15 results
Shortening the Alzheimer’s disease assessment scale cognitive subscale
- Stephen Z. Levine, Yair Goldberg, Anat Rotstein, Myrto Samara, Kazufumi Yoshida, Andrea Cipriani, Takeshi Iwatsubo, Stefan Leucht, Toshiaki A. Furukawa
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- Journal:
- European Psychiatry / Volume 67 / Issue 1 / 2024
- Published online by Cambridge University Press:
- 23 February 2024, e19
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Background
A short yet reliable cognitive measure is needed that separates treatment and placebo for treatment trials for Alzheimer’s disease. Hence, we aimed to shorten the Alzheimer’s Disease Assessment Scale Cognitive Subscale (ADAS-Cog) and test its use as an efficacy measure.
MethodsSecondary data analysis of participant-level data from five pivotal clinical trials of donepezil compared with placebo for Alzheimer’s disease (N = 2,198). Across all five trials, cognition was appraised using the original 11-item ADAS-Cog. Statistical analysis consisted of sample characterization, item response theory (IRT) to identify an ADAS-Cog short version, and mixed models for repeated-measures analysis to examine the effect sizes of ADAS-Cog change on the original and short versions in the placebo versus donepezil groups.
ResultsBased on IRT, a short ADAS-Cog was developed with seven items and two response options. The original and short ADAS-Cog correlated at baseline and at weeks 12 and 24 at 0.7. Effect sizes based on mixed modeling showed that the short and original ADAS-Cog separated placebo and donepezil comparably (ADAS-Cog original ES = 0.33, 95% CI = 0.29, 0.40, ADAS-Cog short ES = 0.25, 95% CI =0.23, 0.34).
ConclusionsIRT identified a short ADAS-cog version that separated donepezil and placebo, suggesting its clinical potential for assessment and treatment monitoring.
Is cognition integral to psychopathology? A population-based cohort study
- Anat Rotstein, Suzanne Fund, Stephen Z. Levine, Abraham Reichenberg, Judy Goldenberg
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- Journal:
- Psychological Medicine / Volume 53 / Issue 15 / November 2023
- Published online by Cambridge University Press:
- 28 April 2023, pp. 7350-7357
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Background
Lower cognitive functioning has been documented across psychiatric disorders and hypothesized to be a core deficit of mental disorders. Situating psychopathology and cognition as part of a unitary construct is therefore important to understanding the etiology of psychiatric disorders. The current study aims to test competing structural models of psychopathology and cognition in a large national cohort of adolescents.
MethodsThe analytic sample consisted of 1189 participants aged 16–17 years, screened by the Israeli Draft Board. Psychopathology was assessed using a modified version of the Brief Symptom Inventory, and cognition was assessed based on four standardized test scores ((1) mathematical reasoning, concentration, and concept manipulation; (2) visual-spatial problem-solving skills and nonverbal abstract reasoning; (3) verbal understanding; (4) categorization and verbal abstraction). Confirmatory factor analysis was implemented to compare competing structural models of psychopathology with and without cognition. Sensitivity analyses examined the models in different subpopulations.
ResultsConfirmatory factor analysis indicated a better model fit of psychopathological symptoms without cognition (RMSEA = 0.037; TLI = 0.991; CFI = 0.992) than with cognition (RMSEA = 0.04–0.042; TLI = 0.987–0.988; CFI = 0.988–0.989). Sensitivity analyses supported the robustness of these results with a single exception. Among participants with low cognitive abilities (N = 139), models that integrated psychopathological symptoms with cognition had a better fit compared to models of psychopathology without cognition.
ConclusionsThe current study suggests that cognition and psychopathology are, generally, independent constructs. However, within low cognitive abilities, cognition was integral to the structure of psychopathology. Our results point toward an increased vulnerability to psychopathology in individuals with low cognitive abilities and may provide valuable information for clinicians.
Maternal rheumatoid arthritis and risk of autism in the offspring
- Weiyao Yin, Mattias Norrbäck, Stephen Z. Levine, Natalia Rivera, Joseph D. Buxbaum, Hailin Zhu, Benjamin Yip, Abraham Reichenberg, Johan Askling, Sven Sandin
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- Journal:
- Psychological Medicine / Volume 53 / Issue 15 / November 2023
- Published online by Cambridge University Press:
- 24 April 2023, pp. 7300-7308
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Background
Maternal Rheumatoid Arthritis (RA) is suggested to increase the risk of Autism Spectrum Disorder (ASD) in the offspring, mainly through inflammation/autoimmunity, but the association is unclear. A prospective population-based cohort study was implemented to examine the association between maternal RA and offspring ASD.
