High-frequency measurement of depressive severity in a patient treated for severe treatment-resistant depression with deep-brain stimulation.

Although there have been previous studies of deep-brain stimulation (DBS), we present, to our knowledge, the first example of high-frequency depressive severity measurement-based DBS treatment in particular and psychiatric treatment in general. Daily post-surgical e-mail prompts for a period of 6 months resulted in 93 administrations of a computerized adaptive test (CAT) of depression severity (CAT-Depression Inventory or CAT-DI) via the internet. There was an average of 3.37 weekly measurements with an average separation of 2.12 days. No additional incentive was provided to the patient for completing the adaptive tests. The patient is a 55-year-old female with six psychiatric hospitalizations for depression, two suicide attempts, marginal response to eight electroconvulsive therapy (ECT) treatments and 35 psychotropic medications. We report results after high-frequency stimulation of the superolateral branch of the medial forebrain bundle. The CAT-DI was used for daily assessments before, during and after (remotely in response to an e-mail prompt) the DBS procedure. Two follow-up Hamilton Depression Scales (HAM-Ds) were also collected. Response to treatment varied markedly, with a decrease from severe (>75) to mild (60), which is three times the size of the uncertainty level. Although the HAM-D scores decreased, they missed the more complete temporal pattern identified by CAT-DI daily monitoring. We demonstrated feasibility of daily depressive severity measurement at high levels of precision and compliance. Clinician ratings confirm the general pattern of treatment benefit, but mask the marked variability in mood and more marked periods of benefit and decline.

Pollen counts and suicide rates. Association not replicated.

OBJECTIVE: To replicate a previously reported association between pollen counts and county suicide rates in the continental United States, across space and time. METHOD: The authors evaluated the relationship between airborne pollen counts and suicide rates in 42 counties of the continental United States, containing a pollen-counting station participating in the Aeroallergen Monitoring Network in the United States (N = 120,076 suicides), considering years' quarter, age group, sex, race, rural/urban location, number of local psychiatrists, and median household income, from 1999 to 2002. The county-level effects were broken into between-county and within-county. RESULTS: No within-county effects were found. Between-county effects for grass and ragweed pollen on suicide rates lost statistical significance after adjustment for median income, number of psychiatrists, and urban vs. rural location. CONCLUSION: Future research is necessary to reappraise the previously reported relationship between pollen levels and suicide rates that may have been driven by socioeconomic confounders.

The development of study exit criteria for evaluating antimanic compounds.

BACKGROUND: There is increasing interest on the part of investigators and the public at large in finding ways to study and improve treatments for the seriously mentally ill without exposing such individuals to unnecessary risks. One group of particular interest in this regard are patients suffering from acute mania. We set out to define "exit" criteria or novel clinical endpoints that might help to assess the efficacy of antimanic compounds. We sought a method that would be safer, more economical, and less sensitive to nonspecific factors in the clinical environment while still allowing unambiguous assessment of efficacy. METHOD: From a pool of subjects being screened for or already participating in intervention studies, we retrospectively identified 76 admissions of patients with a manic or mixed episode according to DSM-IV. We fit a mixed-effects regression model to all available data obtained using the Bech-Rafaelsen Mania Scale from admission to day 28 of treatment. Using the estimated model coefficients, we obtained empirical Bayes (EB) estimates of each subject's trend coefficients based on (1) all available data and (2) data through day 11 of treatment for mania. RESULTS: We found a high correlation (r = .67) between EB estimates of final response at day 28 and actual day 28 scores on the Bech-Rafaelsen scale based on scores through day 11. When subjects were categorized as full, partial, or nonresponders according to their final Bech-Rafaelsen score, we were able to show that only 2 of the 23 predicted nonresponders became full responders, 27 of the 31 predicted full responders became full responders, and 16 of the 22 predicted partial responders became partial or full responders. CONCLUSION: We conclude on the basis of this chart review study that it should be possible to define exit criteria for trials assessing the efficacy of antimanic compounds on the basis of relatively short duration exposure to experimental treatment.

Oppositional defiant disorder with onset in preschool years: longitudinal stability and pathways to other disorders.

