Archives of General Psychiatry

doi: 10.1001/archpsyc.58.7.628pmid: N/A

Functional brain imaging studies of major depressive disorder (MDD) have demonstrated abnormal activity in prefrontal cortex (PFC), anterior cingulate gyrus, and the temporal lobe that normalizes with treatment. Using positron emission tomography, Brody et alArticle compared subjects with MDD with normal controls at baseline and examined changes in regional brain activity from before to after treatment with either medication (paroxetine) or interpersonal psychotherapy. Subjects with MDD had higher normalized activity than controls at baseline in PFC, caudate, and thalamus, and lower activity in the temporal lobe. These abnormalities tended to normalize with treatment. Regional changes appeared similar with both forms of treatment. Martin et alArticle studied major depressive disorder with sequential SPECT over 6 weeks in 15 patients who took the antidepressant venlafaxine and 13 patients who were solely treated with interpersonal psychotherapy. Blood flow increased with interpersonal psychotherapy in the limbic system but not with venlafaxine, while both treatments had increased blood flow in the basal ganglia. Commentaries by Sackeim and Thase are included.ArticleArticle Zhou et alArticle found less vasopressin messenger RNA expression and more vasopressin immunoreactivity in the suprachiasmatic nucleus, the mammalian circadian pacemaker, of depressed subjects as compared with controls. These findings suggest a change in the balance between vasopressin production and transport, resulting in a diminished functional ability of the sprachiasmatic nucleus in depression. Psychotic illness is more prevalent in urban areas. van Os et alArticle hypothesized that the factors that increase risk for psychotic disorder in urban areas contribute to a larger continuum of preclinical psychotic experiences, of which population only a fraction develops illness. They found that individuals living in more urbanized areas not only had higher rates of DSM-III-R psychotic disorders, but also more often displayed mental states characterized by delusional thinking and hallucinatory experiences in the absence of clinical disorder. The findings suggest that urban risk affects the community level of psychotic symptoms rather than illness in a few individuals. Fanous et alArticle found significant correlations between positive and negative symptoms in schizophrenic probands, and, respectively, positive and negative schizotypal symptoms in their nonpsychotic relatives. The factors influencing the dimensions of schizophrenic psychopathology appear to be etiologically related to those influencing the dimensions of schizotypal personality. In a large register-based study, Bennedsen et alArticle reported a large increase in the risk of infant death and a small increase in the risk of congenital malformations among children of women with schizophrenia. The high risk of infant death was largely explained by an increased risk of sudden infant death syndrome. The association between schizophrenia and sudden infant death syndrome should be investigated in future studies. In a controlled, 12-week trial of early clonazepam coadministration with sertraline, Goddard et alArticle observed superior early stabilization in panic disorder patients assigned to clonazepam/sertraline treatment compared with those receiving placebo/sertraline treatment. Both treatment groups experienced similar rates of side effects, and tapering after 4 weeks of clonazepam or placebo was uneventful. Hser et alArticle studied community-based drug abuse treatment for adolescents and found that about half of the adolescents had stopped regular use of marijuana in the year after leaving treatment. Treatment was also effective in achieving important behavioral and psychological improvements. Longer stays in treatment were positively associated with several favorable outcomes, although length of time in treatment was generally short. Little is known about the long-term health benefits of programs to improve quality of care for depression in community-based primary care settings. Sherbourne et alArticle compare 2-year outcomes for usual care vs 2 programs providing practices with training and materials that support guideline-concordant treatment and extra resources for either medication follow-up or improved access to psychotherapy, while maintaining treatment choice. Both programs improved 1-year outcomes but only the therapy resource intervention improved second-year outcomes relative to usual care.

Brody, Arthur L.; Saxena, Sanjaya; Stoessel, Paula; Gillies, Laurie A.; Fairbanks, Lynn A.; Alborzian, Shervin; Phelps, Michael E.; Huang, Sung-Cheng; Wu, Hsiao-Ming; Ho, Matthew L.; Ho, Mai K.; Au, Scott C.; Maidment, Karron; Baxter, Jr, Lewis R.

Zhou, Jiang-Ning; Riemersma, Rixt F.; Unmehopa, Unga A.; Hoogendijk, Witte J. G.; van Heerikhuize, Joop J.; Hofman, Michel A.; Swaab, Dick F.