MethodsWe included all children born alive in Sweden from 1995 to 2015, followed up through 2017. Diagnoses of ASD and RA were clinically ascertained from National Patient Register. We quantified the association by hazard ratios (HR) and two-sided 95% confidence intervals (CI), from Cox regression after detailed adjustment for potential confounders. We examined RA serostatus, etiological subgroups and the timing of exposure. To closer examine the underlying mechanism for the association, we included a negative control group for RA, arthralgia, with similar symptomology as RA but free from inflammation/autoimmunity.
ResultsOf 3629 children born to mothers with RA, 70 (1.94%) were diagnosed with ASD, compared to 28 892 (1.92%) of 1 503 908 children born to mothers without RA. Maternal RA before delivery was associated with an increased risk of offspring ASD (HR = 1.43, 95% CI 1.11–1.84), especially for seronegative RA (HR = 1.61, 95% CI 1.12–2.30). No similar association was observed for paternal RA, maternal sisters with RA, or RA diagnosed after delivery. Maternal arthralgia displayed as high risks for offspring ASD as did maternal RA (HR = 1.41, 95% CI 1.24–1.60).
ConclusionsIn Sweden, maternal RA before delivery was associated with an increased risk of offspring ASD. The comparable association between maternal arthralgia and ASD risk suggests other pathways of risk than autoimmunity/inflammation, acting jointly or independently of RA.
Somatic comorbidities of mental disorders in pregnancy
- Vahe Khachadourian, Arad Kodesh, Stephen Z. Levine, Emma Lin, Joseph D. Buxbaum, Veerle Bergink, Sven Sandin, Abraham Reichenberg, Magdalena Janecka
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- Journal:
- European Psychiatry / Volume 66 / Issue 1 / 2023
- Published online by Cambridge University Press:
- 16 January 2023, e15
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Background
Mental and physical health conditions are frequently comorbid. Despite the widespread physiological and behavioral changes during pregnancy, the pattern of comorbidities among women in pregnancy is not well studied. This study aimed to systematically examine the associations between mental and somatic disorders before and during pregnancy.
MethodThe study used data from mothers of a nationally representative birth cohort of children born in Israel (1997–2008). We compared the risk of all major somatic disorders (International Classification of Diseases, Ninth Revision) in pregnant women with and without a mental disorder. All analyses were adjusted for maternal age, child’s birth year, family socioeconomic status, and the total number of maternal encounters with health services around pregnancy period.
ResultsThe analytical sample included 77,030 mother–child dyads, with 30,083 unique mothers. The mean age at child’s birth was 29.8 years. Prevalence of diagnosis of mental disorder around pregnancy in our sample was 4.4%. Comorbidity between mental and somatic disorders was two times higher than the comorbidity between pairs of different somatic disorders. Of the 17 somatic disorder categories, seven were positively associated with mental health disorders. The highly prevalent comorbidities associated with mental disorders in pregnancy included e.g. musculoskeletal (OR = 1.30; 95% CI = 1.20–1.42) and digestive system diseases (OR = 1.23; 95% CI = 1.13–1.34).
ConclusionsWe observed that associations between maternal diagnoses and mental health stand out from the general pattern of comorbidity between nonmental health diseases. The study results confirm the need for screening for mental disorders during pregnancy and for potential comorbid conditions associated with mental disorders.
Increased incident rates of antidepressant use during the COVID-19 pandemic: interrupted time-series analysis of a nationally representative sample
- Sophia Frangou, Yael Travis-Lumer, Arad Kodesh, Yair Goldberg, Faye New, Abraham Reichenberg, Stephen Z. Levine
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- Journal:
- Psychological Medicine / Volume 53 / Issue 11 / August 2023
- Published online by Cambridge University Press:
- 10 June 2022, pp. 4943-4951
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Background
The COVID-19 pandemic has been associated with increased levels of depression and anxiety with implications for the use of antidepressant medications.
