Early Detection Of Illness Research Articles

Grasping the weight cut-off for anorexia nervosa in children and adolescents.

The Diagnostic and Statistical Manual of Mental Disorders 5 suggests the 5th age-adjusted body mass index (BMI) percentile as the numeric cut-off for anorexia nervosa (AN) in children and adolescents. We aimed to investigate the degree to which the 5th age-adjusted percentile as the numeric cut-off for AN in youths reflects the clinical population of patients accepted for treatment. From a specialized eating disorder clinic, 305 patients with AN below 18 years of age were grouped according to age-adjusted BMI percentiles [below the 5th (low), above the 10th (high), and between the 5th and the 10th (medium)]. The distribution of eating disorder diagnoses and severity measured by the Eating Disorder Examination was compared. Full-syndrome anorexia nervosa (F.50.0) was found in 182 (59.5%) patients and atypical anorexia nervosa (F.50.1) in 123 patients (40.5%). The number of patients in the low, medium, and high BMI percentile groups was 189 (62.0%), 34 (11.1%), and 82 (26.9%), respectively. Patients in the low BMI group differed from patients in the medium BMI group by a lower frequency of vomiting. The high BMI group presented with more weight and shape concern than the lower BMI group. Age was not a confounder of these associations. We question the applicability of the 5th BMI percentile as a substantiated cut-off for the weight criterion in anorexia nervosa in youths and argue that the cut-off should not be ascribed great clinical importance as this may hinder early detection of illness and initiation of treatment.

Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media.

Digital technologies hold promise for supporting the detection and management of schizophrenia. This exploratory study aimed to generate an initial understanding of whether patterns of communication about depression and anxiety on popular social media among individuals with schizophrenia are consistent with offline representations of the illness. From January to July 2016, posts on Twitter were collected from a sample of Twitter users who self-identify as having a schizophrenia spectrum disorder (n = 203) and a randomly selected sample of control users (n = 173). Frequency and timing of communication about depression and anxiety were compared between groups. In total, the groups posted n = 1,544,122 tweets and users had similar characteristics. Twitter users with schizophrenia showed significantly greater odds of tweeting about depression compared with control users (OR = 2.69; 95% CI 1.76-4.10), and significantly greater odds of tweeting about anxiety compared with control users (OR = 1.81; 95% CI 1.20-2.73). This study offers preliminary insights that Twitter users with schizophrenia may express elevated symptoms of depression and anxiety in their online posts, which is consistent with clinical characteristics of schizophrenia observed in offline settings. Social media platforms could further our understanding of schizophrenia by informing a digital phenotype and may afford new opportunities to support early illness detection.

English

Voluntary counseling and testing (VCT) for HIV is essential and critical way for prevention, care and treatment of HIV. It allows early detection of illness, reducing transmission, morbidity and mortality from it. But youth knowledge and attitude towards HIV testing services is not universal. The aim of this study was to assess knowledge, attitude, practice and factors associated with VCT for HIV among University Students in Ethiopia. Institution based cross sectional study was conducted among University students in Ethiopia using pretested, structured self-administered questionnaire. Single proportion formula was used to calculate the sample size (361). Multistage sampling with stratified sampling technique was used. Data was analyzed using SPSS version 16.0 with chi square test, bi-variate and multivariate analysis via OR, AOR, p-value and confidence interval. Binary logistic regression was used. Out of 361 sample 93.6% of them responded, with 178 (52.7%) male and most are below 25 years. Majority were single, orthodox in religion, and from urban residence. 86% of them have heard of voluntary testing for HIV and know that having early test will prevent future spread of HIV. Based on the scoring 66% are knowledgeable about VCT. Almost 60% of participants were willing to undergo the test and 80% advices their friends to have test. Also 40.5% had good attitude toward test for HIV and 65.7 and 29.6% were ever tested and tested in the previous year respectively. Fear of HIV test results, stigma, and service related factors were major barriers for testing. Being male (AOR=1.607, CI: (1.01-2.57) p value= 0.047) and first year (AOR= 3.25, 1.33 – 8.83) were significantly had lower odds of being knowledgeable than females and seniors respectively. Those who were not knowledgeable have two times odd of poor attitude (AOR=1.901(1.20-3.02) P=0.006)). Students from urban (AOR=2.088, CI: (1.273- 3.425) P=0.004), unseparated family (AOR=2.24CI: (1.02-4.92) P=0.045) and with poor attitude (AOR= 1.76 CI: (1.08-2.89) p=0.024) were more likely not to have test than counterparts. Knowledge and attitude towards HIV testing is not satisfactory. Freshman students are the target for behavioral interventions. Improving awareness and attitude of students through peer to peer discussion and other packages is crucial to increase the practice and ultimately combat HIV in higher institutions. Key words: Attitude, HIV/AIDS, Knowledge, University, voluntary counseling and testing (VCT), associated factors.