doi: 10.1001/archpsyc.58.7.655pmid: 11448372

BackgroundCircadian rhythm disturbances are frequently found in depressed subjects. Although it has been presumed that these disturbances may reflect a disorder of the circadian pacemaker, this has never been established. The suprachiasmatic nucleus (SCN) is the pacemaker of the circadian timing system in mammals, and arginine vasopressin (AVP) is one of its major neuropeptides. As peptide content is often taken as a measure for activity, we hypothesized that a decreased number of AVP-immunoreactive (AVP-IR) neurons and amount of AVP–messenger RNA (mRNA) would be present in the SCN of depressed subjects.MethodsBrains of 11 subjects suffering from major depression (8 cases) and bipolar disorder (3 cases), and of 11 controls, matched for sex, age, and clock time at death, were collected. The number of AVP-IR neurons in the SCN was determined by means of a digitizer (CalComp Inc, Reading, England). The amount of AVP-mRNA expression in the SCN was quantified with the Interaktive Bild Analyse System image analysis system (Kontron, Munich, Germany).ResultsIn depressed subjects, the number of AVP-IR neurons in the SCN was more than one and a half times higher than in controls, while the total masked area of silver grains, as an estimate of the amount of AVP-mRNA, was about one half that of controls.ConclusionsContrary to our hypothesis, an increase in the number of AVP-IR neurons in the SCN in depression was found, together with an expected decrease in AVP-mRNA. These findings suggest that, in depressed patients, both the synthesis and release of AVP in the SCN is reduced, resulting in an impaired functional ability. A disbalance between AVP production and transport needs further investigation in future studies.THE SUPRACHIASMATIC nucleus (SCN) is the circadian pacemaker of the mammalian brain, generating and coordinating diurnal rhythms (eg, sleep-wakefulness, body temperature, hormonal rhythms).Over the years, a variety of studies have pointed to the possible involvement of the circadian pacemaker in depression.An argument in favor of this idea is that in the melancholic type of depression, patients feel worst in the morning and typically suffer from early morning awakenings.In addition, a decrease in the amplitude of body temperature is consistently found in depressed patients.Furthermore, the successful treatment of seasonal affective disorder with light therapy,and, to a lesser extent, also of patients with non–seasonal affective disorders,has led to the hypothesis that the effect of bright light on depression acts on the circadian pacemaker via the retinohypothalamic tract.Whether the observed disturbances of circadian rhythms in depression indeed reflect a disorder of the SCN has, however, so far not been established.Another important hypothalamic structure that is involved in depression consists of the corticotropin-releasing hormone (CRH) neurons of the paraventricular nucleus (PVN). The increased number of neurons expressing CRH and the increased amount of CRH–messenger RNA (mRNA) are signs of strong activation of these neurons in depression.These findings are of particular interest because there are similarities between the signs and symptoms of major depression (MD) and the behavioral effects of centrally administered CRH in animalsand CRH overproduction in transgenic mice.Furthermore, there is a functional relationship between arginine vasopressin (AVP) in the SCN and CRH in the PVN. Arginine vasopressin neurons from the SCN inhibit CRH neurons in the PVN of rats.In this way, the SCN plays a key role in the circadian regulation of the hypothalamic-pituitary-adrenal axis resulting in circadian fluctuations of cortisol levels.On the basis of these observations, we hypothesized that the functional ability of the SCN in maintaining normal biological rhythms might be diminished in depression. Our hypothesis was that the number of AVP containing neurons and the amount of AVP-mRNA in the SCN of depressed subjects would be decreased.SUBJECTS AND METHODSSUBJECTSBrains of 11 depressed subjects were collected and matched with 11 controls for sex, age, and clock time at death (Table 1). Brain material of both depressed and control subjects was obtained from the Netherlands Brain Bank. Within the framework of the Netherlands Brain Bank autopsies take place after informed consent is given by the donor and/or the next-of-kin for the following: (1) performing a brain autopsy, (2) the subsequent use of the tissue and fluids obtained for scientific research, and (3) permission to use the donor's medical history for research purposes. The medical records of the control subjects did not report any psychiatric or neurological disease, except for subject C2b. The diagnosis was established by the physician in attendance and confirmed by a psychiatrist (W.J.G.H), after reviewing the medical record. The DSM-IVcriteria were used for the diagnosis of MD and bipolar disorder (BD), at any time during life. No relatives were interviewed to give additional information to the medical record diagnosis. In case data were missing, an additional interview took place with the physician who treated the subject. In this procedure, DSM-IVcriteria for the presence, duration, and severity of symptoms of either MD or BD, as well as the exclusion of other psychiatric and neurological disorders, were systematically scored. Eight patients fulfilled the criteria for MD and 3 fulfilled the criteria for BD (Table 1). Four MD subjects and 2 BD subjects suffered from their last episode just before death. In the 2 BD cases, this last episode was a manic episode. For detailed information on the time of the last episode, see Table 1. A complete overview of the psychiatric medication in the past and in the last month before death for both depressed and control subjects is given in Table 1. The medical record did not reveal any alcohol or other drug abuse among depressed subjects or control subjects at the time of death, but no toxicology screens were performed. Microscopical examination of the liver of subject D11 showed microabcesses and infiltration with neutrophilic and eosinophylic granulocytes. These signs could be compatible with drug intoxication. Potential cases and controls were excluded if not enough material was available to stain the complete SCN. For this reason, 3 controls of the immunocytochemical study were replaced by 3 other controls for the in situ hybridization (ISH) study (C2, C7, and C9) (Table 1).Brain Material of Depressed and Control Subjects*†SubjectSexAge/Age at Onset, yBrain Weight, gPMD, hFix, dTime at DeathMonth of DeathCause of DeathNo. of Episodes/ End of Last Episode‡, No. of Months Before DeathPast Suicide Attempt§Psychiatric Medication TakenIn the Last MonthIn the PastD1M51/41139075282 PMNovemberRespiratory insufficiency, lung emphysema8/7NoLI, HAL, PHT&par;¶BZDC1M49125422333:10 PMNovemberSepsis, colon carcinomaBZD&par;¶D2F55/≤4013207527:45 AMNovemberHeart failure, urosepsis2/DeathYesSSRI, BROMAP, BZD, TCA, PHTC2aF5012107407 AMJanuaryRenal insufficiency, multiple myelomaC2b#F5812217287:15 AMMarchPostoperative coma after craniotomyD3M61/≤50142441354:40 AMOctoberPneumonia1/144NoPHTTCAC3M63125010325 AMJanuaryPneumoniaD4M63/12121020332:15 PMMarchHeart failure>6/DeathNoHAL, BZD, PHTLIC4M78144282412:15 PMJulyCardiac arrhythmiaD5M70/40150044287 PMDecemberHeart failure4/DeathNoSSRI, BZD, CLZC5M7014549338 AMFebruaryPneumonia, renal failureD6M71/5397516384:15 PMFebruaryCerebral ischemia, pneumonia4/DeathYesNoneBZD, MAO-I, TCAC6M7413178601 PMNovemberHeart failure, myocardial infarctionD7M71/≤651109142610:30 PMAugustRespiratory insufficiency≥2/21NoLI, BZD, MAOI, PHTC7aM85140016444:50 PMJulyChronic myelocytic leukemiaC7b#M61222014649:22 PMAprilEsophagus carcinomaD8F72/54128722397 PMJanuaryPneumonia≥3/36NoBZD&par;MAPC8F6312166325:01 PMSeptemberMammocarcinoma, euthanasiaBZD¶BZDD9F72/53111628354:20 AMAprilHeart failure, septic shock, pyelonephritis≥4/108NoBZDMIA, TCAC9aF7313448349:10 AMFebruarySeptic shock, pneumoniaC9b#F65. . .7281:45 AMFebruaryRespiratory insufficiencyBZDD10M74/74144457355:05 PMMarchStrangulation (suicide)1/DeathYesZUC, SSRI, BZDNoneC10M7814407324 AMSeptemberHeart failure, lung embolismD11F80/60130033698 AMDecemberPneumonia≥4/DeathNoLI, HAL, ZUCTCA, BRO, PHT, BZD, CARC11F7811356328 AMNovemberRespiratory insufficiency, lung carcinomaBZDBZD*PMD indicates postmortem; Fix, fixation time; D, depressed subject; M, male; LI, lithium; HAL, haloperidol; PHT, phenothiazine; BZD, benzodiazepine; C, control subject; F, female; SSRI, selective serotonin reuptake inhibitors; BRO, bromperidol; MAP, maprotiline; TCA, tricyclic antidepressants; CLZ, clozapine; MAOI, monoamine oxidase inhibitor; MIA, mianserin; ellipses, not determined; ZUC, zuclopenthixol; and CAR, carbamazepine.†All patients suffered from major depression, except D1, D4, and D11, who had bipolar disorder.‡All last episodes were depressive, except for subjects D4 and D11, whose last episode was a manic episode.§Subject D2 attempted suicide 1 month before death, D6 7 days before death, and D10 died of the attempt.&par;Also used corticosteroids.¶Also used morphine.#Because not enough material was available, these subjects replaced the "a" subjects for in situ hybridization.IMMUNOCYTOCHEMISTRY AND MORPHOMETRYFor immunocytochemical analysis of AVP, 6-µm-thick paraffin sections through the entire SCN were stained with an antibody against AVP. The immunocytochemical and morphometric procedures were performed, as described extensively elsewhere.Briefly, measurements of the vasopressinergic SCN area and the number of cell nuclei were performed unilateraly by means of a digitizer (CalComp Inc, Reading, England). The rostrocaudal axis was determined by staining every 25th section, starting from the lamina terminalis and ending at the caudal end of the optic chiasm. The rostral and caudal borders of the SCN were assessed by staining every 10th section in the area and by determining the sections in which, respectively, the first and the last AVP cells were present. The volume of the SCN was determined by integrating all the area measurements of the SCN sections that contained immunocytochemically stained cells. The numerical cell density of AVP-IR neurons was estimated by counting the total number of nuclear profiles per unit area, followed by a discrete "unfolding" procedure, which included the modification proposed by Cruz-Oriveand a correction for section thickness (6 µm, z-axis). All nuclear profiles within a rectangular grid in one of the oculars that corresponded to 38 000 µm2in the section were measured according to Gundersen.The total number of AVP-IR neurons was computed by multiplying the numerical cell density with the corresponding volume of the AVP subnucleus.ISH AND QUANTITATIVE ANALYSISFor ISH, 3 control subjects (C2, C7, and C9) were replaced by other matched controls (Table 1) because not enough material was left to stain the entire SCN. Hybridization was performed on every 50th section of the SCN. Sections were randomly divided over 2 hybridization assays of approximately 120 sections each. The AVP probe (human vasopressin 3, provided by G. Mengod and J. M. Palacios, Basel, Switzerland) consisted of an oligomer of 48 nucleotides complementary to bases 411 to 458 of the human preprovasopressin precursor.The specificity of the probe has been described previously.The probe was 3′-end labeled using terminal deoxynucleotidyl transferase (Roche, Mannheim, Germany) and [α-35S] dATP (NEN Life Sciences, Boston, Mass) as described earlier.Tissue pretreatments were performed mainly as previously describedexcept for the deproteination and delipidation. Deproteination was done in proteinase-K (10 µg/mL at 37°C) for 15 minutes instead of 30 minutes. Delipidation was performed in 0.1% Triton X-100 (Sigma, St Louis, Mo) in phosphate-buffered saline for 10 minutes and sections were washed in phosphate-buffered saline without dehydration before hybridization. Each section was incubated with 68-µL hybridization solution containing approximately 1 × 106-cpm–labeled probe. After overnight incubation at 42°C, the sections were rinsed in 1×silver sulfadiadiazine chlorhexidine for 30 minutes at 50°C, 2 × 30 minutes 0.1×silver sulfadiadiazine chlorhexidine at 50°C, and 2 × 30 minutes 0.1×silver sulfadiadiazine chlorhexidine at room temperature. Sections were dehydrated at room temperature in 300-mmol ammonium acetate (pH, 5.5)/100% ethanol at volume ratios 1:1, 3:7, 1:9, and 0:1. To check the autoradiographic signal, a β-max hyperfilm (Nycomed Amersham plc, Buckingham, England) was apposed and developed after 5 days. Subsequently, slides were dipped in photographic emulsion (NTB2; Eastman Kodak, Rochester, NY) at 42°C, dried on a cool glass plate, and stored in a light-tight box at 4°C. After 17 days, slides were developed for 2 minutes in Dektol Developer (Sigma, St Louis, Mo) at 15°C and fixed in Kodak fixer (Sigma) at 15°C for 10 minutes. Sections were washed to remove the fixative and counterstained with thionine.For quantitative analysis of the ISH signal of the AVP-mRNA in the SCN, the Interaktive Bild Analyse System image analysis system (Kontron, Munich, Germany) was connected to a Bosch TYK9B television camera (Bosch, Stuttgart, Germany) equipped with a chalnicon tube mounted on a Zeiss microscope (Zeiss, Munich). The microscope was equipped with planapo objectives, a blue filter, and a scanning stage. The main principle and procedure of the Interaktive Bild Analyses System measurement have been extensively described before.Briefly, the area of the SCN was manually outlined at low magnification (×4 objective) and a grid of fields was superimposed. From this grid, 50% of the fields indicated in red rectangles were randomly selected and stored (Figure 1A). Then, at high magnification (×40 objective), each field was retrieved on the image analysis monitor (Figure 1B). A mask was superimposed over the silver grains in these images. After the blue filter was removed, the profiles identified as cells by means of thionin staining were manually outlined. Finally, the total number of profiles expressing AVP-mRNA in the SCN and total mask area of the silver grains in the profiles were calculated as an estimate of total amount of AVP-mRNA in the SCN. In addition, total mask of the silver grains was divided by the total number of profiles to estimate the mean amount of AVP-mRNA per profile. This gives an estimate of the average AVP-RNA production per neuron.Figure 1.A, Outline of the suprachiasmatic nucleus at low magnification (×2.5 objective) at the Interaktive Bild Analyses System monitor with superimposed grid and selected fields. B, Outline of positive profiles at high magnification (×40 objective) at the Interaktive Bild Analyses System monitor with superimposed mask over the silver grains and red inclusion line.Neither for the assessment of the number of AVP-IR neurons nor for the quantification of the AVP-mRNA were the raters blind to the antemortem diagnosis, but the measurements were standardized in such a way that this could not have influenced the study outcome.STATISTICAL ANALYSISDifferences among the groups were statistically evaluated by the Wilcoxon signed-rank test (2-tailed). Values of P<.05 were considered to be significant. All values are expressed as mean ± SD. Differences within the depressed group according to their medication in the last month were tested with the nonparametric Mann-Whitney Utest.Linear regression analysis was performed to study the effects of postmortem delay (PMD) and the duration of the disease on the AVP data set, using the Spearman correlation coefficient.RESULTSThe groups were matched for sex, age, and clock time at death. Both groups consisted of 4 female and 7 male subjects. Data on age (67 ± 8.7 years, for depressed subjects; 70 ± 12 years, for control subjects); brain weight (1280 ± 162.3 g, for depressed subjects; 1399 ± 307.5 g, for control subjects) (P= .96); clock time at death; PMD; and fixation time (38 ± 12.5 hours, for depressed subjects; 34.4 ± 15.5 hours, for control subjects) (P= .48) are presented in Table 1. There were no differences in these factors between the control and depression group except for the PMD. The control group had a shorter mean ± SD average PMD (9.5 ± 4.7 hours) than the depression group (32.5 ± 20.4 hours) (Z= −2.67, P= .008), but no significant relationship was found between PMD and the number of AVP-IR neurons (rs= −0.20, P= .56, for the depressed subjects; rs= 0.34, P= .31, for the controls) or amount of AVP-mRNA (r= 0.15, P= .66, for the depressed subjects; rs= 0.20, P= .55, for the controls).The mean ± SD of AVP-IR neurons in depression (6589 ± 2389) was found to be significantly higher than in controls (3706 ± 1678) (Z= −2.40, P= .02) (Figure 2). A clearly smaller amount of AVP-mRNA was found in the SCN of the subjects with depression (Figure 2). In depressed subjects, the total mask area of silver grains, as an estimate of total amount of AVP-mRNA in the SCN, was approximately half that of control subjects (5921 ± 3802 µm2vs 12 206 ± 5827 µm2) (Z= −2.49; P= .01). Furthermore, the mean ± SD area of masked silver grains per profile was significantly lower in depressed subjects (0.33 ± 0.11 µm2) compared with control subjects (0.52 ± 0.15 µm2)(Z= −2.85, P= .004). Although there was a tendency toward a lower mean ± SD number of profiles that expressed AVP-mRNA in the SCN in the depressed subjects (16 072 ± 8036) than in the controls (23 372 ± 8202), this difference did not reach significance (Z= −1.87, P= .06).Figure 2.The number of arginine vasopressin-immunoreactive (AVP-IR) neurons (A) and the mask area of silver grains of the AVP-messenger RNA (B) in the suprachiasmatic nucleus (SCN) in control subjects (n = 11) and depressed subjects (n = 11). The error bars indicate the SD. Note the change in the balance between the presence of more AVP and less AVP-messenger RNA in depression.There was no difference either in the number of AVP-IR neurons (Z= −0.57, P= .65) or in the amount of AVP-mRNA (Z= −0.95, P= .41) between 4 subjects who had taken lithium in the past (D1, D4, D7, and D11; D1 and D7 took lithium in the last month before death), and the other depressed subjects. In addition, we did not find any difference in the number of AVP-IR neurons (Z= −0.38, P= .79) or AVP-mRNA (Z= −0.57, P= .65) between the subjects who took benzodiazepines (D4, D5, D7, D8, D9, D10, and D11) during the last month before death and the other subjects. The number of AVP-IR neurons and AVP-mRNA in 3 subjects who were treated with selective serotonin reuptake inhibitors during the last month before death (D2 with fluoxetine, D5 with fluvoxamine, and D10 with paroxetine) did not differ from the other subjects (Z= −0.612, P= .63, and Z= −0.41, P= .78, respectively).There was no relationship between either the number of AVP-IR neurons and the duration of the disease (from <1 year to 51 years) (rs= 0.04, P= .89), or for the amount of AVP-mRNA and the duration of the disease (rs= 0.009, P= .98).The differences in the number of AVP-IR neurons and the amount of AVP-mRNA between MD subjects and matched controls did not change in significance when the BD subjects were left out of the analysis (Z= −2.52, P= .01, and Z= −2.10, P= .04, respectively).COMMENTIn the present study, we found that the number of AVP-IR neurons in the SCN was higher in depressed subjects than in control subjects. At the same time, the expression of AVP-mRNA in the SCN was lower in depressed subjects compared with control subjects. The difference in AVP-mRNA is at least partly caused by a decrease in the mean AVP production per neuron. These findings indicate a change in the balance between the production and transport of AVP in depression. A functional alteration of neurons in the SCN is in line with circadian rhythm disturbances that have been found in depression (ie, in sleep-wakefulness, body temperature, hormonal rhythms, and the periodicity of manic-depressive cycles in some BP patients).As mentioned in the introduction, we hypothesized that the number of AVP-IR neurons in the SCN would be decreased. This would be in line with an attenuated inhibition of AVP from the SCN on CRH neurons in the PVN,which could explain the increased number of CRH neurons together with increased CRH-mRNA levels in the PVN.Finally, this would lead to the frequently found increased levels of cortisol in depression. It was thus a surprise to find just the opposite, namely an increased number of AVP-IR neurons in the SCN in depression. We then wanted to know whether this increase was also reflected by the production of AVP in these neurons and performed an ISH for AVP-mRNA. The results of this experiment brought us back to our hypothesis, because we found a clearly decreased amount of AVP-mRNA in depression. Probably there is accumulation of AVP in the neurons of the SCN in depression caused by a decreased transport rate of the neuropeptide. Arginine vasopressin is normally transported from the SCN to its target areas by axonal transport. So far, there is not much known about changes in transport rate related to psychiatric diseases, but in Alzheimer disease, a decreased axonal transport rate of the neurotrophin/track complex caused by cytoskeletal changes may be the underlying event for the neuronal atrophy in the nucleus basalis of Meynert.The possibility of a decreased axonal transport rate in depression certainly needs further investigation.It should be mentioned that the number of cell profiles that expressed AVP-mRNA was higher than the total number of AVP peptide-expressing neurons (controls in this study). This is because, in the ISH study, profiles of cells were counted instead of the number of cells as estimated in the immunocytochemical study. For a comprehensive discussion on the use of the deconvolution or unfolding technique, we refer to a previous study at our institute by Raadsheer et al.In this article, a comparison is made between the use of the unfolding method and the dissector and a high correlation was found between both methods (rs= 0.98).Information on the exact influence of antidepressants on the SCN, and, more specifically, AVP in the SCN, is limited. Lithium acts on hamster SCN neuronal firing in vitro, although it is not known on what type of neurons.It has also been shown that the diurnal rhythm of AVP-mRNA in the rat SCN did not seem to be affected by benzodiazepines.Depletion of serotonin in the SCN has been shown to disrupt phase and period characteristics of the daily locomotor rhythm in rats and hamsters.However, the diurnal rhythm of AVP-mRNA of the rat SCN in tissue culture was not disrupted after the administration of the serotonin-depleting agent parachlorophenylalanine, a tryptophan hydroxylase inhibitor.All these observations argue against treatment effects and support the idea that the alteration of AVP neurons in the SCN might well be related to the trait of depression per se. However, our sample was too small to draw any firm conclusions on the effect of treatment on the outcome measures.With respect to a possible confounding effect of alcohol on AVP neurons in the SCN in humans, nothing is known. One study by Harding et aldescribed that the use of high doses of alcohol is correlated with neuronal degeneration of magnocellular vasopressin neurons in the PVN and SCN. They did, however, not describe an effect on the parvicellular vasopressin neurons in the SCN. In the rat SCN, Madeira et alstudied the effect of ethanol treatment and withdrawal on AVP-immunoreactivity and mRNA levels in the rat SCN. They found a reduction in the number of AVP neurons in the SCN in both the ethanol-treated and withdrawn rats. Also, the hybridization signal for AVP-mRNA was reduced in both the ethanol-treated and withdrawn rats, with even a weaker signal in the withdrawn rats. This makes it clear that not only the use of alcohol at the time of death should be taken into consideration, but also a possible irreversible effect after alcohol withdrawal during lifetime that could still confound the immunocytochemistry and ISH findings. However, none of the subjects used alcohol at the time of death, as reported by the medical scores. Only 2 subjects, D3 and C3, have a history of alcohol abuse, but these subjects were matched with each other and did not influence our conclusions.Since the SCN is the clock of the brain, the time of death should also be considered as a possible confounding factor. We excluded this possibility by matching depressed subjects as much as possible with control subjects who had died around the same time (Table 1). Moreover, a higher number of AVP-IR neurons and a lower amount of AVP-mRNA were found in depressed subjects over the entire period of the day and night (Figure 3).Figure 3.A, Number of vasopressin-immunoreactive (AVP-IR) neurons plotted against clock time at death of each individual (11 depressed subjects, and 11 control subjects). B, Area of masked silver grain plotted against clock time of death of each individual. This figure illustrates that the difference between depressed and control subjects is present at different points of the day and that there is no overlap between the 2 groups when you take the clock time at death into account.The functional alterations of AVP neurons in the SCN of depressed subjects are of special interest in relation to the impaired regulation of the hypothalamic-pituitary-adrenal system in depression.Animal data show that AVP neurons of the SCN exert an inhibitory influence on CRH in the PVN and thereby reduce the stress-induced release of glucocorticoids.Increased levels of circulating glucocorticoids increase AVP-mRNA in the SCN within a narrow time window,which will strengthen the inhibition of CRH in the PVN. How exactly the SCN and the hypothalamic-pituitary-adrenal axis are linked to the pathobiology of depression needs further investigation (ie, the feedback mechanism of glucorticoids on the hypothalamic-pituitary-adrenal axis and on how the SCN is involved in this feedback).Since this study was performed on postmortem human brain material, antemortem and postmortem factors, such as agonal state, medication, PMD, duration of fixation, and storage time of the tissue, may contribute to the variation observed in mRNA levels.Information on the exact influence of each of these factors on AVP-mRNA levels, however, is still very limited. As far as PMD is concerned, a significant decrease in the amount of AVP-mRNA, with increasing PMD, was indeed shown in postmortem rat brain.Relatively few ISH studies on postmortem effects on human brain material have been performed. Using ISH, several human mRNAs have been localized after a PMD of up to 40 hours.In addition, no significant correlation was found between the density of the hybridization signal and PMD (range, 2.5-66 hours) in a comparison of propiomelanocortin mRNA levels in pituitary glands between controls and different diseased patients.Lucassen et alreported that after 6 hours, no further decrease in signal was detected in the AVP-mRNA in the human supraoptic nucleus and paraventricular nucleus of the hypothalamus. In our material, the PMD was 6 hours or longer (Table 1). We did not find a significant correlation between the number of AVP-IR neurons and PMD or between the amount of AVP-mRNA and PMD in the present study in either the control or in the depressed group, so that there is no indication that PMD might have influenced our conclusions.CONCLUSIONSWe found an alteration of the AVP neurons in the SCN in depressed subjects, both at the level of AVP-peptide and AVP-gene expression. The results suggest that the functional ability of the SCN to maintain normal biological rhythms is diminished in subjects suffering from depression, which seems to be the result of changes in the balance between production and transport of AVP in the SCN.JHMeijerWJRietveldNeurophysiology of the suprachiasmatic circadian pacemaker in rodents.Physiol Rev.1989;69:671-707.DFSwaabEJVan SomerenJNZhouMAHofmanBiological rhythms in the human life cycle and their relationship to functional changes in the suprachiasmatic nucleus.Prog Brain Res.1996;111:349-368.AWirz-JusticeBiological rhythms in mood disorders.In: Bloom FE, Kupfer DJ, eds. Psychopharmacology: The Fourth Generation of Progess. 4th ed. 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I. de Vos, MD, PhD), for the brain material; Wouter Kamphorst, MD, PhD, Frans C. Stam, MD, PhD, Richard A. I. de Vos, and Dick Troost, MD, PhD, for performing the neuropathology; Ronald W. H. Verwer, PhD, Bart Fisser, PhD, Lucien te Bulte, Frank P. M. Kruijver, MD, Henk Stoffels, Gerber v. d. Meulen, Roy J. E. M. Raymann, Tini Eikelboom, and Wilma T. P. Verweij for their technical help; and Ruud M. Buijs, PhD, Andries Kalsbeek, PhD, Paul J. Lucassen, PhD, and Fred J. H. Tilders, PhD, for their critical comments.Jiang-Ning Zhou and Rixt F. Riemersma contributed equally to this study.Corresponding author: Dick F. Swaab, MD, PhD, Netherlands Institute for Brain Research, Meibergdreef 33, 1105 AZ Amsterdam, the Netherlands (e-mail: [email protected]).