MethodsThe incident rate of antidepressant fills before and during the COVID-19 pandemic were compared using interrupted time-series analysis followed by comprehensive sensitivity analyses on data derived from electronic medical records from a large health management organization providing nationwide services to 14% of the Israeli population. The dataset covered the period from 1 January 2013 to 1 February 2021, with 1 March 2020 onwards defined as the period of the COVID-19 pandemic. Forecasting analysis was implemented to test the effect of the vaccine roll-out and easing of social restrictions on antidepressant use.
ResultsThe sample consisted of 852 233 persons with a total antidepressant incident fill count of 139 535.4 (total cumulative rate per 100 000 = 16 372.91, 95% CI 16 287.19–16 459.01). We calculated the proportion of antidepressant prescription fills for the COVID-19 period, and the counterfactual proportion for the same period, assuming COVID-19 had not occurred. The difference in these proportions was significant [Cohen's h = 10−3 (0.16), 95% CI 10−3 ( − 0.71 to 1.03)]. The pandemic was associated with a significant increase in the slope of the incident rate of antidepressant fills (slope change = 0.01, 95% CI 0.00–0.03; p = 0.04) and a monthly increase of 2% compared to the counterfactual (the estimated rate assuming no pandemic occurred). The increased rate was more pronounced in women, and was not modified by lockdown on/off periods, socioeconomic or SARS-CoV-2 status. The rate of observed antidepressant fills was similar to that forecasted under the assumption of ongoing COVID-19 distress.
ConclusionThese findings underscore the toll of the pandemic on mental health and inform mental health policy and service delivery during and after implementing COVID-19 attenuation strategies.
Gender differences in quality of life and the course of schizophrenia: national study
- Anat Rotstein, Efrat Shadmi, David Roe, Marc Gelkopf, Stephen Z. Levine
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- Journal:
- BJPsych Open / Volume 8 / Issue 2 / March 2022
- Published online by Cambridge University Press:
- 01 February 2022, e35
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Background
Evidence from various sources suggests that females with schizophrenia tend to report lower quality of life than males with schizophrenia despite having a less severe course of the disorder. However, studies have not examined this directly.
AimsTo examine gender differences in the association between quality of life and the risk of subsequent psychiatric hospital admissions in a national sample with schizophrenia.
MethodThe sample consisted of 989 (60.90%) males and 635 (39.10%) females with an ICD-10 diagnosis of schizophrenia. Quality of life was assessed and scored using the Manchester Short Assessment of Quality of Life. The course of schizophrenia was assessed from the number of psychiatric hospital admissions. Participants completed the quality of life assessment and were then followed up for 18-months for subsequent psychiatric admissions. Hazard ratios (HR) from Cox proportional hazards regression models were estimated unadjusted and adjusted for covariates (age at schizophrenia onset and birth year). Analyses were computed for males and females separately, as well as for the entire cohort.
ResultsA subsample of 93 males and 55 females was admitted to a psychiatric hospital during follow-up. Higher quality of life scores were significantly (P < 0.05) associated with a reduced risk of subsequent admissions among males (unadjusted: HR = 0.96, 95% CI 0.93–0.99; adjusted HR = 0.96, 95% CI 0.93–0.99) but not among females (unadjusted: HR = 0.97, 95% CI 0.93–1.02; adjusted HR = 0.97, 95% CI 0.93–1.02).
ConclusionsQuality of life in schizophrenia is a gender-specific construct and should be considered as such in clinical practice and future research.
Biopsychosocial exposure to the COVID-19 pandemic and the relative risk of schizophrenia: Interrupted time-series analysis of a nationally representative sample
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- Yael Travis-Lumer, Arad Kodesh, Yair Goldberg, Abraham Reichenberg, Sophia Frangou, Stephen Z. Levine
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- Journal:
- European Psychiatry / Volume 65 / Issue 1 / 2022
- Published online by Cambridge University Press:
- 24 January 2022, e7
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Background
Studies of COVID-19 pandemic biopsychosocial exposure and schizophrenia risk showed contradictory results, were undertaken early in the pandemic, and did not consider lockdowns or COVID-19 infection. Hence, we examined the association between COVID-19 biopsychosocial exposure and incident schizophrenia.