OBJECTIVE: To examine the stability and change in oppositional defiant disorder (ODD) with onset among preschool children in a pediatric sample. METHOD: A total of 510 children aged 2-5 years were enrolled initially in 1989-1990 (mean age 3.42 years); 280 participated in five waves of data collection over a period of 48 to 72 months (mean wave 5 age, 8.35 years). Test batteries varied by age, but they included the Child Behavior Checklist, developmental evaluation, Rochester Adaptive Behavior Inventory, and a play session (before age 7 years) and a structured interview (Diagnostic Interview for Children and Adolescents, parent and child versions) at ages 7+ years. Consensus diagnoses were assigned by using best-estimate procedures. RESULTS: Wave 1 single-diagnosis ODD showed a significant relationship with both single-diagnosis ODD and single-diagnosis attention-deficit hyperactivity disorder (ADHD) at subsequent waves, but not with single-diagnosis anxiety or mood disorders. Single-diagnosis ODD at wave 1 was associated with later comorbidity of ODD/ADHD, ODD/anxiety, and ODD/mood disorders. Stability across waves 2 through 5 was moderate to high for comorbid ODD/anxiety and ODD/ADHD; low to moderate stability for single-diagnosis ODD and single-diagnosis mood disorder; and low for mood disorder, single-diagnosis ADHD, and single-diagnosis anxiety disorder. CONCLUSIONS: Preschool children with ODD are likely to continue to exhibit disorder, with increasing comorbidity with ADHD, anxiety, or mood disorders.

Rational service planning in pediatric primary care: continuity and change in psychopathology among children enrolled in pediatric practices.

OBJECTIVE: To examine the stability of the occurrence of psychiatric disorders in a nonpsychiatric sample of young children. METHOD: There were 510 children ages 2-5 years enrolled through pediatric practices, with 391 children participating in the second wave, and 344 in the third wave of data collection 42-48 months later. The assessment battery administered at each wave yielded best-estimate consensus DSM-III-R diagnoses and dimensional assessments of psychopathology. RESULTS: The prevalence of disruptive disorders (DDs) decreased, while emotional disorders (EDs), other disorders, and comorbid DD increased. The DDs were associated with lower family cohesion, more maternal negative affect, stressful life events, and male gender. Comorbid DDs were associated with increasing age and family cohesion. Older children, lower family cohesion, and maternal negative affect were associated with EDs. Time trends for the dimensional assessment of psychopathology was similar to DSM-III-R disorders, but correlates differed. CONCLUSIONS: We discuss implications for service planning in pediatric primary care.

Use of combined Shewhart-CUSUM control charts for ground water monitoring applications.

Statistical properties of combined Shewhart-CUSUM control charts are examined in terms of the site-wide false positive rate, false negative rate, and average run length (i.e., the average number of sampling events that it takes to detect a significant exceedance). The effects of multiple comparisons (i.e., multiple monitoring wells and constituents), verification resampling, background sample size, and background updating are incorporated into the analysis. The effects of (1) verification resampling plans including no resampling, pass one resample, pass one of two resamples, and pass two of two resamples; (2) background sample sizes (n = 8,12,16,20, and 24); and (3) multiple comparisons (k = 1 to 500 future comparisons) were examined. In addition, various alternative control limit factors (h = SCL= 4.5, 5.0,5.5, and 6.0) were considered, where the control limit is x + hs. Results of simulations revealed that it is possible to balance site-wide false positive and false negative rates at low levels using combined Shewhart-CUSUM control charts. The results revealed that it is important to select the appropriate multiplier, verification resampling plan, number of background samples, and number of intervening samples prior to updating background.

Psychiatric disorders with onset in the preschool years: I. Stability of diagnoses.

OBJECTIVE: To examine the stability of psychiatric disorders with onset in preschool years. METHOD: Five hundred ten children aged 2 through 5 years enrolled initially, with 344 participating in a third wave of data collection 42 through 48 months later. The test batteries used for diagnoses varied by child's age, but they included the Child Behavior Checklist, developmental evaluation, Rochester Adaptive Behavior Inventory and a play session (under age 7 years), and a structured interview (Diagnostic Interview for Children and Adolescents, for parent and child) (ages 7 and older). Consensus DSM-III-R diagnoses were assigned using best-estimate procedures. RESULTS: Intraclass correlations were 0.497 for emotional disorders, 0.718 for disruptive disorders, 0.457 for other diagnoses, and 0.544 for disruptive disorders comorbid with another disorder, indicating moderate stability for all groups of disorders. More than 50% of the children who were aged 2 through 3 years at wave 1 continued to have some psychiatric disorder at wave 2 or 3. Rates were higher for children aged 4 through 5 initially; approximately two thirds were cases subsequently. Odds ratios indicate that having an emotional or disruptive disorder is a strong risk factor for later diagnoses. CONCLUSIONS: While some preschool children in primary care "grow out of" their disorder, an equally large number do not; this finding supports the need for early detection and intervention.

Psychiatric disorders with onset in the preschool years: II. Correlates and predictors of stable case status.