van Os, Jim; Hanssen, Manon; Bijl, Rob V.; Vollebergh, Wilma

doi: 10.1001/archpsyc.58.7.663pmid: 11448373

BackgroundUrban and rural populations have different rates of psychotic illness. If psychosis exists as a continuous phenotype in nature, urban-rural population differences in the rate of psychotic disorder should be accompanied by similar differences in the rate of abnormal mental states characterized by psychotic or psychosislike symptoms.MethodsA random sample of 7076 individuals aged 18 to 64 years were interviewed by trained lay interviewers with the Composite International Diagnostic Interview. Approximately half of those with evidence of psychosis according to the Composite International Diagnostic Interview were additionally interviewed by clinicians. We investigated associations between a 5-level urbanicity rating and (1) any DSM-III-Rdiagnosis of psychotic disorder (sample prevalence, 1.5%), (2) any rating of hallucinations and/or delusions (sample prevalence, 4.2%), and (3) any rating of psychotic or psychosislike symptoms (sample prevalence, 17.5%).ResultsLevel of urbanicity was associated not only with DSM-III-Rpsychotic disorder (adjusted odds ratio [OR] over 5 levels, 1.47; 95% confidence interval [CI], 1.25-1.72), but also, independently, with any rating of delusion and/or hallucination (adjusted OR, 1.28; 95% CI, 1.17-1.40; clinician-assessed psychotic symptoms only: OR, 1.30; 95% CI, 1.03-1.64) and any rating of psychosislike symptom (adjusted OR, 1.18; 95% CI, 1.13-1.24). Psychotic symptoms were strongly and independently associated with psychotic disorder, regardless of the level of urbanization.ConclusionsCommunity level of psychotic and psychosislike symptoms may be inextricably linked to the prevalence of psychotic disorder. The prevalence of abnormal mental states that facilitate development to overt psychotic illness increases progressively with level of urbanization.SOME RISK FACTORS for psychotic illness can be used to define populations with different levels of risk. For example, exposure to urban birth, upbringing, or residence increases the risk for later psychotic illness, suggesting that urban and rural populations have different lifetime risks.A plausible explanation for the urban-rural differences is that environmental factors associated with urban life make individuals more vulnerable to the development of psychotic states.The factors that increase the risk for psychotic disorder in urban areas may contribute to a much larger pool of preclinical psychotic experiences, of which only a small proportion may continue to result in overt disorder. This hypothesis would be compatible with the suspected multifactorial origin of psychotic disorders, according to which it is unlikely that any multifactorial disease exists as a purely dichotomous entity in nature without less severe, nonpathologic manifestations of the phenotype.It is also compatible with accumulating evidence that schizotypal signs and psychosislike symptoms such as delusional ideation and isolated hallucinations are prevalent in the general populationand show longitudinal,neuropsychological,psychopathologic,familial,neuroradiologic,epidemiologic,and risk factorcontinuities with clinical psychotic syndromes such as schizophrenia. All of these data suggest that, at the level of the general population, lesser psychotic states exist that are associated with the more severe clinical disorders that necessitate hospital admission.Given these continuities, it is attractive to hypothesize that the higher level of psychotic disorders in urban areas is accompanied by a similar increase in the level of psychosislike symptoms. Population studies of minor psychiatric disorders have shown that the prevalence of disorder is linearly related to the mean number of psychiatric symptoms.A similar relationship between symptoms and disorder may exist in psychosis.In the current study, we investigated to what degree the increase in risk for psychotic disorder associated with urban life is reflected in similar increases in the mean number of psychotic and psychosislike symptoms. We hypothesized that the mean level of symptoms would increase with the rate of disorder across increasingly urbanized areas. In addition, we hypothesized that the association between symptoms and disorder would be constant across the populations in the different strata, suggesting variation of susceptibility between populations rather than within populations.For example, if the rate of some rare psychotic disorder is higher in population A than in population B, whereas their levels of more prevalent psychotic symptoms are the same, a likely explanation is that (1) some rare cause of a rare disorder is more prevalent in population A and (2) psychosislike experiences in the population are qualitatively distinct from the disorder. Thus, populations A and B are essentially similar, except for the distribution of some rare cause affecting a few individuals. There is variation within populations. If, however, not only the rate of disorder, but also the rate of symptoms, is higher in population A than in population B, a likely explanation would be that (1) the population level of vulnerability differs between population A and population B and (2) the psychosislike experiences are, at least in part, on a quantitative continuum with disorder. A graphic representation of this argument, using hypothetical data, is depicted in Figure 1.A, In this scenario, the prevalence of psychotic disorder increases in populations living in progressively more urbanized areas, but the prevalence of symptoms remains constant. This suggests that there is no continuity between symptoms and disorder. B, In this scenario, the prevalence of symptoms increases simultaneously with the prevalence of symptoms. If it can be shown additionally that symptoms are associated with disorder in each population, the situation in this graph suggests that symptoms are continuous with the disorder.SUBJECTS AND METHODSSUBJECTSThe Netherlands Mental Health Survey and Incidence Study is a prospective study with 3 measurement points during a period of 3 years.The current report is based on the baseline data. A multistage, stratified, random sampling procedure was used to select first 90 municipalities, then a sample of private households, and finally a Dutch-speaking individual aged 18 to 64 years within each household. Selected households were sent an introductory letter by the Minister of Health, inviting them to participate. A total of 7076 individuals provided informed consent and were interviewed at baseline, representing a response rate of 69.7%. Nearly 44% of nonresponders agreed to complete a mailed questionnaire, including a General Health Questionnaire,and were found to have the same mean score on the questionnaire (responders, 1.19; nonresponders, 1.16). Nonresponse was not associated with level of urbanicity.The sample was representative of the Dutch population in terms of sex, marital status, and level of urbanization,with the exception of a slight underrepresentation of individuals in the age group 18 to 24 years. As this was a study of relative rather than absolute risk, no poststratification weightings were applied to the data.INSTRUMENTSSubjects were interviewed at home. The Composite International Diagnostic Interview (CIDI) version 1.1was used, yielding DSM-III-Rdiagnoses. The CIDI was designed for trained interviewers who are not clinicians and has high interrater reliabilityand high test-retest reliability.Ninety interviewers experienced in systematic data collection gathered the data, having received a 3-day training course in recruiting and interviewing, followed by a 4-day course at the World Health Organization–CIDI training center in Amsterdam, the Netherlands. Extensive monitoring and quality checks took place throughout the entire data collection period.PSYCHOSIS RATINGSLifetime ratings from the 17 CIDI core psychosis sections on delusions (13 items) and hallucinations (4 items) were used (items G1-G13, G15, G16, G20, and G21). These concern classic psychotic symptoms involving, for example, persecution, thought interference, auditory hallucinations, and passivity phenomena. All of these items can be rated in 6 ways: 1, no symptom; 2, symptom present but not clinically relevant (not bothered by it and not seeking help for it); 3, symptom the result of ingestion of drugs; 4, symptom the result of somatic disease; 5, true psychiatric symptom; and 6, may not really be a symptom because there appears to be some plausible explanation for it. Because psychotic symptoms are difficult to diagnose in a structured interview,clinical reinterviews were conducted over the telephone by an experienced trainee psychiatrist for all individuals who had at least 1 rating of 5 or 6, using questions from the Structured Clinical Interview for DSM-III-R, an instrument with proved reliability and validity in diagnosing schizophrenia.The CIDI ratings were corrected on the basis of these clinical interviews, and the Netherlands Mental Health Survey and Incidence Study DSM-III-Rdiagnoses of psychotic disorder are based on the data from these clinical reinterviews.To examine the validity of the contrasts implied in the different CIDI ratings, associations were compared between the ratings and lifetime mental health service use (see below) and quality of life measured with the 36-item Short Form Health Survey quality-of-life schedule.Psychotic disorderwas defined as any DSM-III-Raffective or nonaffective psychotic diagnosis. Psychotic symptomwas defined as any CIDI rating of 2, 3, 4, 5, or 6 on any of the 17 CIDI core psychosis items. A previous study showed that all 5 of these ratings on the CIDI psychosis items were strongly associated with each other, including the clinical reinterview ratings of psychotic symptom (ie, a rating of 5 on any of the CIDI psychosis items). In addition, the 5 ratings independently showed a similar pattern of associations with known risk factors for psychosis.As they therefore appear to reflect the same underlying latent dimension of "psychosis," they were joined into a single broad rating of psychotic symptomfor the purpose of the current study. To check on the validity of this procedure, associations were also examined with "psychotic symptom" narrowly defined as a clinical reinterview rating of psychosis (ie, a rating of 5 on any of the CIDI psychosis items).LEVEL OF URBANICITYFive levels of urbanization were defined, following the standard classification of urbanization of place of residence according to the Dutch Central Bureau of Statistics. These are based on the density of addresses per square kilometer in an area and are classified as less than 500, 500 to 999, 1000 to 1499, 1500 to 2499, and 2500 or more. This density is calculated by assessing the density of addresses in a circle of 1 km around each address. The density of addresses in an area is then calculated as the mean address density of all the addresses in that area.DATA ANALYSESThe lifetime prevalences of at least 1 psychotic symptom, broadly and narrowly defined, and of any psychotic disorder were examined in relation to level of urbanicity of place of residence, adjusted for the a priori selected possible confounding effects of age in years, sex, level of education (4 levels), and country of birth of subject, subject's mother, and subject's father (coded Dutch-born, foreign-born, and information missing).To assess the independence of any associations with urbanicity in the different symptom groups, associations were also examined after exclusion of individuals with psychotic disorder from the group with narrowly defined psychotic symptoms, and after exclusion of individuals with psychotic disorder and individuals with narrowly defined psychotic symptoms from the group with broadly defined psychotic symptoms.To examine the possible effects of selective "drift" of mental health patients toward urban areas, we also looked for interactions with lifetime history of mental health care (any contact with community mental health center, psychiatric outpatient clinic, private psychiatrist, psychologist, or psychotherapist, or any psychiatric admission or day treatment; n = 1352). Logistic regression yielding odds ratios (ORs) and 95% confidence intervals (CIs)was used to examine associations between psychotic disorder on the one hand, and psychotic symptoms broadly and narrowly defined (coded 0, 1, 2, 3, 4, and 5 or more symptoms) on the other, at different levels of urbanicity. Interactions were assessed by likelihood ratio (LR) tests.SENSITIVITY ANALYSESOf the 479 individuals who were eligible for a clinical reinterview over the telephone, 226 (47.2%) were actually interviewed. Successful reinterview was not associated with level of urbanicity (OR, 0.91; 95% CI, 0.79-1.04). Of a possible 226 × 17 = 3842 CIDI ratings of psychotic symptoms in the 226 individuals who were reinterviewed, changes after clinical reinterview were introduced in 266 ratings (6.9%). Change of CIDI rating was not associated with level of urbanicity (OR, 0.95; 95% CI, 0.86-1.04). Sensitivity analyses representing the possible extremes of clinical reinterview were performed to examine whether differential clinical reinterview rates could have biased the results.RESULTSThe sample consisted of 7076 individuals (46.6% male) with a mean age of 41.2 years (SD, 12.2). There were 936 individuals (13.2%) who were foreign-born or whose father or mother was foreign-born. The prevalences of the different CIDI ratings on the 17 psychosis items were as follows: any CIDI rating of 2, n = 915 (12.9%); any CIDI rating of 3 or 4, n = 39 (0.6%); any CIDI rating of 5, n = 295 (4.2%); and any CIDI rating of 6, n = 285 (4.0%). After adjustment for age, sex, ethnic group, educational level, level of urbanicity, and presence of any psychotic or nonpsychotic DSM-III-Rdiagnosis, all but one of the different CIDI ratings were independently (assessed by entering them together in the model) associated with a lifetime history of mental health service (rating 2: OR, 0.99; 95% CI, 0.82-1.19; rating 3 or 4: OR, 2.65; 