Accurate Counting Research Articles

Multisite Qualification of an Automated Incubator and Colony Counter for Environmental and Bioburden Applications in Pharmaceutical Microbiology.

Traditional microbiological techniques have been used for well over a century as the basis for contamination testing of pharmaceutical products and processes. With more recent focus on faster product release and concerns around the integrity of the test data, new technologies have been implemented to detect and enumerate organisms faster and provide paperless processes to minimize data integrity issues. Manual colony counting technologies, where incubation is performed in a standard incubator, and the plate is manually transferred to the colony counter for a single read at the end of incubation, have been used for many years to reduce the potential for human error; however, they pose validation challenges due to poor counting accuracy. Colony counters that automatically perform both the incubation and enumeration functions (multiple enumeration calculations through the incubation phase) have recently been implemented for quality control (QC) laboratory analytical processes, supporting a cGMP environment. This article summarizes the findings of eight companies demonstrating the qualification of an automated colony counter technology to perform the majority of microbial tests required for QC, environmental monitoring, and bioburden for in-process, bulk drug substance, and water system testing. Comparable analytical performance and time to result data generated during individual studies at all companies allows the system to be qualified and implemented for cGMP processes while reducing data integrity risks.

Accurate Determination of Colorectal Polyp Counts Is Not Mission Impossible for Pathologists and Is Essential for Surveillance Interval Decision-Making: A Review of 1294 Specimens

Abstract Introduction/Objective The US Multi-Society Task Force on colorectal cancer updated recommendations in 2020 for follow-up after colonoscopy. This emphasized differential surveillance intervals based on the number of polyps removed. We determined how often we accurately counted the number of polyps and what submission practices allowed accurate quantification. Methods/Case Report We reviewed consecutive colorectal polyp cases from 10/1 to 12/31, 2019, excluding inflammatory polyps in inflammatory bowel disease. Pathology reports were reviewed to determine if the polyp number could be determined from history, gross description, and histologic findings. When numbers did not match or were unclear, endoscopy reports and glass slides were reviewed. Results (if a Case Study enter NA) 601 cases with 1294 specimens were identified. We accurately determined polyp number in 1235 (95.4%) specimens. 1072 (82.8%) specimens were submitted as single polyp per container (SP), while 222 (17.2%) were submitted with > 1 polyp per container (MP). Polyp number was not quantifiable in 58 (26.1%) MP due to 11 labeled as “multiple polyps” in requisition/endoscopy reports and 47 submitted with polyp number different from number of tissue portions seen grossly and microscopically. In 1 (0.1%) SP, polyp number was unclear because separate portions of tissue contained two different diagnoses. Rate of accurate polyp count is significantly different between SP and MP (p<0.01) by Fisher exact test. Conclusion We accurately determined number of polyps in most specimens (95.4%) because our gastroenterologists usually submit SP. In MP, polyp count in 26.1% of specimens was unquantifiable. This could lead to uncertainty in surveillance follow-up intervals. Therefore, we recommend submitting one polyp per container.

Estimating the Prevalence of De Novo Monogenic Neurodevelopmental Disorders from Large Cohort Studies.

Rare diseases impact up to 400 million individuals globally. Of the thousands of known rare diseases, many are rare neurodevelopmental disorders (RNDDs) impacting children. RNDDs have proven to be difficult to assess epidemiologically for several reasons. The rarity of them makes it difficult to observe them in the population, there is clinical overlap among many disorders, making it difficult to assess the prevalence without genetic testing, and data have yet to be available to have accurate counts of cases. Here, we utilized large sequencing cohorts of individuals with rare, de novo monogenic disorders to estimate the prevalence of variation in over 11,000 genes among cohorts with developmental delay, autism spectrum disorder, and/or epilepsy. We found that the prevalence of many RNDDs is positively correlated to the previously estimated incidence. We identified the most often mutated genes among neurodevelopmental disorders broadly, as well as developmental delay and autism spectrum disorder independently. Finally, we assessed if social media group member numbers may be a valuable way to estimate prevalence. These data are critical for individuals and families impacted by these RNDDs, clinicians and geneticists in their understanding of how common diseases are, and for researchers to potentially prioritize research into particular genes or gene sets.

