Parkinson’s disease: an inquiry into the etiology and treatment

Parkinson’s disease affects over one million people in the United States. Although there have been remarkable advances in uncovering the pathogenesis of this disabling disorder, the etiology is speculative. Medical treatment and operative procedures provide symptomatic relief only. Compression of the cerebral peduncle of the midbrain by the posterior cerebral artery in a patient with Parkinson’s Disease (Parkinson’s Disease) was noted on magnetic resonance imaging (MRI) scan and at operation in a patient with trigeminal neuralgia. Following the vascular decompression of the trigeminal nerve, the midbrain was decompressed by mobilizing and repositioning the posterior cerebral artery The patient's Parkinson's signs disappeared over a 48-hour period. They returned 18 months later with contralateral peduncle compression. A blinded evaluation of MRI scans of Parkinson's patients and controls was performed. MRI scans in 20 Parkinson's patients and 20 age and sex matched controls were evaluated in blinded fashion looking for the presence and degree of arterial compression of the cerebral peduncle. The MRI study showed that 73.7 percent of Parkinson's Disease patients had visible arterial compression of the cerebral peduncle. This was seen in only 10 percent of control patients (two patients, one of whom subsequently developed Parkinson’s Disease); thus 5 percent. Vascular compression of the cerebral peduncle by the posterior cerebral artery may be associated with Parkinson’s Disease in some patients. Microva-scular decompression of that artery away from the peduncle may be considered for treatment of Parkinson’s Disease in some patients

Accounting for seasonal patterns in syndromic surveillance data for outbreak detection

BACKGROUND: Syndromic surveillance (SS) can potentially contribute to outbreak detection capability by providing timely, novel data sources. One SS challenge is that some syndrome counts vary with season in a manner that is not identical from year to year. Our goal is to evaluate the impact of inconsistent seasonal effects on performance assessments (false and true positive rates) in the context of detecting anomalous counts in data that exhibit seasonal variation. METHODS: To evaluate the impact of inconsistent seasonal effects, we injected synthetic outbreaks into real data and into data simulated from each of two models fit to the same real data. Using real respiratory syndrome counts collected in an emergency department from 2/1/94–5/31/03, we varied the length of training data from one to eight years, applied a sequential test to the forecast errors arising from each of eight forecasting methods, and evaluated their detection probabilities (DP) on the basis of 1000 injected synthetic outbreaks. We did the same for each of two corresponding simulated data sets. The less realistic, nonhierarchical model's simulated data set assumed that "one season fits all," meaning that each year's seasonal peak has the same onset, duration, and magnitude. The more realistic simulated data set used a hierarchical model to capture violation of the "one season fits all" assumption. RESULTS: This experiment demonstrated optimistic bias in DP estimates for some of the methods when data simulated from the nonhierarchical model was used for DP estimation, thus suggesting that at least for some real data sets and methods, it is not adequate to assume that "one season fits all." CONCLUSION: For the data we analyze, the "one season fits all " assumption is violated, and DP performance claims based on simulated data that assume "one season fits all," for the forecast methods considered, except for moving average methods, tend to be optimistic. Moving average methods based on relatively short amounts of training data are competitive on all three data sets, but are particularly competitive on the real data and on data from the hierarchical model, which are the two data sets that violate the "one season fits all" assumption

Improving access for community health and sub-acute outpatient services: protocol for a stepped wedge cluster randomised controlled trial

BACKGROUND: Waiting lists for treatment are common in outpatient and community services, Existing methods for managing access and triage to these services can lead to inequities in service delivery, inefficiencies and divert resources from frontline care. Evidence from two controlled studies indicates that an alternative to the traditional &quot;waitlist and triage&quot; model known as STAT (Specific Timely Appointments for Triage) may be successful in reducing waiting times without adversely affecting other aspects of patient care. This trial aims to test whether the model is cost effective in reducing waiting time across multiple services, and to measure the impact on service provision, health-related quality of life and patient satisfaction. METHODS/DESIGN: A stepped wedge cluster randomised controlled trial has been designed to evaluate the impact of the STAT model in 8 community health and outpatient services. The primary outcome will be waiting time from referral to first appointment. Secondary outcomes will be nature and quantity of service received (collected from all patients attending the service during the study period and health-related quality of life (AQOL-8D), patient satisfaction, health care utilisation and cost data (collected from a subgroup of patients at initial assessment and after 12&nbsp;weeks). Data will be analysed with a multiple multi-level random-effects regression model that allows for cluster effects. An economic evaluation will be undertaken alongside the clinical trial. DISCUSSION: This paper outlines the study protocol for a fully powered prospective stepped wedge cluster randomised controlled trial (SWCRCT) to establish whether the STAT model of access and triage can reduce waiting times applied across multiple settings, without increasing health service costs or adversely impacting on other aspects of patient care. If successful, it will provide evidence for the effectiveness of a practical model of access that can substantially reduce waiting time for outpatient and community services with subsequent benefits for both efficiency of health systems and patient care.<br /

Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

<p>Abstract</p> <p>Background</p> <p>Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.</p> <p>Methods</p> <p>Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios.</p> <p>Result</p> <p>The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.</p> <p>Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected.</p> <p>Conclusion</p> <p>The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.</p

Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p

Validation of Syndromic Surveillance for Respiratory Pathogen Activity

The studied respiratory syndromes are suitable for syndromic surveillance because they reflect respiratory pathogen activity pattern

Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance

BACKGROUND: Concern over bio-terrorism has led to recognition that traditional public health surveillance for specific conditions is unlikely to provide timely indication of some disease outbreaks, either naturally occurring or induced by a bioweapon. In non-traditional surveillance, the use of health care resources are monitored in "near real" time for the first signs of an outbreak, such as increases in emergency department (ED) visits for respiratory, gastrointestinal or neurological chief complaints (CC). METHODS: We collected ED CCs from 2/1/94 – 5/31/02 as a training set. A first-order model was developed for each of seven CC categories by accounting for long-term, day-of-week, and seasonal effects. We assessed predictive performance on subsequent data from 6/1/02 – 5/31/03, compared CC counts to predictions and confidence limits, and identified anomalies (simulated and real). RESULTS: Each CC category exhibited significant day-of-week differences. For most categories, counts peaked on Monday. There were seasonal cycles in both respiratory and undifferentiated infection complaints and the season-to-season variability in peak date was summarized using a hierarchical model. For example, the average peak date for respiratory complaints was January 22, with a season-to-season standard deviation of 12 days. This season-to-season variation makes it challenging to predict respiratory CCs so we focused our effort and discussion on prediction performance for this difficult category. Total ED visits increased over the study period by 4%, but respiratory complaints decreased by roughly 20%, illustrating that long-term averages in the data set need not reflect future behavior in data subsets. CONCLUSION: We found that ED CCs provided timely indicators for outbreaks. Our approach led to successful identification of a respiratory outbreak one-to-two weeks in advance of reports from the state-wide sentinel flu surveillance and of a reported increase in positive laboratory test results

Be careful with triage in emergency departments: interobserver agreement on 1,578 patients in France

<p>Abstract</p> <p>Background</p> <p>For several decades, emergency departments (EDs) utilization has increased, inducing ED overcrowding in many countries. This phenomenon is related partly to an excessive number of nonurgent patients. To resolve ED overcrowding and to decrease nonurgent visits, the most common solution has been to triage the ED patients to identify potentially nonurgent patients, i.e. which could have been dealt with by general practitioner. The objective of this study was to measure agreement among ED health professionals on the urgency of an ED visit, and to determine if the level of agreement is consistent among different sub-groups based on following explicit criteria: age, medical status, type of referral to the ED, investigations performed in the ED, and the discharge from the ED.</p> <p>Methods</p> <p>We conducted a multicentric cross-sectional study to compare agreement between nurses and physicians on categorization of ED visits into urgent or nonurgent. Subgroups stratified by criteria characterizing the ED visit were analyzed in relation to the outcome of the visit.</p> <p>Results</p> <p>Of 1,928 ED patients, 350 were excluded because data were lacking. The overall nurse-physician agreement on categorization was moderate (kappa = 0.43). The levels of agreement within all subgroups were variable and low. The highest agreement concerned three subgroups of complaints: cranial injury (kappa = 0.61), gynaecological (kappa = 0.66) and toxicology complaints (kappa = 1.00). The lowest agreement concerned two subgroups: urinary-nephrology (kappa = 0.09) and hospitalization (kappa = 0.20). When categorization of ED visits into urgent or nonurgent cases was compared to hospitalization, ED physicians had higher sensitivity and specificity than nurses (respectively 94.9% versus 89.5%, and 43.1% versus 30.9%).</p> <p>Conclusions</p> <p>The lack of physician-nurse agreement and the inability to predict hospitalization have important implications for patient safety. When urgency screening is used to determine treatment priority, disagreement might not matter because all patients in the ED are seen and treated. But using assessments as the basis for refusal of care to potential nonurgent patients raises legal, ethical, and safety issues. Managed care organizations should be cautious when applying such criteria to restrict access to EDs.</p