95% CI, 1.30-5.44; rating 5: OR, 4.19; 95% CI, 3.23-5.44; rating 6: OR, 3.12; 95% CI, 2.39-4.06), and all were associated with a lower quality-of-life total score measured with the quality-of-life schedule of the 36-item Short Form Health Survey (rating 2: multiple regression coefficient B = −1.99; 95% CI, −3.24 to −0.75; rating 3 or 4: B = −6.78; 95% CI, −12.25 to −1.13; rating 5: B = −6.79; 95% CI, −8.94 to −4.63; rating 6: B = −3.09; 95% CI, −5.25 to −0.94).The lifetime prevalence of DSM-III-Rschizophrenia, schizoaffective psychosis, and schizophreniform disorder was 0.37% (26 cases), and the lifetime prevalence of affective psychosis (major depression or bipolar disorder with psychotic features) was 1.14% (81 cases), making a total of 107 cases (1.51%). The prevalence of psychotic symptoms broadly defined was 17.5% (n = 1237), and the prevalence of psychotic symptoms narrowly defined was 4.2% (n = 295).PSYCHOSIS IN RELATION TO URBANICITYThe lifetime prevalences of DSM-III-Rpsychotic disorder, psychotic symptoms narrowly defined, and psychotic symptoms broadly defined increased in a monotonic fashion with level of urbanicity. Adjustment for age, sex, level of education, and country of birth of subject and parents changed the parameters only by a small amount (Table 1). Associations also remained after exclusion of individuals with psychotic disorder from the group with narrowly defined psychotic symptoms, and after exclusion of individuals with psychotic disorder and individuals with narrowly defined psychotic symptoms from the group with broadly defined psychotic symptoms (Table 1). There was no interaction with lifetime mental health treatment for any of the 3 groups (LR test psychotic disorder: χ21= 0.05, P= .83; LR test narrowly defined: χ21= 0.21, P= .64; LR test broadly defined: χ21= 0.00, P= .98).Table 1. Sample Prevalences of Psychotic Disorder and Narrowly and Broadly Defined Psychotic Symptoms in Relation to Urbanicity*Area Address Density/km2No. InterviewedAny Psychotic DisorderPsychotic SymptomsNarrow Definition†Broad Definition†No. (%)OR (95% CI)No. (%)OR (95% CI)No. (%)OR (95% CI)<50011857 (0.59)1‡28 (2.36)1‡163 (13.76)1‡500-999161015 (0.93)1.58 (0.64-3.89)45 (2.80)1.19 (0.74-1.92)223 (13.85)1.01 (0.81-1.25)1000-1499154123 (1.49)2.55 (1.09-5.96)69 (4.48)1.94 (1.24-3.03)262 (17.00)1.28 (1.04-1.59)1500-2499149728 (1.87)3.21 (1.40-7.37)82 (5.48)2.40 (1.55-3.70)303 (20.24)1.59 (1.29-1.96)≥2500124234 (2.74)4.74 (2.09-10.73)71 (5.72)2.51 (1.61-3.91)286 (23.03)1.88 (1.52-2.32)OR linear trend§1.44 (1.24-1.68), P<.0011.28 (1.17-1.40), P<.0011.19 (1.14-1.25), P<.001Adjusted OR linear trend&par;1.47 (1.25-1.72), P<.0011.28 (1.17-1.40), P<.0011.18 (1.13-1.24), P<.001Adjusted OR with nonoverlapping outcomes¶NA1.19 (1.06-1.32), P= .0021.16 (1.10-1.22), P<.001*OR indicates odds ratio; CI, confidence interval; and NA, not applicable.†For explanation see the "Psychosis Ratings" subsection of the "Subjects and Methods" section.‡Reference category.§The summary increase in risk with 1-unit change in address density.&par;For explanation see the "Data Analyses" subsection of the "Subjects and Methods" section.¶Adjusted as above, and excluding individuals with any psychotic disorder from the analysis with narrow psychotic symptoms, and excluding individuals with any psychotic disorder and narrow psychotic symptoms from the analysis with broad psychotic symptoms.SENSITIVITY ANALYSESThe following analyses were conducted. First, we repeated the analyses of association between urbanicity and the 3 groups of symptom ratings, adjusted for age, sex, level of education, and country of birth of subject and parents, excluding the 253 individuals who were eligible for clinical reinterview but who were not interviewed. Second, we repeated the same analyses restricted to the group who were interviewed by the psychiatric trainees (n = 226). Third, we repeated the analyses assuming that all individuals who were eligible for reinterview, but were not reinterviewed, would have received a rating of 5 (true symptom) on all CIDI psychosis items if they had been reinterviewed. Finally, we repeated the same analyses assuming that these individuals would have received a rating of 1 (no symptom) on all ratings. The pattern of results for all these analyses was the same and was similar to the results in Table 1(Table 2).Table 2. Sensitivity AnalysisSensitivity Analysis*Adjusted* Odds Ratio Linear Trend (95% Confidence Interval)†Psychotic DisorderPsychotic Symptom, Narrow DefinitionPsychotic Symptom, Broad DefinitionExclusion of individuals missed for reinterview1.54 (1.26-1.89)1.32 (1.16-1.51)1.19 (1.13-1.26)Restriction to individuals who were interviewed by psychiatrist1.34 (1.04-1.74)1.30 (1.03-1.64)1.15 (0.88-1.52)Assuming all CIDI psychosis items of individuals missed at reinterview would have been rated "5"1.35 (1.19-1.53)1.21 (1.12-1.31)1.19 (1.14-1.25)Assuming all CIDI psychosis items of individuals missed at reinterview would have been rated "1"1.54 (1.26-1.88)1.32 (1.15-1.50)1.18 (1.12-1.25)*See the "Sensitivity Analyses" subsection of the "Subjects and Methods" section for explanation. CIDI indicates Composite International Diagnostic Interview.†The summary increase in risk with 1-unit change in address density.ASSOCIATIONS BETWEEN SYMPTOMS AND DISORDER AT DIFFERENT LEVELS OF URBANICITYPsychotic symptoms, broadly and narrowly defined, were strongly associated with psychotic disorder (broadly defined: summary OR over 6 levels, 3.59; 95% CI, 3.17-4.06; narrowly defined: OR, 6.96; 95% CI, 5.57-8.68). The association between psychotic disorder and broadly defined psychotic symptoms remained after adjustment for presence of narrowly defined psychotic symptoms (adjusted OR, 2.29; 95% CI, 1.93-2.73), indicating that broadly defined symptoms were associated with psychotic disorder independent of their association with narrow symptoms. There was no evidence that this association differed as a function of urbanicity (LR test broadly defined: χ24= 4.20, P= .38; LR test narrowly defined: χ24= 4.51, P= .34), or if the sample was restricted to those who had had clinical reinterviews (n = 226; P= .54 and P= .95, respectively).COMMENTIn a representative sample of 7076 subjects sampled from the general population, lifetime level of psychotic and psychosislike symptoms independently increased with level of urbanicity in the same manner as did DSM-III-Rpsychotic disorder. Initial response rate, clinical reinterview response rate, and change rate of CIDI psychosis items after clinical reinterview were not associated with level of urbanicity. At all levels of urbanicity, psychosislike symptoms were strongly associated with psychotic disorder. These findings therefore suggest that the increased prevalence of psychotic disorder in urban environments should be interpreted in light of increased levels of "psychosis proneness" in urban populations.More generally, the results suggest that there is a link between community level of psychotic symptoms and rates of clinical disorder. As the association between symptoms and disorder did not differ as a function of urbanicity, the implication is that susceptibility to psychotic disorder varies between populations and can be demonstrated by comparing rates of psychosislike phenomena. Given that the rates of these phenomena are much higher than those for rare psychotic disorders, etiologic research may be served by focusing on these related phenotypes. Similarly, preventive action may be served by population interventions rather than, or in addition to, the high-risk strategies that are currently being explored.The high rates of psychotic illness in urban environments may be the result of the influence of environmental factors. As the urban effect appears to have its impact during urban upbringing rather than during adult residence per se,developmental mechanisms ought to be considered. A possible developmental mechanism whereby social factors may create enduring liabilities for adult psychosis are the effects of the wider social environment, such as the neighborhood environment, on child and adolescent development.Mental states are reactive to experience, and differences in the level of deprivation and social isolation of the neighborhood environment in urban areas have been shown to be associated with variation in a range of mental health outcomes from problem behavior in childrento incidence of neurosis and schizophrenia.High levels of deprivation and low levels of social capitalin the wider social environment may enhance the development of "at-risk" mental states that in turn may facilitate the onset of clinical psychosis in adult life.These results should be viewed in light of several possible limitations. First, we examined lifetime rates of disorder and symptoms in relation to current urban residence. Thus, one explanation for the findings is that symptomatic individuals could have "drifted" to urban areas. Although we cannot exclude this mechanism, a previous report by our group found that there was a high degree of lifetime stability of urban exposure status (around 75% of individuals living in urbanized areas had also been born there), indicating that current exposure is likely to reflect stable lifetime exposure in most cases.In addition, the association between psychosis and urbanicity did not differ as a function of lifetime mental health patient status. This suggests that it is unlikely that the findings can be explained solely by a process of urban drift of the most symptomatic individuals, in which case one would have expected associations to be stronger in the patient group. Second, the validity of the CIDI ratings of psychosis, such as a rating of 2 (symptom present but not clinically relevant) is not well researched. However, the associations with lifetime mental health service contact (no association with rating of 2 and significant associations with other ratings, especially rating of 5 for true psychotic symptom) and lower quality of life (weakest for rating of 2 and strongest for rating of 5), independent of each other and of any lifetime DSM-III-Rdiagnosis, provided some degree of validity for the conceptual contrast of the ratings as well as for their existence independent of psychiatric disorder. Third, psychotic symptom ratings were assessed by lay interviewers (CIDI ratings of 1, no symptom; 2, symptom present but not clinically relevant; 3, symptom the result of ingestion of drugs; and 4, symptom the result of somatic disease), whereas the other CIDI ratings (5, true psychiatric symptom; and 6, may not really be a symptom because there appears to be some plausible explanation for it) were assessed by clinicians through telephone interviews in approximately 50% of eligible cases. Although the sensitivity analyses showed that incomplete rates of reinterview by clinicians are unlikely to have biased the results, it is likely that, especially with lay interviewer ratings,a degree of misclassification did occur. However, for misclassification to explain the results, one would have to hypothesize that with greater degrees of urbanization, interviewers became progressively more likely to misclassify in one specific direction, and, furthermore, this influence of misclassification would have had to be the same for lay interviewers and clinicians. 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Amsterdam, the Netherlands: Elsevier Science Publishers; 1990:138-143.HUWittchenReliability and validity studies of the WHO–Composite International Diagnostic Interview (CIDI): a critical review.J Psychiatr Res.1994;28:57-84.JEHelzerLNRobinsLTMcEvoyELSpitznagelRKStoltzmanAFarmerIFBrockingtonA comparison of clinical and diagnostic interview schedule diagnoses: physician reexamination of lay-interviewed cases in the general population.Arch Gen Psychiatry.1985;42:657-666.JCAnthonyMFolsteinAJRomanoskiMRVon KorffGRNestadtRChahalAMerchantCHBrownSShapiroMKramerEMGruenbergComparison of the lay diagnostic interview schedule and a standardized psychiatric diagnosis: experience in Eastern Baltimore.Arch Gen Psychiatry.1985;42:667-675.SACooperRACollacottRelapse of depression in people with Down's syndrome.Br J Dev Disabil.1994;40:32-37.RLSpitzerJBWilliamsMGibbonMBFirstThe Structured Clinical Interview for DSM-III-R(SCID), I: history, rationale, and description.Arch Gen Psychiatry.1992;49:624-629.JEWareBGandekIPGroupThe SF-36 Health Survey: development and use in mental health research and the IQOLA project.Int J Ment Health.1994;23:49-73.KVan der ZeeRSandermanJHeyinkDe Psychometrische kwaliteiten van de MOS 36-item Short Form Health Survey (SF-36) in een Nederlandse populatie.Tijdschr Soc Gezondheidzorg.1993;71:183-191.Central Bureau of StatisticsBevolking der Gemeenten van Nederland.The Hague, the Netherlands: CBS Publications; 1993.RWPlattLogistic regression and odds ratios.Inj Prev.1997;3:294.KLRadackGRouanJHedgesThe likelihood ratio: an improved measure for reporting and evaluating diagnostic test results.Arch Pathol Lab Med.1986;110:689-693.TLeventhalJBrooks-GunnThe neighborhoods they live in: the effects of neighborhood residence on child and adolescent outcomes.Psychol Bull.2000;126:309-337.ACKalffMKroesJSHVlesJGMHendriksenFJMFeronJSteyaertTMCBVan ZebenJJollesJvan OsNeighbourhood-level and individual-level effects on child problem behaviour: a multilevel analysis.J Epidemiol Community Health.2001;55:246-250.GDriessenNGuntherJvan OsShared social environment and psychiatric disorder: a multilevel analysis of individual and ecological effects.Soc Psychiatry Psychiatr Epidemiol.1998;33:596-605.Jvan OsGDriessenNGuntherPDelespaulNeighbourhood variation in schizophrenia incidence: evidence for person-environment interaction.Br J Psychiatry.2000;176:243-249.IKawachiBPKennedyKLochnerDProthrow-StithSocial capital, income inequality, and mortality.Am J Public Health.1997;87:1491-1498.Accepted for publication January 23, 2001.Corresponding author and reprints: Jim van Os, MD, PhD, MRCPsych, Department of Psychiatry and Neuropsychology, Maastricht University, European Graduate School of Neuroscience, PO Box 616 (PAR 45), 6200 MD Maastricht, the Netherlands.