MethodsAn interrupted time-series study design was implemented based on Israeli electronic health records from 2013 to 2021 with national coverage. The period coinciding with the COVID-19 pandemic biopsychosocial exposures from March 2020 to February 2021 was classified as exposed, otherwise unexposed. The effect of the COVID-19 pandemic on incident schizophrenia was quantified by fitting a Poisson regression and modeling the relative risk (RR) and corresponding 95% confidence intervals (CI). Three scenarios were projected from the third lockdown to 10 months to forecast incident schizophrenia rates and their associated 95% prediction intervals (PI).
ResultsThe total population (N = 736,356) yielded 4,310 cases of incident schizophrenia over time. The primary analysis showed that the period exposed to the COVID-19 pandemic was associated with a reduced RR (RR = 0.81, 95% CI = 0.73, 0.91, p < 0.001). This conclusion was supported in 12 sensitivity analyses, including scrutinizing lockdowns and COVID-19 infection status. Two of three forecast scenarios projected an incident increase (6.74, 95% PI = 5.80, 7.84; 7.40, 95% PI = 6.36, 8.60).
ConclusionsThe reduced risk of schizophrenia during the pandemic suggests no immediate triggering of new onsets either by the virus or the pandemic-induced psychosocial adversities. Once restrictions are lifted, the increased projected presentations have implications for clinicians and healthcare policy.
Capturing adolescents in need of psychiatric care with psychopathological symptoms: A population-based cohort study
- Anat Rotstein, Judy Goldenberg, Suzan Fund, Stephen Z. Levine, Abraham Reichenberg
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- Journal:
- European Psychiatry / Volume 64 / Issue 1 / 2021
- Published online by Cambridge University Press:
- 29 November 2021, e76
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Background
The current study aims to overcome past methodological limitations and capture adolescents in need of psychiatric care with psychopathological symptoms in a cohort with unrestricted access to mental health professionals.
MethodsThe study source population consisted of a random sample of adolescents aged 16-17 years (N=1,369) assessed by the Israeli Draft Board. An adapted version of the Brief Symptom Inventory was used to identify clinically relevant psychopathological symptoms with scores categorized as severe if they were in the top 10th percentile of symptoms, otherwise not severe. An independent interview with a subsequent referral to a mental health professional was used to categorize adolescents in need of psychiatric care. To examine the association between severe psychopathological symptoms and the need for psychiatric care, logistic regression models were fitted unadjusted and adjusted for age, sex, and intellectual assessment scores. Adjusted classification measures were estimated to examine the utility of severe psychopathological symptoms for clinical prediction of need for psychiatric care.
ResultsInformation on 1,283 adolescents was available in the final analytic sample. Logistic regression modeling showed a statistically significant (p<0.001) association between self-reported severe psychopathological symptoms and the need for psychiatric care (OR adjusted: 4.38; 95% CI: 3.55–5.40). Severe psychopathological symptoms had a classification accuracy of 83% (CI: 81%–85%).
ConclusionsSevere psychopathological symptoms, although accounting for a fair proportion of treatment seeking, would perhaps be better useful for classification purposes alongside other variables rather than in isolation.
Attempted suicide rates before and during the COVID-19 pandemic: interrupted time series analysis of a nationally representative sample
- Yael Travis-Lumer, Arad Kodesh, Yair Goldberg, Sophia Frangou, Stephen Z. Levine
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- Journal:
- Psychological Medicine / Volume 53 / Issue 6 / April 2023
- Published online by Cambridge University Press:
- 19 October 2021, pp. 2485-2491
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Background
To characterize the association between the protracted biopsychosocial coronavirus disease 2019 (COVID-19) pandemic exposures and incident suicide attempt rates.
MethodsData were from a nationally representative cohort based on electronic health records from January 2013 to February 2021 (N = 852 233), with an interrupted time series study design. For the primary analysis, the effect of COVID-19 pandemic on incident suicide attempts warranting in-patient hospital treatment was quantified by fitting a Poisson regression and modeling the relative risk (RR) and the corresponding 95% confidence intervals (CIs). Scenarios were forecast to predict attempted suicide rates at 10 months after social mitigation strategies. Fourteen sensitivity analyses were performed to test the robustness of the results.