Machine learning for enumeration of cell colony forming units

As one of the most widely used assays in biological research, an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process. To speed up the colony counting, a machine learning method is presented for counting the colony forming units (CFUs), which is referred to as CFUCounter. This cell-counting program processes digital images and segments bacterial colonies. The algorithm combines unsupervised machine learning, iterative adaptive thresholding, and local-minima-based watershed segmentation to enable an accurate and robust cell counting. Compared to a manual counting method, CFUCounter supports color-based CFU classification, allows plates containing heterologous colonies to be counted individually, and demonstrates overall performance (slope 0.996, SD 0.013, 95%CI: 0.97–1.02, p value < 1e-11, r = 0.999) indistinguishable from the gold standard of point-and-click counting. This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.

IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring

The violation traffic laws by driving at high speeds, the overloading of passengers, and the unfastening of seatbelts are of high risk and can be fatal in the event of any accident. Several systems have been proposed to improve passenger safety, and the systems either use the sensor-based approach or the computer-vision-based approach. However, the accuracy of these systems still needs enhancement because the entire road network is not covered; the approaches utilize complex estimation techniques, and they are significantly influenced by the surrounding environment, such as the weather and physical obstacles. Therefore, this paper proposes a novel IoT-based traffic violation monitoring system that accurately estimates the vehicle speed, counts the number of passengers, and detects the seatbelt status on the entire road network. The system also utilizes edge computing, fog computing, and cloud computing technologies to achieve high accuracy. The system is evaluated using real-life experiments and compared with another system where the edge and cloud layers are used without the fog layer. The results show that adding a fog layer improves the monitoring accuracy as the accuracy of passenger counting rises from 94% to 97%, the accuracy of seatbelt detection rises from 95% to 99%, and the root mean square error of speed estimation is reduced from 2.64 to 1.87.

Application of Automatic Colony Counter in the Detection of Dairy Products

Objective: To improve the counting efficiency and realize the traceability of raw data. The applicability, accuracy, precision and repeatability of the automatic colony counter were verified by experiments, Determine the applicability of colony count in the detection of dairy products and raw and auxiliary materials. Methods: The plate pouring method and test piece method are selected for verification test, including psychrophilic bacteria, aerobic spores, thermophilic aerobic spores, lactic acid bacteria count, total number of colonies, coliform, mold, yeast, Staphylococcus aureus and other items. The manual count is compared with the automatic colony counter to determine its feasibility, applicability and accuracy. Results: The colony count of normal Petri dish without background interference was compared with that of 3M test piece. The paired t-test was carried out by visual method and instrument method, <I>P </I>= 0.17, greater than 0.05. There was no significant difference in statistical results, and the colored medium had no significant effect on automatic counting. The counting effect of the instrument is poor when there are needle shaped colonies, the color of colonies is close to that of the culture medium, the contour of colonies is not obvious, the colonies spread greatly and the edges of colonies are irregular, there are food particles and colonies in the culture medium at the same time, or the color of colonies is close to that of food particles, which can not meet the accuracy requirements. Conclusion: For samples with uniform texture, the accuracy and precision of automatic colony counting are relatively good, the work efficiency is high, the operation time of experimental personnel is saved, and the original experimental data and images can be stored in the instrument for a long time. It is a counting method worthy of popularization in the laboratory. However, for the samples and culture medium with interference in the test verification, appropriate measures can be taken to change the counting conditions and manually correct the counting results of the instrument, but the counting efficiency will be reduced, and the laboratory can choose to use it according to the situation.

Differentially Private Triangle Counting in Large Graphs

Triangle count is a critical parameter in mining relationships among people in social networks. However, directly publishing the findings obtained from triangle counts may bring potential privacy concern, which raises great challenges and opportunities for privacy-preserving triangle counting. In this paper, we choose to use differential privacy to protect triangle counting for large scale graphs. To reduce the large sensitivity caused in large graphs, we propose a novel graph projection method that can be used to obtain an upper bound for sensitivity in different distributions. In particular, we publish the triangle counts satisfying the node-differential privacy with two kinds of histograms: the triangle count distribution and the cumulative distribution. Moreover, we extend the research on privacy preserving triangle counting to one of its applications, the local clustering coefficient. Experimental results show that the cumulative distribution can fit the real statistical information better, and our proposed mechanism has achieved better accuracy for triangle counts while maintaining the requirement of differential privacy.

Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy.

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9µm polystyrene particles were measured under flow and signal quality improved by6.09dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by7.50dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration.

Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image

Leaf age is an important trait in the process of maize (Zea mays L.) growth. It is significant to estimate the seed activity and yield of maize by counting leaves. Detection and counting of the maize leaves in the field are very difficult due to the complexity of the field scenes and the cross-covering of adjacent seedling leaves. A method was proposed in this study for detecting and counting maize leaves based on deep learning with RGB images collected by unmanned aerial vehicles (UAVs). The Mask R-CNN was used to separate the complete maize seedlings from the complex background to reduce the impact of weeds on leaf counting. We proposed a new loss function SmoothLR for Mask R-CNN to improve the segmentation performance of the model. Then, YOLOv5 was used to detect and count the individual leaves of maize seedlings after segmentation. The 1005 field seedlings images were randomly divided into the training, validation, and test set with the ratio of 7:2:1. The results showed that the segmentation performance of Mask R-CNN with Resnet50 and SmoothLR was better than that with LI Loss. The average precision of the bounding box (Bbox) and mask (Mask) was 96.9% and 95.2%, respectively. The inference time of single image detection and segmentation was 0.05 s and 0.07 s, respectively. YOLOv5 performed better in leaf detection compared with Faster R-CNN and SSD. YOLOv5x with the largest parameter had the best detection performance. The detection precision of fully unfolded leaves and newly appeared leaves was 92.0% and 68.8%, and the recall rates were 84.4% and 50.0%, respectively. The average precision (AP) was 89.6% and 54.0%, respectively. The rates of counting accuracy for newly appeared leaves and fully unfolded leaves were 75.3% and 72.9%, respectively. The experimental results showed the possibility of current research on exploring leaf counting for field-grown crops based on UAV images.

Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning

AbstractPopulation monitoring is essential to management and conservation efforts for migratory birds, but traditional low‐altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost‐prohibitive. Therefore, automated methods for counting are critical to cost‐efficient application of remote sensing for wildlife surveys covering large areas. We conducted nocturnal surveys of sandhill cranes (Antigone canadensis) during spring migration in the Central Platte River Valley of Nebraska, USA, using midwave thermal infrared sensors. We developed a framework for automated counting of sandhill cranes from thermal imagery using deep learning, assessed and compared the performance of two automated counting models, and quantified the effect of spatial resolution on counting accuracy. Aerial thermal imagery data were collected in March 2018 and 2021; 40 images were analyzed. We applied two deep learning models: an object detection approach, Faster R‐CNN and a recently developed pixel‐density estimation approach, ASPDNet. Model performance was determined using data independent of the training imagery. The effect of spatial resolution was quantified with a beta regression on relative error. Our results showed model accuracy of 9% mean percent error for ASPDNet and 18% for Faster R‐CNN. Most error was related to the undercounting of sandhill cranes. ASPDNet had &lt;50% of the error of Faster R‐CNN as measured by mean percent error, root‐mean‐squared error and mean absolute error. Spatial resolution affected accuracy of both models, with error rate increasing with coarser resolution, particularly with Faster R‐CNN. Deep learning models, particularly pixel‐density estimators, can accurately automate counting of migratory birds in a dense, aggregate setting such as nocturnal roosting sites.

Enhanced Detection of Surface Plasmon Resonance Microscope via Multiangle Illumination and Pretrained Deep Neural Network

Surface plasmon resonance microscopy (SPRM) has proved to be a powerful tool for label‐free imaging, detection, and mass/size measurement of single isolated virus, cells, and nanoparticles. Herein, SPRM that can be extended to detect the dense nanoparticles, such as counting the number density of nanoparticles, is demonstrated. To improve the counting accuracy, multiangle illumination is adopted in a conventional SPRM, then a deep learning method based on pretrained neural network approach is proposed to deal with the large number of SPRM images. Numerical simulations show that the new approach can significantly enhance the accuracy in detecting the number densities of nanoparticles. The ability to precisely count the number of particles is important for the monitor and detection of environmental pollution because that the number density of the particulate matter is strongly related to the air quality and atmospheric visibility.