PP.39.03] TREATMENT DISCONTINUATION AND ADHERENCE IN A COHORT OF BELGIAN PATIENTS NEWLY DIAGNOSED WITH ARTERIAL HYPERTENSION

Objective: A major problem of arterial hypertension is that many hypertensive people are not treated or do not follow their treatment correctly. Nevertheless, a good adherence with antihypertensive medications significantly reduces cardiovascular complications. This study was aimed to analyze the treatment discontinuations and the adherence in a cohort of patients newly diagnosed with arterial hypertension. Design and method: The pharmaceutical treatment of 18706 new patients using for the first time an antihypertensive drug was followed during 5 years (2008–2012) with retrospectively collected administrative database of a Belgian health insurer. Adherence was measured using the medication possession ratio (MPR greater than 80%). We defined treatment discontinuation as interruption of drug treatment during at least 6 months. Results: 12% of the cohort permanently stopped the antihypertensive treatment after the first year. On average patients under 50 years interrupted the treatment during 6 months more than 4 times, while patients over age 50 have had 2 periods of interruption. The correlation between the absence of treatment discontinuation the first two years and the following three years was positive and significant (r = 0.84). Only 22.4% of the cohort were compliant with antihypertensive therapy during the 5 years of treatment. Over the period studied, the rate of compliance among patients receiving one antihypertensive ATC class increased significantly (from 63% to 75%), whereas it remained relatively stable for two combined (or sequential) ATC classes (55%-56%) and for multi ATC classes (42%-44%). Regardless of the treatment year, compliance was particularly poor among patients using beta blockers. The correlation between good compliance at the beginning of the treatment (the first two years) and good compliance the following years was positive and significant (r = 0.66). Conclusions: The study emphasizes the necessity of early detection of illness and continued treatment, allowing to avoid or delay complications. We also highlight the importance to take effective action early in the treatment to improve the compliance, since the initial compliance conditions compliance over the following years (especially among patients under 50 years, where treatment discontinuations are more frequent and the preventive benefit high).

Do the Trajectories of Bipolar Disorder and Schizophrenia Follow a Universal Staging Model?

The purpose of this study is to address the question of whether a universal staging model of severe psychiatric disorders is a viable direction for future research by examining the extant literature. A narrative review was conducted of the relevant historical, conceptual, and empirical literature pertaining to the clinical trajectory of bipolar disorder and schizophrenia and issues relevant to staging. There is substantive evidence that classic recurrent bipolar disorder is separable from schizophrenia on the basis of family history, developmental and clinical course, treatment response, and neurobiological findings. However, because of the intrinsic heterogeneity of diagnostic categories that has been amplified by recent changes in psychiatric taxonomy, key distinctions between the groups have become obfuscated. While mapping risk and illness markers to emerging psychopathology is a logical approach and may be of value for some psychiatric disorders and/or their clinical subtypes, robust evidence supporting identifiable stages per se is still lacking. Presently, even rudimentary stages such as prodromes cannot be meaningfully applied across different disorders and no commonalities can be found for the basis of universal staging. Advances in the prediction of risk, accurate early illness detection, and tailored intervention will require mapping biomarkers and other risk indicators to reliable clinical phases of illness progression. Given the capricious nature of mood and psychotic disorders, this task is likely to yield success only if conducted in narrowly defined subgroups of individuals at high risk for specific illnesses. This approach is diametrically opposite to that being promulgated by proponents of a universal staging model.