ResultsDespite the increasing trend in the unexposed interval, the interval exposed to the COVID-19 pandemic was statistically significant (p < 0.001) associated with a reduced RR of incident attempted suicide (RR = 0.63, 95% CI 0.52–0.78). Consistent with the primary analysis, sensitivity analysis of sociodemographic groups and methodological factors were statistically significant (p < 0.05). No effect modification was identified for COVID-19 lockdown intervals or COVID-19 illness status. All three forecast scenarios at 10 months projected a suicide attempt rate increase from 12.49 (7.42–21.01) to 21.38 (12.71–35.99).
ConclusionsThe interval exposed to the protracted mass social trauma of the COVID-19 pandemic was associated with a lower suicide attempt rate compared to the unexposed interval. However, this trend is likely to reverse 10 months after lifting social mitigation policies, underscoring the need for enhanced implementation of public health policy for suicide prevention.
Maternal health around pregnancy and autism risk: a diagnosis-wide, population-based study
- Arad Kodesh, Stephen Z. Levine, Vahe Khachadourian, Rayees Rahman, Avner Schlessinger, Paul F. O'Reilly, Jakob Grove, Diana Schendel, Joseph D. Buxbaum, Lisa Croen, Abraham Reichenberg, Sven Sandin, Magdalena Janecka
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- Psychological Medicine / Volume 52 / Issue 16 / December 2022
- Published online by Cambridge University Press:
- 26 March 2021, pp. 4076-4084
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Background
Many studies have reported an increased risk of autism spectrum disorder (ASD) associated with some maternal diagnoses in pregnancy. However, such associations have not been studied systematically, accounting for comorbidity between maternal disorders. Therefore our aim was to comprehensively test the associations between maternal diagnoses around pregnancy and ASD risk in offspring.
MethodsThis exploratory case–cohort study included children born in Israel from 1997 to 2008, and followed up until 2015. We used information on all ICD-9 codes received by their mothers during pregnancy and the preceding year. ASD risk associated with each of those conditions was calculated using Cox proportional hazards regression, adjusted for the confounders (birth year, maternal age, socioeconomic status and number of ICD-9 diagnoses during the exposure period).
ResultsThe analytic sample consisted of 80 187 individuals (1132 cases, 79 055 controls), with 822 unique ICD-9 codes recorded in their mothers. After extensive quality control, 22 maternal diagnoses were nominally significantly associated with offspring ASD, with 16 of those surviving subsequent filtering steps (permutation testing, multiple testing correction, multiple regression). Among those, we recorded an increased risk of ASD associated with metabolic [e.g. hypertension; HR = 2.74 (1.92–3.90), p = 2.43 × 10−8], genitourinary [e.g. non-inflammatory disorders of cervix; HR = 1.88 (1.38–2.57), p = 7.06 × 10−5] and psychiatric [depressive disorder; HR = 2.11 (1.32–3.35), p = 1.70 × 10−3] diagnoses. Meanwhile, mothers of children with ASD were less likely to attend prenatal care appointment [HR = 0.62 (0.54–0.71), p = 1.80 × 10−11].
ConclusionsSixteen maternal diagnoses were associated with ASD in the offspring, after rigorous filtering of potential false-positive associations. Replication in other cohorts and further research to understand the mechanisms underlying the observed associations with ASD are warranted.
Quantifying the heterogeneity of cognitive functioning in Alzheimer’s disease to extend the placebo-treatment dichotomy: Latent class analysis of individual-participant data from five pivotal randomized clinical trials of donepezil
- Stephen Z. Levine, Yair Goldberg, Kazufumi Yoshida, Myrto Samara, Andrea Cipriani, Takeshi Iwatsubo, Stefan Leucht, Toshiaki A. Furawaka
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- Journal:
- European Psychiatry / Volume 64 / Issue 1 / 2021
- Published online by Cambridge University Press:
- 15 February 2021, e16
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Background
The extent and profiles of heterogeneity in cognitive functioning among participants in clinical trials of antidementia medication are unknown. We aimed to quantify and identify profiles of heterogeneity of cognition in Alzheimer’s disease.