A novel and robust method for counting components within bio-molecular complexes using fluorescence microscopy and statistical modelling

Cellular biology occurs through myriad interactions between diverse molecular components, many of which assemble in to specific complexes. Various techniques can provide a qualitative survey of which components are found in a given complex. However, quantitative analysis of the absolute number of molecules within a complex (known as stoichiometry) remains challenging. Here we provide a novel method that combines fluorescence microscopy and statistical modelling to derive accurate molecular counts. We have devised a system in which batches of a given biomolecule are differentially labelled with spectrally distinct fluorescent dyes (label A or B), and mixed such that B-labelled molecules are vastly outnumbered by those with label A. Complexes, containing this component, are then simply scored as either being positive or negative for label B. The frequency of positive complexes is directly related to the stoichiometry of interaction and molecular counts can be inferred by statistical modelling. We demonstrate this method using complexes of Adenovirus particles and monoclonal antibodies, achieving counts that are in excellent agreement with previous estimates. Beyond virology, this approach is readily transferable to other experimental systems and, therefore, provides a powerful tool for quantitative molecular biology.

Picture-based acne lesion counts: A validation study to assess accuracy and reliability of acne lesion counts via photography.

Acne lesion counting (ALC) is widely used to evaluate efficacy of new acne treatments. Although such evaluations are precise and highly discriminative, if assessed live, it can be a time-consuming and intrusive measurement. Photographic assessment is a viable mode for ALCs and for training and/or qualifying evaluators. The purpose of this study was to validate photographic methods for performing ALCs and to provide an objective measurement tool to train and/or qualify lesion count evaluators for deployment in both small- and large-scale studies. Assess accuracy and reliability of acne lesion counting via photographic methods in 8 subjects aged 16 to 40 years, with Fitzpatrick Skin Types I to VI. Frontal and 45°angle images taken at the single in-clinic visit. Each subject underwent 3 counts per 4 evaluators: 1 set of live counts and 2 sets of photo counts. Intra-evaluator and inter-evaluator reliability measures for photo counts were evaluated using calculations of intraclass correlation coefficients (ICCs). Eight subjects (2 males and 6 females) age between 16 and 40 (min 16 years, max 25 years) diagnosed with facial acne vulgaris participated in the study. Fitzpatrick Skin Types ranged from III to V (three Type-III, four Type-IV, and one Type-V). ICC values for intra-evaluator reliability were found to be >0.95 for each evaluator, and ICC for inter-evaluator reliability was found to be 0.98. Photographic lesion count methodology is a reliable and accurate tool for objective measurement of ALCs and additionally, for training and/or qualifying evaluators.

Prospective study of the technology for evaluating and measuring in-row seed spacing for precision planting: A review

Corn is the most cultivated and consumed cereal in the world. The overall objective of this review was to study the methodologies to measure and evaluate the in-row seed spacing for precision planting as well as to determine the technological alternatives that would allow obtaining information about seed mapping for corn crop planting in precision agriculture applications. As a conceptual synthesis about the electronic measurement system, there are two strategies for determining in-row seed spacing in the precision planting. Indirect methods correspond to the measurement before the seeds reach the furrow, while direct methods correspond to the measurement with the seeds placed in the furrow. The indirect measurement strategy is the most widely used in research publications and commercial planter monitors. Within this method, the seed spacing measurement systems use optical or radio wave type seed sensors. Corn seed counting accuracy through electronic measurement systems with optical-type seed sensor is at least 96%. The microwave seed sensor is used commercially by a few companies whose technologies are patented. The direct measurement strategy is under development and requires further research. The main limitation of these technologies is the seed detection in the furrow, which limits the planter travel speed and the equipment cost. The conceptual proposal for the term ‘seed mapping’ is to provide integrated and geo-referenced information on in-row seed spacing and depth for precision planting.