OBJECTIVE: To examine the correlates and predictors of stability and change in psychiatric disorder occurring among preschool children in a nonpsychiatric, primary care pediatric sample. METHOD: Five hundred ten children aged 2 through 5 years were enrolled; 344 participated in a third wave of data collection 42 through 48 months later. Consensus diagnoses were assigned using best-estimate procedures; variables of maternal psychopathology, family climate, and life stresses were the correlates/predictors studied. RESULTS: For children who were cases initially, family cohesion predicted diagnostic stability. Among initial noncases, those remaining noncases experienced increased family cohesion; for those who later became cases, family cohesion declined. Negative life events declined when children were consistently noncases. Children who were initially noncases but were cases at the two subsequent waves had the highest levels of maternal negative affect. Predictors at wave 1 for wave 2 cases status included lower socioeconomic status, less family cohesiveness, and greater family inhibition/control. Wave 2 correlates of wave 2 status included older children and negative life events. Wave 2 predictors of wave 3 status included being older, while wave 3 correlates of wave 3 case status included older children and higher maternal negative affect. CONCLUSIONS: Family context contributes to the maintenance and onset of problems beginning in the preschool years.

Emergence of childhood psychiatric disorders: a multivariate probit analysis.

We applied a computationally practical form of probit analysis for multiple response variables to data on early childhood development of four psychiatric disorders: disruptive disorders (DD-attention deficit disorders, oppositional defiant disorder, conduct disorder); adjustment disorders (ADJ); emotional disorders (ED-all anxiety disorders, depression); and other DSM-III-R Axis I disorders (OTHER). In addition to estimating the intercept slope and higher order polynomial terms for each age versus diagnosis regression, we estimated simultaneously the correlation among the four diagnostic categories. We then took into account the correlation found among these four diagnostic categories when testing the hypothesis of no age effect, which would have been ignored in a piecemeal univariate approach. Regression lines for diagnostic prevalence indicate a linear increase for OTHER disorders, and a curvilinear increase for ED. We then used expected frequencies of individual response patterns (that is, the 2(4) = 16 possible diagnostic combinations) in obtaining more precise estimates of diagnostic comorbidity and its relation to age. We further generalize the Bock and Gibbons model to alternative specification of the random-effects distribution (that is, they assumed multivariate normality), illustrate how one can estimate the random-effects distribution empirically, and study the robustness of parameter estimates to specification of the random-effects distribution.

Psychopathology and health care use among preschool children: a retrospective analysis.

OBJECTIVE: To examine the relationship between psychopathology and health care utilization beginning in the preschool (ages 2 to 5) years. METHOD: Five hundred ten preschool children were enrolled through 68 primary care physicians. The test battery used for diagnoses included the Child Behavior Checklist, a developmental evaluation, the Rochester Adaptive Behavior Inventory, and a videotaped play session. Consensus DSM-III-R diagnoses were assigned using best-estimate procedures. Frequency of primary care visits was established through 1-year retrospective record review; mothers estimated total visits and emergency department (ED) use. RESULTS: Logistic regression models showed that a DSM-III-R diagnosis was related to increased ED use but not primary care or total visits. Greater functional impairment was associated with fewer primary care visits and more ED visits. Total, internalizing, and externalizing behavior problem scores were associated with increased primary care and total visits; ED visits were associated with increased total and internalizing problems. Child's health status consistently correlated with utilization. CONCLUSION: There is a consistent relationship between health care use and child psychopathology beginning in the preschool years.

Random effects probit and logistic regression models for three-level data.

In analysis of binary data from clustered and longitudinal studies, random effect models have been recently developed to accommodate two-level problems such as subjects nested within clusters or repeated classifications within subjects. Unfortunately, these models cannot be applied to three-level problems that occur frequently in practice. For example, multicenter longitudinal clinical trials involve repeated assessments within individuals and individuals are nested within study centers. This combination of clustered and longitudinal data represents the classic three-level problem in biometry. Similarly, in prevention studies, various educational programs designed to minimize risk taking behavior (e.g., smoking prevention and cessation) may be compared where randomization to various design conditions is at the level of the school and the intervention is performed at the level of the classroom. Previous statistical approaches to the three-level problem for binary response data have either ignored one level of nesting, treated it as a fixed effect, or used first- and second-order Taylor series expansions of the logarithm of the conditional likelihood to linearize these models and estimate model parameters using more conventional procedures for measurement data. Recent studies indicate that these approximate solutions exhibit considerable bias and provide little advantage over use of traditional logistic regression analysis ignoring the hierarchical structure. In this paper, we generalize earlier results for two-level random effects probit and logistic regression models to the three-level case. Parameter estimation is based on full-information maximum marginal likelihood estimation (MMLE) using numerical quadrature to approximate the multiple random effects. The model is illustrated using data from 135 classrooms from 28 schools on the effects of two smoking cessation interventions.