MethodsIndividual-level participant data were analyzed from five pivotal clinical trials of donepezil for Alzheimer’s disease (N = 2,919). Based on Alzheimer’s Disease Assessment Scale–Cognitive Subscale total scores from baseline up to week 12, a latent class model was used to identify heterogeneous groups. A logistic regression model was used to examine factors associated with group membership. Sensitivity analysis was conducted, restricted to the donepezil, and then the placebo arm.
ResultsThe latent class model identified three classes labeled as low scorers (i.e., least cognitive impairment; N = 1,666, 76.04%), improvers (N = 27, 1.23%), and high scorers (N = 498, 22.73%). Logistic modeling showed that donepezil compared to placebo was significantly (p < 0.05) positively associated with membership in the improvers class (OR = 6.88, 95% CI = 2.03, 42.95), and negatively with high scorers (OR = 0.79, 95% CI = 0.64, 0.98). Sensitivity analysis restricted to the placebo, then donepezil arms replicated similar heterogeneity patterns.
ConclusionsOur results inform clinicians regarding the extent of heterogeneity in cognitive functioning during treatment and contribute to trial design considerations.
Identification of newborns at risk for autism using electronic medical records and machine learning
- Rayees Rahman, Arad Kodesh, Stephen Z. Levine, Sven Sandin, Abraham Reichenberg, Avner Schlessinger
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- Journal:
- European Psychiatry / Volume 63 / Issue 1 / 2020
- Published online by Cambridge University Press:
- 26 February 2020, e22
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Background.
Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample.
Methods.We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]).
Results.All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset.
Conclusions.We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.
Age of onset and quality of life among males and females with schizophrenia: A national study
- Anat Rotstein, David Roe, Marc Gelkopf, Stephen Z. Levine
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- Journal:
- European Psychiatry / Volume 53 / September 2018
- Published online by Cambridge University Press:
- 27 June 2018, pp. 100-106
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Background:
Age of onset is considered central to understanding the course of schizophrenia, yet little is known regarding its association with quality of life in general, and specifically among males and females.
Aims:To examine the association between the age of schizophrenia onset and quality of life, in general, and among males and females, using data from a national sample and competing statistical models.
Methods:Participants with a diagnosis of schizophrenia (N = 1624) completed the Manchester Short Assessment of Quality of Life (MSA-QoL) and were rated on a parallel measure by their professional caregivers (N = 578). Multiple regression analysis models were computed for self-appraised quality of life, and mixed models with random intercepts were used for caregivers. Six competing models were tested for parsimony for each rating source. Three models without adjustment and three models adjusted for confounding variables. Sensitivity analyses were conducted for males and females separately.
Results:Age of onset was statistically significantly (P <.05) negatively associated with self-appraised and caregiver-appraised quality of life on aggregate and among females. Among males, a significant (P <.01) quadratic effect of onset age on self-appraised quality of life demonstrated a negative association up to onset age of 36.67 years, after which the association was positive.
