Background: Major depressive disorder (MDD) is largely managed in primary care, but physicians vary widely in their understanding of symptoms and treatments. This study aims to better understand the evolution of depression from initial diagnosis over a 3-year period.
Methods: This was a noninterventional, retrospective, longitudinal study, with 2 waves of participant interviews approximately 3 years apart. Phone interviews were conducted using the hybrid artificial intelligence (AI) Sleep-EVAL system, an AI-driven diagnostic deep learning tool. Participants were noninstitutionalized adults representative of the general population in 8 US states. Diagnosis was confirmed according to the DSM-5 using the Sleep-EVAL System.
Results: 10,931 participants completed Wave 1 and 2 (W1, W2) interviews. The prevalence of MDD, including partial and complete remission, was 13.4 % and 19.6 % in W1 and W2, respectively. About 42 % of MDD participants at W1 continued to report depressive symptoms at W2. Approximately half of antidepressant (AD) users in W1 were moderately to completely dissatisfied with their treatment; 29.6 % changed their AD for a different one, with 16.4 % switching from one SSRI to another between W1 and W2. Primary care physicians were the top AD prescribers, both in W1 (45.7 %) and W2 (59%), respectively.
Limitations: Data collected relied on self-reporting by participants. As such, the interpretation of the data may be limited.
Conclusions: Depression affects a sizeable portion of the US population. Dissatisfaction with treatment, frequent switching of ADs, and changing care providers are associated with low rates of remission. Residual symptoms remain a challenge that future research must address.
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Objective: The objectives of this study are to evaluate the prevalence and incidence of Narcolepsy type 1 and type 2 and to determine the prevalence of narcolepsy diagnosis criteria in the US general population.
Methods: This longitudinal study was conducted in the adult US general population in two occasions. The initial interviews included 15 states (Arizona, California, Colorado, Florida, Idaho, Missouri, New York, North Carolina, North Dakota, Oregon, Pennsylvania, South Dakota, Texas, Washington, and Wyoming). The follow-up interviews, was done three years later in eight of these states. Of the 19,136 contacted individuals, 15,929 completed the initial interview and 10,931 completed the follow-up. Participants were interviewed using the Sleep-EVAL system, an artificial intelligence tool. Narcolepsy Type 1 (with cataplexy) and Narcolepsy Type 2 (without cataplexy) were defined according to the ICSD-3 classification. Symptoms of narcolepsy were assessed by frequency per week and duration. Medical visits and diagnoses were also collected.
Results: Participants were aged between 18 and 102 years of age (mean 45.8 ± 17.9 years), 51.3 % were women. The prevalence of narcolepsy with cataplexy was 12.6 per 100,000 individuals (95 % C.I., 0 to 30) and narcolepsy without cataplexy was 25.1 per 100,000. The incidence per year was 2.6 per 100,000 individuals (95 % C.I., 0 to 11).
Conclusions: Narcolepsy is a rare condition affecting 37.7/100,000 individuals (126,191 individuals in the current US population). Our US general population prevalence is in line with rates found in community-based studies but lower than what is reported in claim database studies.
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Epidemiological studies can provide information not only on specific diagnostic entities but also on their underlying symptomatic constellations. For this purpose, an expert system was developed for the assessment of sleep disorders and endowed with the fuzzy logic capabilities necessary to determine the degree to which a given symptom corresponds to a specific diagnosis.
Uncertainty is inherent in fields such as sleep medicine and psychiatry, and becomes evident in clinical practice at the stages of data collection and diagnostic formulation, when the clinician must determine whether a symptom is present and must choose from several diagnostic possibilities. The process involves a considerable degree of subjectivity on the part of the patient in trying to describe his or her symptoms, and of the clinician whose final diagnosis will depend on his or her clinical experience and interpretation of what is normal and what is pathological.
Inferential models of the probabilistic or fuzzy logic type take into account such uncertainty.
The Sleep-Eval system has been used in epidemiological and clinical studies involving 34,044 interviews collected by close to 300 interviewers.
The diagnostic potential of these models is illustrated using data collected in an epidemiological study of the noninstitutionalized general population of Italy and underlines the advantages and limits of the binary, bayesian, and fuzzy logic methods and analyses.
To validate the Sleep-EVAL expert system, a computerized tool designed for the assessment of sleep disorders, against polysomnographic data and clinical assessments by sleep specialists. Patients were interviewed twice, once by a physician using Sleep-EVAL and again by a sleep specialist. Polysomnographic data were also recorded to ascertain diagnoses.
Agreement between diagnoses generated by Sleep-EVAL and those formulated by sleep specialists was determined via the kappa statistic. Settings: Sleep disorder centers at Stanford University (USA) and Regensburg University (Germany); 105 patients aged 18 years or over were included.
Results: Sleep-EVAL made an average of 1.32 diagnoses per patient, compared with 0.93 for the sleep specialists. Overall agreement on any sleep-breathing disorder was 96.9% (Kappa .94). More than half of the patients were diagnosed with obstructive sleep apnea syndrome (OSAS); the agreement rate for this specific diagnosis was 96.7% (Kappa .93).
The findings indicate that the Sleep-EVAL system is a valid instrument for the recognition of major sleep disorders, particularly insomnia and OSAS.
Our study aims to explore the associations between outdoor nighttime lights (ONL) and sleep patterns in the human population. Cross-sectional telephone study of a representative sample of the general US population age 18 y or older. 19,136 noninstitutionalized individuals (participation rate: 83.2%) were interviewed by telephone. The Sleep-EVAL expert system administered questions on life and sleeping habits; health; sleep, mental and organic disorders (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision; International Classification of Sleep Disorders, Second Edition; International Classification of Diseases, 10th Edition).
Individuals were geolocated by longitude and latitude. Outdoor nighttime light measurements were obtained from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS), with nighttime passes taking place between 19:30 and 22:30 local time. Light data were correlated precisely to the geolocation of each participant of the general population sample.
Living in areas with greater ONL was associated with delayed bedtime (P < 0.0001) and wakeup time (P < 0.0001), shorter sleep duration (P < 0.01), and increased daytime sleepiness (P < 0.0001). Living in areas with greater ONL also increased the dissatisfaction with sleep quantity and quality (P < 0.0001) and the likelihood of having a diagnostic profile congruent with a circadian rhythm disorder (P < 0.0001).
Although they improve the overall safety of people and traffic, nighttime lights in our streets and cities are clearly linked with modifications in human sleep behaviors and also impinge on the daytime functioning of individuals living in areas with greater ONL.