Comparison of the Prediction Accuracy of Total Viable Bacteria Counts in a Batch Balloon Digester Charged with Cow Manure: Multiple Linear Regression and Non-Linear Regression Models

Biogas technology is rapidly gaining market penetration, and the type of digesters employed in the harnessing of the biogas from biodegradable waste is crucial in enhancing the total viable bacteria counts. This study focused on the exploration of input parameter (number of days, daily slurry temperature, and pH) and target (total viable bacteria counts) datasets from anaerobic balloon digester charged with cow manure using data acquisition system and standard methods. The predictors were ranked according to their weights of importance to the desired targets using the reliefF test. The complete dataset was randomly partitioned into testing and validated samples at a ratio of 60% and 40%, respectively. The developed non-linear regression model applied on the testing samples was capable of predicting the yield of the total viable bacteria counts with better accuracy as the determination coefficient, mean absolute error, and p-value were 0.959, 0.180, and 0.602, respectively, as opposed to the prediction with the multiple linear regression model that yielded 0.920, 0.206, and 0.514, respectively. The 2D multi-contour surface plots derived from the developed models were used to simulate the variation in the desired targets to each predictor while the others were held constant.

Video-Based Vehicle Counting and Analysis using YOLOv5 and DeepSORT with Deployment on Jetson Nano

In recent years, the advancements in deep learning and high-performance edge-computing systems have increased tremendously and have become the center of attention when it comes to the analysis of video-based systems on edge by making use of computer vision techniques. Intelligent Transportation Systems (ITS) is one area where deep learning can be used for several tasks including highway-based vehicle counting systems whereby making use of computer vision techniques, an edge computing device and cameras installed in specific locations on the road, we are able to obtain very accurate vehicle counting results and replace the use of traditional and laborious hardware devices with modern low-cost solutions. This paper proposes and implements a modern, compact, and reliable vehicle counting system based on the most recent and popular object detection algorithm as of writing this paper, known as the YOLOv5, combined with a state-of-the-art object tracking algorithm known as DeepSORT. The YOLOv5 will be used in the following system to detect and classify four different classes of vehicles, whereas DeepSORT will be used to track those vehicles across different frames in the video sequence. Finally, a unique and efficient vehicle counting method will be implemented and used to count tracked vehicles across the highway scenes. A new highway vehicle dataset consisting of four vehicle classes, namely: car, motorcycle, bus, and truck, was collected, cleaned, and annotated with a total of 11,982 images published in the following study and used for the training of our robust vehicle detection model. From the results obtained over real-world highway surveillance videos, the following system was able to obtain an average vehicle detection mAP score of 96.1% and a vehicle counting accuracy of 95.39%, all while being able to be deployed on a compact Nvidia Jetson Nano edge-computing device with an average speed of 15 FPS which outperforms other previously proposed tools in terms of both accuracy and speed.

Convolution Network Enlightened Transformer for Regional Crop Disease Classification

The overarching goal of smart farming is to propose pioneering solutions for future sustainability of humankind. It is important to recognize the image captured for monitoring the growth of plants and preventing diseases and pests. Currently, the task of automatic recognition of crop diseases is to research crop diseases based on deep learning, but the existing classifiers have problems regarding, for example, accurate identification of similar disease categories. Tomato is selected as the crop of this article, and the corresponding tomato disease is the main research point. The vision transformer (VIT) method has achieved good results on image tasks. Aiming at image recognition, tomato plant images serve as this article’s data source, and their structure is improved based on global ViT and local CNN (convolutional neural network) networks, which are built to diagnose disease images. Therefore, the features of plant images can be precisely and efficiently extracted, which is more convenient than traditional artificial recognition. The proposed architecture’s efficiency was evaluated by three image sets from three tomato-growing areas and acquired by drone and camera. The results show that this article method garners an average counting accuracy of 96.30%. It provides scientific support and a reference for the decision-making process of precision agriculture.