Fanous, Ayman; Gardner, Charles; Walsh, Dermot; Kendler, Kenneth S.

doi: 10.1001/archpsyc.58.7.669pmid: 11448374

BackgroundContinuous rather than categorical measures of psychopathology may provide greater statistical power to detect susceptibility loci for schizophrenia. However, it has not been established that the dimensions of schizophrenic symptomatology and personality traits in nonpsychotic individuals share etiological factors. We therefore sought to clarify the relationship between positive and negative symptoms of schizophrenic probands and dimensions of schizotypy in their first-degree relatives.MethodsIn the Roscommon Family Study, we examined the ability of positive and negative symptoms in probands to predict 7 factors of schizotypy in nonpsychotic relatives using regression analysis. These consisted of positive, negative, and avoidant symptoms; odd speech; suspicious behavior; social dysfunction; and symptoms of borderline personality disorder. We examined 3 proband groups: schizophrenia (n = 127); schizophrenia, simple schizophrenia, and schizoaffective disorder (n = 178); and all nonaffective psychoses (n = 216), and their nonpsychotic relatives (n = 309, 477, and 584, respectively).ResultsPositive symptoms in all nonaffective psychoses probands predicted positive schizotypy (β = 0.1972, P= .0004), social dysfunction (β = 0.0719, P= .0489), and borderline personality disorder symptoms (β = 0.1327, P= .0084) in relatives, while negative symptoms predicted negative schizotypy (β = 0.2069, P= .0002), odd speech (β = 0.2592, P= .0001), suspicious behavior (β = 0.2749, P= .0001), and social dysfunction (β = .2398, P= .0002). Proband negative symptoms and borderline personality disorder symptoms in relatives in the schizophrenia, simple schizophrenia, and schizoaffective disorder group were inversely related (β = −0.1185, P= .05).ConclusionsPositive and negative symptoms in schizophrenia predict corresponding schizotypal symptoms in relatives. This provides evidence that these schizophrenic symptom factors (1) are etiologically distinct from each other and (2) occur on an etiological continuum with their personality-based counterparts.PSYCHIATRISTS from a variety of theoretical perspectives, including Kraepelinand Bleuler,have noted that family members of patients with schizophrenia often have odd personality features, including social isolation, poor interpersonal relationships, unusual thought content, and odd speech.These traits were combined into our current concept of schizotypy by Spitzer et al in DSM-III.Despite the familial relationship between schizophrenia and schizotypy and substantial evidence for a genetic basis to schizophrenia,the extent to which the dimensions of schizophrenia and schizotypy share familial etiological factors is unknown.Establishing that normal and disease states represent end points of a single continuum of liability has important implications for understanding the genetic architecture of not only schizophrenia, but other complex disorders as well, such as hypertension and diabetes, where affection is defined quantitatively, not qualitatively. Establishing such a continuum of liability in schizophrenia will inform the methodology of molecular genetic studies, where it has been difficult to define an optimal phenotype.Quantitative trait loci analysis may be statistically more powerful than traditional linkage methods in detecting susceptibility genes for complex disorders,but its use assumes the genetic continuity of normal and disease states.Schizophrenic psychopathology is multidimensional and heterogeneous.Factor analytic studies often result in positive, negative, and disorganization factors.Like schizophrenia, schizotypy is multidimensional and heterogeneous, and is composed of factors that resemble those of schizophrenia.Some are composed of attenuated forms of classical positive and negative symptoms. Positive schizotypy comprises ideas of reference, illusions, and magical thinking, while negative schizotypy includes poor rapport, aloofness, and guardedness.We know of only one study that has addressed the existence of a single continuum of liability for schizophrenic and schizotypal symptoms, showing that negative symptoms in schizophrenic probands were correlated, at the trend level (P<.10), with negative symptoms in their relatives.The correlation was greater than a similar one observed for positive symptoms. This study, however, included ill relatives in the analysis.To further investigate this question, we examined the relationship between classic positive and negative symptoms of schizophrenic probands and dimensions of schizotypy in their first-degree relatives in the Roscommon Family Study. We hypothesized that positive and negative schizophrenic symptoms in probands would predict, respectively, positive and negative schizotypal symptoms. We also predicted relationships between proband negative symptoms and avoidant schizotypy and social dysfunction, as asociality is a prominent negative symptom,and since negative symptoms are robustly related to social dysfunction.SUBJECTS AND METHODSSUBJECTSThe Roscommon Family Study is an epidemiologically based family study of major mental illness in the west of Ireland. Two groups of index probands were examined: (1) schizophrenic—all cases with a diagnosis of schizophrenia from the population-based Roscommon County Case Register(n = 303), and (2) affective—a randomly chosen subsample of the cases from the Case Register with a diagnosis of major affective disorder. Because of budgetary restrictions, 75% of these cases were included (n = 99). Of the schizophrenic probands, 18 had been ascertained through 2 private hospitals cooperating with the registry. We were unable to obtain access to these individuals, reducing the number of schizophrenic probands to 285. An average of 15 years after onset, we attempted to follow up all 384 index probands, of whom 37 were dead, 23 were untraceable, 50 refused interview, and 274 were personally interviewed by 1 of 2 Irish psychiatrists. Medical records were obtained for 359 probands. In cases of incomplete data, collateral histories, from either family members or community nurses, were obtained for an additional 52 index probands. In our judgment, sufficient clinical information was available to render a psychiatric diagnosis in 375 of these cases, of whom 126 individuals (123 from the original schizophrenic group and 3 from the original affective group) met DSM-III-Rcriteria for schizophrenia. These diagnostic reviews were performed by one of us (K.S.K.) or Alan Gruenberg, MD, using a blind best-estimate procedure with demonstrated high interrater reliability.Of these 126 subjects, 99 were personally interviewed, and 102 had 1 or more relatives with a personal interview or hospital record.We attempted to personally interview, blind to proband status, all first-degree relatives, aged 16 years and older and residing in the island of Ireland or central and eastern England, of the index probands and a group of unscreened population controls matched for age and sex. We also attempted to obtain and abstract psychiatric hospital records for all hospitalized relatives. As several individuals and families were ascertained more than once, we used the general proband method, in which all individuals are counted once for each time they are independently ascertained.Personal interviews were completed in 86% of living traceable relatives (n = 1753), including 342 relatives of probands meeting DSM-III-Rcriteria for schizophrenia. Their mean ± SD age was 46 ± 16 years and 49% were male. For an additional 12 relatives of the DSM-III-Rschizophrenic probands, only hospital records were available. Herein, we present results on the relatives of probands with a personal interview and/or a hospital record.DIAGNOSESThe personal interview with probands and relatives was based on the Structured Clinical Interview for DSM-III-Rdiagnoses for Axis I disorders and the Structured Interview for Schizotypyfor schizophrenia-related Axis II disorders. Blind, best-estimate diagnoses using all available information were also made for all relatives with personal interviews and/or hospital records using DSM-III-Rcriteria by 2 psychiatrists (K.S.K. or Alan M. Gruenberg, MD). In addition to coding diagnoses, these 2 psychiatrists completed the Major Symptoms of Schizophrenia Scale (MSSS).This is an instrument designed for use in a best-estimate procedure that codes key symptom and course features as assessed over the entire course of illness. It was designed to allow the experienced clinician to integrate the relative prominence of clinical features over the entire course of illness. The MSSS contains 9 key symptomatic dimensions: delusions (any), schneiderian delusions, hallucinations, positive thought disorder (such as loosening of associations), catatonic symptoms (including stupor and excitement), depressive symptoms, and manic symptoms. In addition, the MSSS rates chronicity of course and global outcome. All symptom variables were coded on 5-point scales. Details are available on request, but the following general guidelines were adopted for the symptomatic variables: 1, clearly not present; 2, possibly present but subthreshold; 3, clearly present but moderate; 4, clearly present and prominent; and 5, clearly present and severe. The reliability of the MSSS was tested on 47 cases with psychotic illness rated blindly by both psychiatrists. Intraclass correlations for these 11 variables ranged from 0.60 for catatonic symptoms to 0.91 for manic symptoms, with a mean ± SD for all 11 variables of 0.77 ± 0.11.In this study, we performed analyses using 3 definitions of proband caseness: (1) probands with a DSM-III-Rdiagnosis of schizophrenia (n = 127); (2) probands with diagnoses of schizophrenia, simple schizophrenia, and schizoaffective disorder (n = 178); (3) probands with any of the above diagnoses as well as delusional disorder, schizophreniform disorder, brief reactive psychosis, and psychosis, not otherwise specified (n = 216). We examined all first-degree relatives of these 3 proband groups, excluding those with diagnoses of schizophrenia, schizoaffective disorder, delusional disorder, schizophreniform disorder, brief reactive psychosis, and psychosis, not otherwise specified (n = 309, 477, and 584, respectively).STATISTICAL ANALYSISA factor analysis was performed by the method of principal components with varimax rotation using the SAS procedure FACTORon the 9 symptoms of the MSSS, selecting factors with an eigenvalue of 1.0 or greater. As outlined previously,this yielded the 3 factors: (1) negative—with high loadings on negative thought disorder, affective deterioration, positive thought disorder, and catatonia; (2) positive—with high loadings on schneiderian delusions, any delusions, and hallucinations; and (3) affective—with high loadings on manic and depressive symptoms. As detailed elsewhere,a similar factor analysis was performed on all 25 items of the Structured Interview for Schizotypy. This yielded 7 factors: positive, negative, and avoidant symptoms, social dsyfunction, suspicious behavior, and symptoms of borderline personality disorder (BPD).Missing items were imputed by assigning them the mean score of the items in their respective factor. Subjects