Weighted least-squares approach to calculating limits of detection and quantification by modeling variability as a function of concentration.

The limit of detection and limit of quantification are current critical issues in environmental testing. In most laboratories, limits are currently calculated on the basis of the standard deviation of replicate analyses at a single concentration. However, since the standard deviation depends on concentration, these single-concentration techniques result in limits that are directly dependent on spiking concentration. A more rigorous approach uses a weighted least-squares regression analysis of replicates spiked at a series of concentrations [Formula: see text] a calibration design. In this work, the use of weighted tolerance intervals is introduced for estimating detection and quantification limits. In addition, models for estimating the weights used in calculating weighted prediction intervals and weighted tolerance intervals are presented. Using this method, detection and quantification limits were calculated for gas chromatographic analyses of 16 polychlorinated biphenyls. Results show that the approach developed provides improved estimates of analytical limits and that the single-concentration approaches currently in wide use are seriously flawed. Future work should reduce the data needed for the calibration design approach so that more rigorous detection and quantification limits can be routinely applied.

High-dimensional multivariate probit analysis.

A computationally practical form of probit analysis for multiple response variables based on an assumed common factor model for the latent tolerances is proposed. Numerical integration over the factor space provides maximum likelihood estimation of the probit regression parameters and of the probabilities of response combinations under the model. The procedure is applied to five variables from the Pneumoconiosis Field Trial, two variables of which were previously analyzed by Ashford and Sowden (1970, Biometrics 26, 535-546).

Length of inpatient stay and recidivism among patients with schizophrenia.

OBJECTIVE: The study examined whether length of hospital stay is related to recidivism among psychiatric patients. A quasi-experimental approach was used to address limitations of controlled and epidemiological research. METHODS: Three matched groups, each consisting of 55 inpatients with schizophrenia, were selected from public psychiatric units with different mean lengths of stay. Regression models were used to compare the groups on three variables: time to first readmission (survival analysis), number of readmissions (ordinal logit regression), and total time in the community in the postdischarge year (multiple linear regression). RESULTS: An analysis based on the units with different lengths of stay, which was similar to that typically used in controlled studies, found no differences in the three outcome measures. However, a second analysis that examined data for all patients irrespective of their unit assignment found that inpatients treated for 30 days or less relapsed sooner than those with stays longer than 30 days. The disparity in results was largely due to overlapping quasi-experimental conditions: many patients on the short-stay units had a long lengths of stay, and vice versa. The first analysis supports an administrative policy of short stays. The second reinforces previous findings that a group of patients, primarily young males with onset of illness at an early age and multiple previous hospitalizations, is at greater risk of relapse with short-term treatment. CONCLUSIONS: The apparent contradiction between a unit- or patient-based analysis suggests that unit-based results should be interpreted with caution when used to make clinical or utilization review decisions.

MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors.

MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model can be used for analysis of unbalanced longitudinal data, where individuals may be measured at a different number of timepoints, or even at different timepoints. Autocorrelated errors of a general form or following an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also be used for analysis of clustered data, where the mixed-effects model assumes data within clusters are dependent. The degree of dependency is estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm and a Fisher-scoring solution. For the scoring solution, the covariance matrix of the random effects is expressed in its Gaussian decomposition, and the diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished using an empirical Bayes approach. Examples illustrating usage and features of MIXREG are provided.

MIXOR: a computer program for mixed-effects ordinal regression analysis.

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

Prevalence rates and correlates of psychiatric disorders among preschool children.

OBJECTIVE: To determine the prevalence and correlates of psychiatric disorders among preschool children in a primary care pediatric sample. METHOD: In a two-stage design, 3,860 preschool children were screened; 510 received fuller evaluations. RESULTS: For quantitative assessment of disorder (> or = 90th percentile), prevalence of behavior problems was 8.3%. "Probable" occurrence of an Axis I DSM-III-R disorder was 21.4% (9.1%, severe). Logistic regression analyses indicated significant demographic correlates for quantitative outcomes (older age, minority status, male sex, low socio-economic status, father absence, small family size) but not for DSM-III-R diagnoses. Maternal and family characteristics were generally not significant. Child correlates included activity level, timidity, persistence, and IQ. CONCLUSIONS: Overall prevalence of disorder was consistent with rates for older children; correlates varied by approach used for classification.