Using Health Helpline Mediated Self-Swabbing as a Surveillance Tool for Influenza

This session will provide an overview of the pilot project regarding a laboratory-linked telephone health helpline based surveillance system. The surveillance system uses syndromic surveillance tools for early detection of illness and links it to a specimen available for laboratory testing. Through the health helpline, people with influenza-like illness are recruited and sent a nasal swab to obtain a specimen via self-swabbing that can be used to test for influenza viruses. The surveillance system is available to all residents of Ontario and was in operation for one year. Some of the results, analyses, and limitations of this project will be discussed during this session.

The impact of E-Mental Health on prevention and early detection of illness

Our current healthcare system in the United States is characterized by problems with access to timely and evidence-based care, particularly for mental disorders. Telemental health improves access to care regardless of the point-of-service or barriers involved. Its effectiveness across age, population and disorders is as good as in-person care, though adjustments for some populations in the approach is necessary. Early intervention is an example of “Cadillac” care or a best evidence-based approach that is easier to distribute via telemedicine. Cadillac care delivered via TMH has the potential to bring evidence-based early intervention modalities to very young children and their families. However, early access to care is also critical for all populations, particularly those with cultural or medical disadvantages. It appears that telemental health may be preferable or better than in-person care in some instances.Disclosure of interestThe author has not supplied his declaration of competing interest.

A Multidimensional Time-Series Similarity Measure With Applications to Eldercare Monitoring.

In the last decade, data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management, and others. Many data mining techniques are based on computing the similarity between two sensor data patterns. A variety of representations and similarity measures for multiattribute time series have been proposed in the literature. In this paper, we describe a novel method for computing the similarity of two multiattribute time series based on a temporal version of Smith-Waterman (SW), a well-known bioinformatics algorithm. We then apply our method to sensor data from an eldercare application for early illness detection. Our method mitigates difficulties related to data uncertainty and aggregation that often arise when processing sensor data. The experiments take place at an aging-in-place facility, TigerPlace, located in Columbia, MO, USA. To validate our method, we used data from nonwearable sensor networks placed in TigerPlace apartments, combined with information from an electronic health record. We provide a set of experiments that investigate temporal version of SW properties, together with experiments on TigerPlace datasets. On a pilot sensor dataset from nine residents, with a total of 1902days and around 2.1 million sensor hits of collected data, we obtained an average abnormal events prediction F-measure of 0.75.

Should CMS cover lung cancer screening for the fully informed patient?

Lung cancer is the leading cause of cancer death1 and an estimated 12,000 lung cancer deaths could potentially be averted each year in the United States through early detection with low-dose computed tomography (LDCT).2 The Centers for Medicare & Medicaid Services is currently considering national coverage of lung cancer screening with LDCT for individuals at high risk of developing lung cancer based on their age and smoking history.3 The U.S. Preventive Services Task Force (USPSTF) recently updated its recommendation about screening for lung cancer to recommend annual screening with LDCT for adults aged 55–80 years who have a 30-pack-year smoking history and who currently smoke or have quit within the past 15 years (B recommendation, update published December 31, 2013).4 In making its recommendation, the USPSTF weighed many factors including the estimated 16% mortality reduction associated with screening and surgical resection5 and the high false positive rate associated with screening.6 Unlike new private insurance plans, coverage of preventive services with a grade of A or B by the USPSTF is not mandatory for Medicare. CMS is allowed to cover additional preventive services if it determines through the Medicare national coverage determinations process that the service is reasonable and necessary for prevention or early detection of illness, is recommended with an A or B grade by the USPSTF, and it meets certain other requirements.

A framework for harmonizing sensor data to support embedded health assessment.

The use of in-home and mobile sensing is likely to be a key component of future care and has recently been studied by many research groups world-wide. Researchers have shown that embedded sensors can be used for health assessment such as early illness detection and the management of chronic health conditions. However, research collaboration and data sharing have been hampered by disparate sets of sensors and data collection methods. To date, there have been no studies to investigate common measures that can be used across multiple sites with different types of sensors, which would facilitate large scale studies and reuse of existing datasets. In this paper, we propose a framework for harmonizing heterogeneous sensor data through an intermediate layer, the Conceptual Sensor, which maps physical measures to clinical space. Examples are included for sleep quality and ambulatory physical function.

G232(P) Prevalence of elevated PEWS scores in a Specialist Children’s Hospital: snapshot audit

IntroductionEarly Warning Scores are associated with an increase in early detection of illness (true positive), high scores without clinical deterioration (false positive) and clinician over-ride or alarm fatigue that leads...