missing 50% or more of necessary data were excluded from analysis. All scale scores were transformed into standardized scores with mean of 0 and SD of 1. Regression analyses were performed with positive and negative scores of probands as independent variables and all 7 schizotypy factor–derived scores of relatives as dependent variables. Age and sex of relatives and relationship to proband were entered as covariates. A weighted least squares approach was used, weighting for the number of members per family. Analyses were implemented using the GLM procedure in SAS.The results of the above analyses are presented without Bonferroni corrections. We present results using 2-tailed Pvalues.RESULTSThe standardized regression slopes and Pvalues of all analyses are presented in Table 1. More inclusive definitions of caseness were generally associated with more statistically significant results. Proband negative symptoms predicted more schizotypy factors in relatives. These included the negative factor, odd speech, suspicious behavior, and social dysfunction. Negative symptoms also had an inverse relationship with symptoms of borderline personality disorder (BPD) in the analysis of schizophrenia, simple schizophrenia, and schizoaffective disorder probands. Positive symptoms in probands predicted positive schizotypy, BPD symptoms, and social dysfunction in relatives.Relationship Between Positive and Negative Symptoms of Schizophrenia and Dimensions of Schizotypy in Nonpsychotic Relatives*Schizotypy FactorPositive SymptomsNegative SymptomsSlopePSlopePSchizophrenia (n = 127)Positive schizotypy0.1531.07520.0244.7636Negative schizotypy0.0719.33840.2017.0202Avoidant schizotypy0.0256.50350.0713.6588Suspicious behavior0.0919.22220.3162.0001Odd speech0.1031.28330.3320.0009Social dysfunction0.0651.33280.2754.0002Borderline personality0.0796.18070.0025.9694Schizophrenia, Simple Schizophrenia, and Schizoaffective Disorder (n = 178)Positive schizotypy0.1983.0014−0.0158.8099Negative schizotypy0.0642.24160.2104.0004Avoidant schizotypy−0.0065.90270.0929.2014Suspicious behavior0.0828.13890.2913.0001Odd speech0.1121.10810.2716.0002Social dysfunction0.0815.12490.2629.0002Borderline personality0.1495.0071−0.1185.05All Nonaffective Psychoses (n = 216)Positive schizotypy0.1972.00040.0116.8430Negative schizotypy0.0382.44080.2069.0002Avoidant schizotypy−0.0072.88080.1068.0686Suspicious behavior0.0613.05200.2749.0001Odd speech0.0840.16540.2592.0001Social dysfunction0.0719.04890.2398.0002Borderline personality0.1327.0084−0.0956.0746*Results in boldface indicate significant at P≤.05.COMMENTIn this study, we examined the hypothesis that positive and negative symptoms of schizophrenia share familial etiological factors with corresponding dimensions of schizotypy by relating symptom scores in psychotic probands with scores on 7 schizotypy factors in their nonpsychotic relatives. Our purpose was to elucidate the etiological lines of demarcation in the schizophrenia spectrum and to further characterize the pathway between familial etiological factors and the expression of clinical phenotype in schizophrenia. Although a previous study of this sample showed that the deficit syndrome was associated with social isolation in relatives,this is the first study to show that schizophrenia and schizotypy share familial etiological factors for both their positive and negative dimensions.The results generally confirmed our hypotheses. Overall, negative symptoms had statistically significant relationships with more schizotypy factors than did positive symptoms. This coheres with the general notion that negative symptoms have greater familial, and possibly genetic, bases than do positive symptoms. Evidence suggesting this is their association with greater family history,worse premorbid functioning,and greater longitudinal stability.Furthermore, the phenomenological resemblance between positive schizotypal symptoms such as magical thinking and illusions on one hand, and positive schizophrenic symptoms on the other, appears to be less than that between negative symptoms of schizotypyand schizophrenia.The observed relationships between negative symptoms and negative schizotypy and social dsyfunction have face validity, but those with suspicious behavior, odd speech, and BPD symptoms were unexpected. Suspicious behavior might be explained by possible phenomenological overlap with the negative symptoms asociality and poor rapport.Furthermore, one of the items loading on our negative schizotypy factor was guardedness, which intuitively resembles suspiciousness. Features of BPD, however, such as affective instability, inappropriate anger, and impulsivity, seem opposite to classic negative symptoms,which may explain the inverse relationship between them.We were unable to confirm our hypothesis that negative symptoms would predict avoidant schizotypy in relatives. This was indeed surprising, as asociality is an important negative symptom.Our avoidant schizotypy factor, however, while including social isolation, also loaded on the anxiety-related traits hypersensitivity, anxiety, and social anxiety, making an etiological relationship with negative symptoms less intuitively appealing. Furthermore, proband clinical features did not predict anxiety disorders in relatives in a prior study of this sample.We did not expect to find an etiological relationship between negative symptoms and odd speech. However, in several factor analyses,including the one performed on this sample,the negative factor loaded on odd speech or disorganization. Furthermore, parents of nonparanoid schizophrenic individuals manifest greater levels of formal thought disorder than do parents of paranoid schizophrenic individuals,and this subtype should be associated with lower levels of negative symptoms than are other subtypes, based on its operational definition in DSM-III-R.Positive symptoms were significantly related to positive schizotypy, social dysfunction, and BPD symptoms. Social dysfunction had significant relationships with both positive and negative symptoms, which may indicate that it is etiologically related to severity of illness rather than specific symptom dimensions. The relationship with BPD symptoms may be related to the propensity for some patients with BPD to exhibit stress-induced paranoia or other mild psychoticlike symptoms, as operationalized in the DSM-III-Rcriteria.The effect sizes (regression slopes) of the analyses did not change substantially when the definition of proband affection was broadened to include nonschizophrenic psychotic disorders, while significance levels increased substantially. This supports the spectrum concept of schizophrenia—that several disorders share with schizophrenia the same underlying liability.This was consistent with a previous study of this sample in which the spectrum concept was formally tested by fitting a multiple threshold model to the data.Our results indicate that from a familial perspective, the positive and negative dimensions of schizophrenia "breed true" as their attenuated personality-based variants in nonpsychotic individuals. This suggests that the influence of familial etiological factors determining the expression of these symptom dimensions reaches across the boundary of psychotic illness to phenomena currently classified under the rubric of personality. The specificity of the relationships between the positive schizophrenic and schizotypy factors, as well as between the negative schizophrenic and schizotypy factors, further validates the etiological distinctness of some schizophrenic symptom domains, as suggested by sibling resemblance for clinical features.These results provide validation for quantitative phenotype definition in genetic linkage and association studies. As genes are likely to comprise substantial components of the familial etiological factors shared by the dimensions of schizophrenia and schizotypy, the same genes would presumably be involved in both phenotypes. This strategy may increase the power of such studies by including more "units" of genetic liability to schizophrenia in analysis.These results should be interpreted in the context of 4 methodological limitations. First, neither the Structured Interview for Schizotypy nor the MSSS examined all possibly relevant signs and symptoms of schizotypy and schizophrenia, respectively. Furthermore, the 2 scales differ in their comprehensiveness, with the Structured Interview for Schizotypy containing many more items than the MSSS. Both the assessment instrument used and the number of items included in factor analysis may have a significant impact on the composition and number of factors extracted. Perhaps the use of other instruments would have led to different relationships between the factors of schizophrenia and schizotypy. Our use of only 2 factors may be an oversimplification of the multifaceted variability of schizophrenic psychopathology, but there is no consensus in the field about the number of dimensions that best represents the full clinical picture of schizophrenia.We opted to use positive and negative symptoms because of their historical prominence and their conception in the minds of many clinicians as core illness dimensions.Second, the use of different covariates may have resulted in different relationships between the factors of schizophrenia and schizotypy. We used age, sex, and relationship to proband. It may be argued that we should have also controlled for social class of the relatives, as this predicted several schizotypy factors.However, when this covariate was used in a prior study, it had no impact on the prediction of proband diagnoses of psychotic disorders by schizotypy factors.Third, it is not possible to determine whether the results obtained herein are due to genetic as opposed to environmental factors. In addition, an argument made by Kendler et alwith respect to sibling resemblance for psychotic syndromes may apply here. This would hold that the correlations between relatives as reported here might be due not only to susceptibility genes for schizophrenia but also to genes determining temperament and intellect. Future studies should attempt to partial out these effects.Fourth, while we tested a hypothesis about primary negative symptoms, or those due to the disease itself, it was not possible to differentiate between these and secondary negative symptoms in probands. Secondary negative symptoms may result from depression, medication-induced parkinsonism, and chronicity.To differentiate between these and primary negative symptoms, it would have been necessary to perform negative symptom ratings before and after treatment,which would be impractical in a genetic-epidemiological sample such as ours. This possible confound may have decreased the effect sizes obtained in the regression analyses, although a recent study showed that while negative symptoms were associated with greater family history, the deficit syndrome was not.EKraepelinRMBarclayDementia Praecox and Paraphrenia.8th ed. 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Additional support came from the Scottish Rite Benevolent Foundation's Schizophrenia Research Program, Northern Masonic Jurisdiction, United States.Corresponding author and reprints: Ayman Fanous, MD, Box 980126, Richmond, VA 23298-0126 (e-mail: [email protected]).