ConclusionsAn earlier age of onset is associated with a better quality of life in schizophrenia which is tentatively explained by social decline. Specific trends in psychiatric symptom severity may account for this association among females while social advantages may account for the particular results found among males.
Contributors
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- By Mitchell Aboulafia, Frederick Adams, Marilyn McCord Adams, Robert M. Adams, Laird Addis, James W. Allard, David Allison, William P. Alston, Karl Ameriks, C. Anthony Anderson, David Leech Anderson, Lanier Anderson, Roger Ariew, David Armstrong, Denis G. Arnold, E. J. Ashworth, Margaret Atherton, Robin Attfield, Bruce Aune, Edward Wilson Averill, Jody Azzouni, Kent Bach, Andrew Bailey, Lynne Rudder Baker, Thomas R. Baldwin, Jon Barwise, George Bealer, William Bechtel, Lawrence C. Becker, Mark A. Bedau, Ernst Behler, José A. Benardete, Ermanno Bencivenga, Jan Berg, Michael Bergmann, Robert L. Bernasconi, Sven Bernecker, Bernard Berofsky, Rod Bertolet, Charles J. Beyer, Christian Beyer, Joseph Bien, Joseph Bien, Peg Birmingham, Ivan Boh, James Bohman, Daniel Bonevac, Laurence BonJour, William J. Bouwsma, Raymond D. Bradley, Myles Brand, Richard B. Brandt, Michael E. Bratman, Stephen E. Braude, Daniel Breazeale, Angela Breitenbach, Jason Bridges, David O. Brink, Gordon G. Brittan, Justin Broackes, Dan W. Brock, Aaron Bronfman, Jeffrey E. Brower, Bartosz Brozek, Anthony Brueckner, Jeffrey Bub, Lara Buchak, Otavio Bueno, Ann E. Bumpus, Robert W. Burch, John Burgess, Arthur W. Burks, Panayot Butchvarov, Robert E. Butts, Marina Bykova, Patrick Byrne, David Carr, Noël Carroll, Edward S. Casey, Victor Caston, Victor Caston, Albert Casullo, Robert L. Causey, Alan K. L. Chan, Ruth Chang, Deen K. Chatterjee, Andrew Chignell, Roderick M. Chisholm, Kelly J. Clark, E. J. Coffman, Robin Collins, Brian P. Copenhaver, John Corcoran, John Cottingham, Roger Crisp, Frederick J. Crosson, Antonio S. Cua, Phillip D. Cummins, Martin Curd, Adam Cureton, Andrew Cutrofello, Stephen Darwall, Paul Sheldon Davies, Wayne A. Davis, Timothy Joseph Day, Claudio de Almeida, Mario De Caro, Mario De Caro, John Deigh, C. F. Delaney, Daniel C. Dennett, Michael R. DePaul, Michael Detlefsen, Daniel Trent Devereux, Philip E. Devine, John M. Dillon, Martin C. Dillon, Robert DiSalle, Mary Domski, Alan Donagan, Paul Draper, Fred Dretske, Mircea Dumitru, Wilhelm Dupré, Gerald Dworkin, John Earman, Ellery Eells, Catherine Z. Elgin, Berent Enç, Ronald P. Endicott, Edward Erwin, John Etchemendy, C. Stephen Evans, Susan L. Feagin, Solomon Feferman, Richard Feldman, Arthur Fine, Maurice A. Finocchiaro, William FitzPatrick, Richard E. Flathman, Gvozden Flego, Richard Foley, Graeme Forbes, Rainer Forst, Malcolm R. Forster, Daniel Fouke, Patrick Francken, Samuel Freeman, Elizabeth Fricker, Miranda Fricker, Michael Friedman, Michael Fuerstein, Richard A. Fumerton, Alan Gabbey, Pieranna Garavaso, Daniel Garber, Jorge L. A. Garcia, Robert K. Garcia, Don Garrett, Philip Gasper, Gerald Gaus, Berys Gaut, Bernard Gert, Roger F. Gibson, Cody Gilmore, Carl Ginet, Alan H. Goldman, Alvin I. Goldman, Alfonso Gömez-Lobo, Lenn E. Goodman, Robert M. Gordon, Stefan Gosepath, Jorge J. E. Gracia, Daniel W. Graham, George A. Graham, Peter J. Graham, Richard E. Grandy, I. Grattan-Guinness, John Greco, Philip T. Grier, Nicholas Griffin, Nicholas Griffin, David A. Griffiths, Paul J. Griffiths, Stephen R. Grimm, Charles L. Griswold, Charles B. Guignon, Pete A. Y. Gunter, Dimitri Gutas, Gary Gutting, Paul Guyer, Kwame Gyekye, Oscar A. Haac, Raul Hakli, Raul Hakli, Michael Hallett, Edward C. Halper, Jean