Management of Dementia and Depression Utilizing In- Home Passive Sensor Data.

This study investigates whether motion density maps based on passive infrared (PIR) motion sensors and the average time out and average density per hour measures of the density map are sensitive enough to detect changes in mental health over time. Within the sensor network, data are logged from PIR motion sensors which capture motion events as people move around the home. If there is continuous motion, the sensor will generate events at 7 second intervals. If the resident is less active, events will be generated less frequently. A web application displays the data as activity density maps showing events per hour with hours on the vertical axis and progressive days on the horizontal axis. Color and intensity provide textural indications of time spent away from home and activity level. Texture features from the co-occurrence matrix are used to capture the periodicity pattern of the activity (including homogeneity, local variation, and entropy) and are combined with the average motion density per hour and the average time away from home. The similarity of two different density maps is represented by a number that is computed in feature space as the distance from one map to the other, or a measure of dis-similarity. Employing a retrospective approach, density maps were compared with health assessment information (Geriatric Depression Scale, Mini Mental State Exam, and Short Form Health Survey -12) to determine congruence between activity pattern changes and the health information20. A case by case study method, analyzed the density maps of 5 individuals with identified mental health issues. These density maps were reviewed along with the averages of time out of apartment per day per hour and average density per hour for hours at home and mental health assessment scores to determine if there were activity changes and if activity patterns reflected changes in mental health conditions. The motion density maps show visual changes in the client's activity, including circadian rhythm, time away from home, and general activity level (sedentary vs. puttering). The measures are sensitive enough, yielding averages of time out of apartment and average density per hour for hours at home that indicate significant change. There is evidence of congruence with health assessment scores. This pilot study demonstrates that density maps can be used as a tool for early illness detection. The results indicate that sensor technology has the potential to augment traditional health care assessments and care coordination.

A vision for optometry in Oman

A vision for optometry in Oman

Activity Density Map Visualization and Dissimilarity Comparison for Eldercare Monitoring

In this paper, we present a methodology for analyzing passive infrared motion sensor data logged in the homes of seniors. The objective is to capture activity patterns that represent different health conditions. Recognizing changes in the activity patterns can then be used to provide early detection of health changes. A visualization of motion sensor data is introduced in the form of a density map that uses different colors to show varying levels of activity. For evaluating the activity density level accurately, time away from home is determined first using a system of fuzzy rules. In addition, a dissimilarity between two density maps is computed using texture features for automatically determining changes in activity patterns, which may indicate a health problem. The activity density maps are being used in an aging in place senior housing community to aid clinicians in early illness detection. Three case studies of elderly residents are included to illustrate how the density map and dissimilarity measure can be used to track general activity level and daily patterns over time, showing changes in physical, cognitive, and mental health.

Mathematical Models to Enhance the Value of Information from Current Laboratory Platforms

Buckminster Fuller once said, “It is one of our most exciting discoveries that local discovery leads to a complex of further discoveries.” Every minute, a clinical laboratory generates local discoveries. Examples are a creatinine value of 3 mg/dL (265 μmol/L), a potassium concentration of 6 mEq/L, or a hematocrit of 24%. Each observation provides the clinician with the potential to discover the cause of an urgent problem. Some researchers believe we can discover disease earlier by using existing laboratory platforms. For example, while performing a complete blood count (CBC),3 we can collect up to 80 000 measurements at a time. “Clinically, the only thing typically used is the average red blood cell volume and maybe the variance,” said Dr. John Higgins, assistant pathologist at Massachusetts General Hospital in Boston. “It was shocking to me.” Higgins wanted to know what could be done with some of what he considers as high-throughput data. We spoke with Higgins about his pioneering work on a mathematical model that he hopes will make use of the wealth of these data to identify various forms of disease and increase opportunities for the early detection of illness. The body is known to create 200 × 109 red blood cells (RBCs) per day. The body also loses the same number of cells through senescence, thereby maintaining an overall stability of the RBC population; however, little is known about the aspect of the 120-day life cycle of RBCs that occurs after they leave the bone marrow. To try to elucidate more about the RBC aging process, researchers began studying the volume and hemoglobin mass of RBCs, only to find that the 2 parameters are linked: New reticulocytes have a high volume and contain a high hemoglobin mass, and both variables decrease as the cell ages (although cell …

Smart Medical Services: A Discussion of State-of-The-Art Approaches

This paper presents a discussion of current developments in the field of smart medical services. Smart medical services are often cited as a promising solution to support elderly or disabled people. By providing a wide variety of services, they bear an immense potential for revolutionizing the way health services are provided in the future. In general, smart medical services can be clustered into three categories focusing on the detection and prevention of emergency situations, long-term treatment of chronic diseases, and the prevention and early-detection of illnesses. This paper provides an overview over the different types of applications and describes several research demonstrators and prototype systems for each category.

