between 10 and 14);‘Mild depressive symptoms’ (scores between 5 and 9);‘No depressive symptoms’ (scores between 0 and 4).We measured depressive symptoms by the Major Depression Inventory (MDI). The MDI is a widely used self-rating scale to asses depression  accordinAlthough a self-report questionnaire is able to indicate the presence of a clinical depression, a valid diagnosis can only be made by clinical assessment performed by a health care professional . We therSeverity of depressive symptoms at T1 and changes in depressive symptoms from T1 to T2 were used as predictor variables in the longitudinal analyses. Changes in depressive symptoms had three categories: “Worsened”, “improved” or “unaffected” symptoms. Participants were classified with “worsened symptoms” if their MDI score had increased five points or more and with “improved symptoms” if their MDI-score had decreased by five points or more. All other participants were classified with “unaffected symptoms”.“When would you like to retire from the labour market? Five response categories indicated age of intended retirement: “I would like to work until I turn 65 years old”, “I would like to receive early retirement pension, when I am between 62 and 65 years old”, “I would like to receive early retirement pension, when I am between 60 and 62 years old”, “I would like to retire, before I turn 60 years old” or, “I don’t know” These were recoded into three categories:The intention to retire early was measured with a single item: I would like to retire before the age of 60’ and ‘when I am between 60 and 62 years old’;1. Very early retirement intentions, which included the responses: ‘I would like to retire when I am between 62 and 64 years old’;2. Early retirement intentions, which included the response: ‘I would like to work until I turn 65 years old’;3. Normal retirement intentions, which included the response: ‘I don’t know” were excluded from the study populations, as described previously.Participants who answered “χ2).Statistical analysis was conducted in PASW 18 . Using multinomial logistic regression for the cross-sectional and prospective analyses, we examined the association of depressive symptoms with the probability of very early and early retirement intentions. A p-value of <0.05 was regarded as statistically significant in these analyses. The cross-sectional analysis was adjusted for gender, age, marital status, working hours, seniority and type of occupation. We tested two models in the prospective analyses. Model 1 was adjusted for the same variables as the cross-sectional analysis. Model 2 was further adjusted for early retirement intentions at T1. The drop-out analyses were conducted using Pearson chi-square . Of this sample, 37 % (n = 541) did not return the questionnaire because they no longer worked at the particular workplace. Chi-square tests revealed that these non-responders were more likely to be married (p < 0.05), be male (p < 0.001), and have less years of work experience (p < 0.001) compared to the responders from the prospective study population. No differences between depressive symptoms, age, shift work, or type of occupation were found.Chi-square tests revealed that the non-responders who remained at the same work place (n = 926) were more likely to be male (p < 0.001), to be younger (p < 0.05), have less years of work experience (p < 0.05), and to have more severe depressive symptoms (p < 0.001) compared to the responders from the selected prospective study population. No differences between marital status, shift work, or type of occupation were found.Chi-square tests revealed that the participants who were excluded because they had answered “I don’t know” to the question about early retirement intentions from both the cross-sectional and longitudinal study populations were significantly younger (p < 0.05) than the study participants, but did not significantly differ with regards to depressive symptoms, gender, marital status, shift work, or work experience.The results from the cross-sectional analysis showed that all levels of elevated depressive symptoms doubled the chance of very early retirement intentions (before age 62), but not early retirement intentions (between ages 62 and 64). The same association was also found when examined over time, except that ‘severe depressive symptoms’ did not significantly affect intentions to retire before age 62. In addition, those who experienced worsened depressive symptoms over time were also more likely to have very early retirement intentions compared to those with no change in depressive symptoms. However, when adjusting for early retirement intentions at T1 in the longitudinal analyses, the results were no longer significant.Our findings are in line with previous studies showing that depressive symptoms increase the risk of early retirement by either EVR , 37, 38 We were also able to examine the association between depressive symptoms and changes in retirement intentions from T1 to T2. The prospective associations were no longer statistically significant when adjusting for retirement intentions at T1, regardless of the severity of depressive symptoms at T1 or whether the depressive symptoms had worsened or improved. A significant result after adjusting for retirement intentions at T1 would require that the retirement intentions increased from T1 to T2 among those participants who already had depressive symptoms and early retirement intentions at T1. Our finding indicated that the association between depressive symptoms and retirement intentions remained the same or was weakened in the two-year period.In accordance with other recent studies, our study indicated that the presence of even mild depressive symptoms had significant work related consequences , 32, 44,It remains unknown whether the impact of depressive symptoms that are below the threshold of a clinical depression on early retirement should be understood as the effect of residual symptoms from a previous depression or as symptoms that reoccur independently. Our results in either case show that it is important to consider the whole spectrum of depression when analysing the impact of depression on EVR over time.Our results may have been confounded by ‘push’ and ‘pull’ factors at T1. Studies have identified several factors that play an important role in both early retirement intentions and actual early retirement, such as an adverse working environment , 37, 38,Another important limitation of this study was the selective attrition. The response rate from the original study was fairly high , but we Our attrition analyses showed that 37 % of non-responders no longer worked at the particular work place. Early retirement before the age of 60 is rare in Denmark , 48, 49 The study population consisted of mainly female eldercare workers, an occupational group that is known for reporting a lower quality of the work environment compared to other sectors . Thus, iThe whole spectrum of depressive symptoms represents a health related strain that can influence EVR. Our findings suggest that the severity of depressive symptoms can have an immediate impact on the intentions to retire early and that these intentions are maintained, but weakened over time. These findings also suggest that in order to change the intentions to retire early, the work related consequences of depressive symptoms should be addressed as early in the treatment process as possible.