Hampton, R. James Hankinson, K. R. Hanley, Russell Hardin, Robert M. Harnish, William Harper, David Harrah, Kevin Hart, Ali Hasan, William Hasker, John Haugeland, Roger Hausheer, William Heald, Peter Heath, Richard Heck, John F. Heil, Vincent F. Hendricks, Stephen Hetherington, Francis Heylighen, Kathleen Marie Higgins, Risto Hilpinen, Harold T. Hodes, Joshua Hoffman, Alan Holland, Robert L. Holmes, Richard Holton, Brad W. Hooker, Terence E. Horgan, Tamara Horowitz, Paul Horwich, Vittorio Hösle, Paul Hoβfeld, Daniel Howard-Snyder, Frances Howard-Snyder, Anne Hudson, Deal W. Hudson, Carl A. Huffman, David L. Hull, Patricia Huntington, Thomas Hurka, Paul Hurley, Rosalind Hursthouse, Guillermo Hurtado, Ronald E. Hustwit, Sarah Hutton, Jonathan Jenkins Ichikawa, Harry A. Ide, David Ingram, Philip J. Ivanhoe, Alfred L. Ivry, Frank Jackson, Dale Jacquette, Joseph Jedwab, Richard Jeffrey, David Alan Johnson, Edward Johnson, Mark D. Jordan, Richard Joyce, Hwa Yol Jung, Robert Hillary Kane, Tomis Kapitan, Jacquelyn Ann K. Kegley, James A. Keller, Ralph Kennedy, Sergei Khoruzhii, Jaegwon Kim, Yersu Kim, Nathan L. King, Patricia Kitcher, Peter D. Klein, E. D. Klemke, Virginia Klenk, George L. Kline, Christian Klotz, Simo Knuuttila, Joseph J. Kockelmans, Konstantin Kolenda, Sebastian Tomasz Kołodziejczyk, Isaac Kramnick, Richard Kraut, Fred Kroon, Manfred Kuehn, Steven T. Kuhn, Henry E. Kyburg, John Lachs, Jennifer Lackey, Stephen E. Lahey, Andrea Lavazza, Thomas H. Leahey, Joo Heung Lee, Keith Lehrer, Dorothy Leland, Noah M. 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- By Harold P. Adams, Colum F. Amory, Anne Angelillo-Scherrer, Irena Anselm, Marcel Arnold, Robert W. Baloh, Ralf W. Baumgartner, José Biller, Valérie Biousse, Matthias Bischof, Julien Bogousslavsky, Natan M. Bornstein, Marie Germaine Bousser, Robin L. Brey, John C. M. Brust, Alan Bryer, Olivier Calvetti, Louis R. Caplan, José Castillo, Hugues Chabriat, Chin-Sang Chung, Charlotte Cordonnier, Steven C. Cramer, Luís Cunha, Rima M. Dafer, John F. Dashe, Cyrus K. Dastur, Antonio Dávalos, Larry E. Davis, Patricia Davis, Stephen M. Davis, Jan L. De Bleecker, Michael A. De Georgia, Amir R. Dehdashti, Oscar H. Del Brutto, Jacques L. De Reuck, Hans-Christoph Diener, Kathleen B. Digre, Vivian U. Fritz, Nancy Futrell, Bhuwan P. Garg, Philip B. Gorelick, Glenn D. Graham, Alexander Y. Gur, John J. Halperin, Michael Hennerici, Isabel Lestro Henriques, Roberto C. Heros, Daniel B. Hier, Lorenz Hirt, Joanna C. Jen, Taro Kaibara, Sumit Kapoor, Sarosh M. Katrak, Siddharth Kharkar, Walter J. Koroshetz, Monisha Kumar, Sandeep Kumar, Emre Kumral, Tobias Kurth, Rogelio Leira, Steven R. Levine, Didier Leys, Doris Lin, Jonathan Lipton, Alfredo M. Lopez-Yunez, Betsy B. Love, Ayrton Roberto Massaro, Heinrich P. Mattle, Manu Mehdiratta, John H. Menkes, Philippe Metellus, Reto Meuli, Patrik Michel, Panayiotis Mitsias, Jorge Moncayo-Gaete, Julien Morier, Krassen Nedeltchev, Bernhard Neundörfer, Olukemi A. Olugemo, Nikolaos I. H. Papamitsakis, Stephen D. Reck, Luca Regli, Marc D. Reichhart, Daniele Rigamonti, Michael J. Rivkin, E. Steve Roach, Jose F. Roldan, David Z. Rose, Daniel M. Rosenbaum, N. Paul Rosman, Elayna O. Rubens, Sean I. Savitz, Marc Schapira, Robert J. Schwartzman, Magdy Selim, Yukito Shinohara, Aneesh B. Singhal, Michael A. Sloan, Barney J. Stern, Mathias Sturzenegger, Oriana Thompson, A. Wesley Thevathasan, Jonathan D. Trobe, Michael Varner, Dana Védy, Jorge Vidaurre, Engin Y. Yilmaz, Khaled Zamel, Mathieu Zuber
- Edited by Louis R. Caplan, Julien Bogousslavsky
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- Uncommon Causes of Stroke
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