A State of Art Review on Methodologies of Occupancy Estimating in Buildings from 2011 to 2021

Occupancy information is important to building facility managers in terms of building energy efficiency, indoor environmental quality, comfort conditions, and safety management of buildings. When combing the distribution characteristics of the literature, it is found that the field of estimating occupancy counts is a very active area. Researchers from various countries have undertaken extensive explorations to obtain more research results. In this survey, the commonly used occupancy measurement systems and algorithms are described. Through the analysis and research of different occupancy measurement systems and algorithms, their advantages, disadvantages, and limitations are summarized, so that researchers can use them selectively. As for how to choose the method of estimating occupancy counts, suggestions are given in terms of the range of people, accuracy, cost, and privacy. There are still many pressing issues relating to high-density crowd occupancy counting, complex environmental impact, and system robustness. According to the current research progress and technology development trend, the possible future research directions are pointed out. The innovation of this review is the quantitative analysis of the selection of occupancy measurement systems for different ranges of people, and the occupancy counting accuracy situation of different measurement systems and algorithms. It provides more informed opinions on the selection of practical applications. It can be used by other researchers as a starting point for their research and/or project work.

Reliability of Inflammatory Markers as a Diagnostic Tool for Non-Diabetic Foot and Ankle Infections: Case Series Study

Category: Other; Ankle; Basic Sciences/Biologics; Hindfoot; Midfoot/Forefoot; Trauma Introduction/Purpose: The distinction between foot and ankle wound healing complications as opposed to infection is crucial for the appropriate and efficacious allocation of antibiotic therapy. Multiple reports have focused on the diagnostic accuracy of different inflammatory markers, however, mainly in the diabetic population. Our aim was to evaluate the diagnostic accuracy of white cell count (WCC) and C-reactive protein (CRP) as diagnostic tools for this distinction in the non-diabetic cohort. Methods: Data were reviewed from a prospectively maintained Infectious Disease Unit database of 216 patients admitted at Leicester University Hospitals - the United Kingdom with musculoskeletal infections over the period between July 2014 and February 2020 (68 months). All patients with a confirmed diagnosis of diabetes were excluded while only those with a confirmed microbiological or clinical diagnosis of foot or ankle infection were included in our study. For the included patients, we retrospectively retrieved the inflammatory markers (WCCs and CRP) at the time of presentation and during the perioperative period for debridement. Values of CRP 0-10 mg/L and WCC 4.0-11.0 x109 /L were considered normal. Results: After the exclusion of patients with confirmed diabetes, 25 patients with confirmed foot or ankle infections were included. All infections were confirmed microbiologically with positive intra-operative culture results. 7 (28%) patients with osteomyelitis (OM) of the foot, 11 (44%) with OM of the ankle, 5 (20%) with ankle septic arthritis, and 2 (8%) patients with post- surgical wound infection were identified. Previous bony surgery was identified in 52% (n=13), either a corrective osteotomy or an open reduction and internal fixation (ORIF) for a foot or ankle fracture with the infection developing on top of the existing metalwork. 84% (n=21) did have raised inflammatory markers while 16% (n=4) failed to mount an inflammatory response even with subsequent debridement and removal of metalwork. CRP sensitivity was 84%, while WCC sensitivity was only 28%. Conclusion: CRP has relatively good sensitivity in the diagnosis of foot and ankle infections in non-diabetic patients, whereas WCC is a poor inflammatory marker in the detection of such cases. In presence of a clinically high level of suspicion of foot or ankle infection, a normal CRP should not rule out the diagnosis of OM.

Comparing Measures Of Functional Difficulty With Self-Identified Disability: Implications For Health Policy.

The Affordable Care Act mandated data collection standards to identify people with disabilities in federal surveys to better understand and address health disparities within this population. Most federal surveys use six questions from the American Community Survey (ACS-6) to identify people with disabilities, whereas many international surveys use the six-item Washington Group Short Set (WG-SS). The National Survey on Health and Disability (NSHD), which focuses on working-age adults ages 18-64, uses both question sets and contains other disability questions. We compared ACS-6 and WG-SS responses with self-reported disability types. The ACS-6 and WG-SS failed to identify 20percent and 43percent, respectively, of respondents who reported disabilities in response to other NSHD questions (a broader WG-SS version missed 4.4percent of respondents). The ACS-6 and the WG-SS performed especially poorly in capturing respondents with psychiatric disabilities or chronic health conditions. Researchers and policy makers must augment or strengthen federal disability questions to improve the accuracy of disability prevalence counts, understanding of health disparities, and planning of appropriate services for a diverse and growing population.