Early Illness Recognition Using In-home Monitoring Sensors and Multiple Instance Learning

Many older adults in the US prefer to live independently for as long as they are able, despite the onset of conditions such as frailty and dementia. Solutions are needed to enable independent living, while enhancing safety and peace of mind for their families. Elderly patients are particularly at-risk for late assessment of cognitive changes. We predict early signs of illness in older adults by using the data generated by a continuous, unobtrusive nursing home monitoring system. We describe the possibility of employing a multiple instance learning (MIL) framework for early illness detection. The MIL framework is suitable for training classifiers when the available data presents temporal or location uncertainties. We provide experiments on three datasets that prove the utility of the MIL framework. We first tuned our algorithms on a set of 200 normal/abnormal behavior patterns produced by a dedicated simulator. We then conducted two retrospective studies on residents from the Tiger Place aging in place facility, aged over 70, which have been monitored with motion and bed sensors for over two years. The presence or absence of the illness was manually assessed based on the nursing visit reports. The use of simulated sensor data proved to be very useful for algorithm development and testing. The results obtained using MIL for six Tiger Place residents, an average area under the receiver operator characteristic curve (AROC) of 0.7, are promising. However, more sophisticated MIL classifiers are needed to improve the performance.

Changes in behavioural and physiological parameters associated with Taenia pisiformis infection in rabbits (Oryctolagus cuniculus) that may improve early detection of sick rabbits

The purpose of the present experiment was to describe rabbits´ early behavioral responses to illness, and its relation with physiological changes to help improve early detection of infection, particularly those induced by Taenia pisiformis in rabbits. Twenty adult female New Zealand rabbits were randomly allocated in two groups, to determine whether changes in behavior and some physiological parameters can be induced in rabbits after a T. pisiformis infection. Infected animals were orally inoculated with 3,000 eggs of T. pisiformis, while controls only received saline solution. Behavioral activity was recorded daily from 19:00 to 21:00 h starting two days before infection. Mate choice and rank status were assessed, and blood samples were collected at -2, 7, 14 and 25 days post infection (dpi) for hematological and hepatic function determinations. All animals were observed for clinical signs every other day from the beginning of the experiment and euthanized 25 dpi after last sampling. Infected animals spent more (P<0.01) time lying down (87 vs. 17%) and less (P<0.05) time grooming (43 vs. 57%) and drinking (26 vs. 74%) than controls. These differences were noticeable 6 dpi and remained until the end of the experiment. No changes (P>0.05) were observed in the time spent at the feeder, rank status or mate choice. Leucocyte and lymphocyte concentrations increased (P<0.05) while heterophil counts decreased in infected rabbits as the experiment progressed. Furthermore, infected animals had larger concentrations of alkaline phosphatase as soon as 7 dpi. No clinical signs of the disease were detected. Necropsy findings corroborate hepatic lesions and presence of the parasite in all infected animals. It was concluded that an infection with 3,000 eggs of T. pisiformis induced changes in behavioral patterns, supported by some changes in hematological and hepatic parameters that could improve early detection of illness, regardless of clinical signs.

2010 in Review.

2010 in Review.

Improving Nurse Care Coordination With Technology

Technology offers potential solutions to the pending crisis of healthcare for older adults, while healthcare workers are in short supply. Technology can enable remote monitoring of individuals and early detection of potential problems, so that early interventions can help older adults remain as healthy and independent as possible. Research is under way with passive monitoring technology in senior housing that is finding patterns in the data that can enhance nurse care coordination through early illness detection. With early detection, interventions can be more effective and reduce hospitalization and other healthcare expenses. Case studies are presented, and implications are discussed.