Bennedsen, Birgit E.; Mortensen, Preben B.; Olesen, Anne V.; Henriksen, Tine B.

doi: 10.1001/archpsyc.58.7.674pmid: 11448375

BackgroundWomen with schizophrenia have increased exposure to risk factors for congenital malformations, stillbirths, and infant deaths among their children. However, the occurrence of these outcomes is unknown.MethodsThe risks of stillbirth and infant death among 2230 children of women with schizophrenia were compared with the risks among 123 544 children in the general population. The risk of congenital malformations among 746 children of women with schizophrenia were compared with the risk among 56 106 children in the general population. The year of birth, the sex of the child, the mother's age, and parity were included in the analyses as potential confounders. We had no information about socioeconomic status, smoking status, substance abuse, or psychotropic medication use.ResultsChildren of women with schizophrenia had increased risk of postneonatal death (relative risk [RR], 2.76; 95% confidence interval [CI], 1.67-4.56). This was largely explained by an increased risk of sudden infant death syndrome (RR, 5.23; 95% CI, 2.82-9.69). There was no statistically significant increased risk of stillbirth (RR, 1.51; 95% CI, 0.94-2.40) or neonatal death (RR, 1.26; CI, 0.77-2.06). Children of women with schizophrenia had a marginally statistically significant increase in the risk of congenital malformations (RR, 1.70; 95% CI, 1.04-2.77).ConclusionsChildren of women with schizophrenia have a considerable increased risk of death caused by sudden infant death syndrome. However, the results should be interpreted in the light of failure to adjust for socioeconomic status, substance abuse, smoking status, and psychotropic medication use.THE RISK OF congenital malformations, stillbirths, and infant deaths among children of women with schizophrenia has rarely been investigated. Most studies have been based on small sample sizes. However, there is some evidence that the children of schizophrenic women have an increased risk of these outcomes.An increased risk of stillbirth and infant death might be suspected, as schizophrenic women are often smokers and substance abusers, and tend to live in poor socioeconomic conditions.If it was known that children of women with schizophrenia have an increased risk of stillbirth or infant death, it might be possible to prevent some of these deaths in the future.It might be suspected that the prevalence of congenital malformations is increased in children of women with schizophrenia because of the administration of antipsychotic drugs during pregnancy.A common genetic or environmental cause between schizophrenia and congenital malformations has also been suggested.If it is found that children of women with schizophrenia have an increased prevalence of congenital malformations, it may be of interest to research in the etiology of schizophrenia.The aim of this study was to investigate the risk of congenital malformations, stillbirths, and infant deaths among children of women with schizophrenia.SUBJECTS AND METHODSDATA SOURCESData were created by a linkage between the Danish Psychiatric Central Register,the Danish Medical Birth Registry,and the National Registry of Congenital Malformations,which are nationwide registers covering the total Danish population. The Danish Psychiatric Central Register holds computerized information about all admissions to psychiatric departments in Denmark reported by psychiatrists since 1969.The Danish Medical Birth Registry holds information about all births in Denmark since 1973, reported by the midwives on structured coding sheets.Based on linkage between birth notification forms and death certificates, the register contains information about stillbirths and infant deaths. The National Registry of Congenital Malformations was established in 1983. The information is based on standardized reports by physicians about malformations diagnosed during the first year of life.In addition to the children registered in the National Registry of Congenital Malformations, some children were registered in the Danish Medical Birth Registry as having congenital malformations as a cause of death.SUBJECTSThe study group comprised all single births (n = 2230) by women with schizophrenia (n = 1544) in Denmark during the study period of 1973 to 1993. Data concerning congenital malformation were available for the years 1983 to 1992. In that period, the study group consisted of 746 births to 584 women with schizophrenia. Women with schizophrenia were defined as all women who were admitted at least once as inpatients to a Danish psychiatric department during 1969 to 1993 with the diagnosis schizophrenia (International Classification of Diseases, Eighth Revision [ICD-8]).The women in the control group were all women who gave birth to a single child in Denmark on 1 of 3 randomly selected days each month. If these women gave birth more than once in the study period, all their single births were included. For the years 1973-1993 the control group comprised 123 544 births by 72 840 women. For the years 1983-1992 it comprised 56 106 births by 38 589 women.STATISTICAL ANALYSISStatistical analyses were performed using the SAS system version 6.12 (SAS Institute, Cary, NC). Adjusted relative risks (RRs) with 95% confidence intervals (CIs) for the outcomes in children of schizophrenic women compared with controls were computed in an additive model with the logarithm as link function. Two-tailed Pvalues were obtained by likelihood ratio tests. Because the year of birth, the sex of the child, the mother's age (<20, 20-34, ≥35 years), and parity (primipara, multipara) could be associated with the outcomes of interest, these variables were included in the analyses as potential confounders. Information about parity was available only for the years 1978 to 1993, whereas information about the number of previous pregnancies was available for the entire study period and was used as a proxy for parity. The deliveries of women with schizophrenia were previously found to differ from those of control women according to birth weight (<2500 g, ≥2500 g) and gestational age (<37 weeks, ≥37 weeks).As preterm birth and low birth weight are strongly associated with neonatal mortality,these variables were included when the risk of death was analyzed. Differences in the deliveries of women with schizophrenia who gave birth before or after their first admission to a psychiatric department were investigated. It was also determined whether the estimates changed after exclusion of births to women with schizophrenia who had their first psychiatric admission during pregnancy, in the first 3 months after pregnancy, or at the death of a child.Some women gave birth more than once in the study period. Deliveries of the same woman cannot be considered as independent events. To adjust for this potential problem, adjusted relative risks were also computed using the General Estimation Equation (GEE). This method takes into account that repeated observations over subjects are correlated.Neonatal death was defined as death of a liveborn infant during the first month of life. Postneonatal death was defined as death between the age of 1 month and 1 year.RESULTSThe distribution of the potential confounders among women with schizophrenia and control women is shown in Table 1.Table 1. Distribution of Confounders in Schizophrenic and Control Women (1973-1993)Confounder% of Womenχ2dfPSchizophrenic Group (n = 2230)Control Group (n = 123 544)Year of birth1973-197949.636.5233.552.0011980-198630.529.41987-199319.934.1Sex of childBoy52.651.60.741.39Girl47.448.4Maternal age, y<204.54.573.152.00120-3483.888.5≥3511.77.0No. of previous pregnancies0 or not reported*34.435.430.413.001132.734.1216.517.9≥316.412.6Parity (1978-1993)Primiparous or not reported†51.646.415.021.001Multiparous48.453.6Gestational age, wk<377.24.718.862.00137-4068.668.3>4024.227.0Birth weight, g<25008.34.974.702.0012500-399981.981.5≥40009.813.6*In 1973-1990 it was possible to distinguish between not reported and no previous pregnancies. There was 0.05% not reported in both the schizophrenia group and the control group.†Information about parity was only available in 1978-1993. In 1978-1990 it was possible to distinguish between primiparity and not reported parity. There was 0.08% not reported in the schizophrenia group and 0.06% not reported in the control group.CONGENITAL MALFORMATIONSAmong children born to women with schizophrenia in 1983-1992, 16 children delivered by 15 women had a total of 20 malformations (Table 2). These malformations were either conspicuous or potentially life threatening, meaning that they would probably have been diagnosed in all children. As shown in Table 3, there was a small increase of marginal statistical significance in the risk of having at least 1 congenital malformation among children of women with schizophrenia. There was no evident excess of malformations in any specific organ system, although the frequency of malformations in eyes or ears was greater in children of women with schizophrenia (Table 2). There was no statistically significant difference between children of women with schizophrenia who gave birth before or after their first admission to a psychiatric department (P= .91). Exclusion of births to women who had their first psychiatric admission during or in the first 3 months after pregnancy did not change the results. Analysis of data using GEE left the RRs and CIs essentially unchanged.Table 2. Number of Congenital Malformations in Children of Schizophrenic and Control WomenRegionNo. (%)* of MalformationsSchizophrenic GroupControl GroupNervous system1 (0.13)62 (0.11)Eyes or ears3 (0.40)48 (0.09)Face2 (0.27)153 (0.27)Heart or circulatory system5 (0.67)174 (0.31)Respiratory system016 (0.03)Digestive system1 (0.13)45 (0.08)Genitals or urinary system2 (0.27)164 (0.29)Extremities, muscles, or bones5 (0.67)310 (0.55)Others or unspecified1 (0.13)45 (0.08)*All malformations in a specific region in percentage of all children. If a child had more than 1 malformation in a specific region, each malformation was counted separately.Table 3. Relative Risks of Congenital Malformations, Stillbirths, Neonatal and Postneonatal Deaths, and Sudden Infant Death Syndrome in Children of Women With Schizophrenia Compared With Control Women*Proportion of Controls, %Relative Risk (95% Confidence Interval)PCrudeAdjustedCongenital malformations1.21.75 (1.07-2.86)1.70† (1.04-2.77).05Stillbirth0.51.63 (1.02-2.60)1.51‡ (0.94-2.40).11Neonatal death0.51.39 (0.85-2.29)1.26‡ (0.77-2.06).38Postneonatal death0.32.75 (1.67-4.54)2.76‡ (1.67-4.56)<.001Sudden infant death syndrome0.14.74 (2.56-8.76)5.23‡ (2.82-9.69)<.001*For congenital malformations, n = 746 in schizophrenic group and n = 56 106 in the control group; all others, n = 2230 in the schizophrenic group and n = 123 544 in the control group.†Adjusted for year of birth, sex of child, mother's age, and parity.‡Adjusted for year of birth, sex of child, mother's age, and number of previous pregnancies.STILLBIRTHSThere were 18 stillbirths among children of women with schizophrenia for the period 1969-1993 (Table 4). Two of these were delivered by the same woman. As seen in Table 3, the RR of stillbirth was not significantly increased in children of women with schizophrenia. After adjustment for birth weight, the RR decreased to 1.14 (95% CI, 0.72-1.81). Because a substantial number of stillborns in the control group had missing gestational ages, it was not possible to adjust for gestational age. There was no difference between births occurring before or after the mothers' first psychiatric admission (P= .63). Exclusion of births to women with schizophrenia who had their first admission during or in the first 3 months after pregnancy did not change the results. Analysis of data using GEE left the RRs and CIs essentially unchanged. Estimation of RR excluding children born with malformations did not change the results.Table 4. Primary Causes of Death in Stillborn Children of Schizophrenic and Control WomenCause of DeathNo. (%)* StillbirthsSchizophrenic GroupControl GroupCongenital malformations3 (0.13)70 (0.06)Maternal disease020 (0.01)Preeclampsia1 (0.05)28 (0.02)Placental or umbilical cord conditions7 (0.31)293 (0.24)Fetal anoxia or hypoxia2 (0.09)81 (0.07)Other reasons1 (0.05)27 (0.02)Unknown reasons4 (0.18)93 (0.08)Total18 (0.81)612 (0.50)*Percentage of all children.NEONATAL DEATHSThere were 16 neonatal deaths among children of women with schizophrenia (Table 5). Two of these children were delivered by the same woman. As seen in Table 3, the risk of neonatal death was not significantly increased in children of women with schizophrenia. After adjustment for birth weight and gestational age, the RR decreased to 0.88 (95% CI, 0.54-1.45). There was no difference between births given before and after the mothers' first psychiatric admission (P= .46). The estimates did not change after exclusion of children whose mothers had their first psychiatric admission in relation to pregnancy or death of child. Analysis by GEE did not change the results. After exclusion of children born with malformations, the RR of neonatal death was essentially unchanged.Table 5. Primary Causes of Death in Children Who Died NeonatallyCause of DeathNo. (%)* of Neonatal DeathsSchizophrenic GroupControl GroupCongenital malformations6 (0.27)211 (0.17)Complications during pregnancy and delivery4 (0.18)127 (0.10)Neonatal anoxia and hypoxia4 (0.18)191 (0.16)Immaturitas1 (0.04)42 (0.03)Infections029 (0.02)Others1 (0.04)38 (0.03)Total16 (0.72)638 (0.52)*Percentage of all children at risk.POSTNEONATAL DEATHSThere were 16 children delivered of 16 women with schizophrenia who died during the postneonatal period (Table 6). The risk of postneonatal death was increased among children of women with schizophrenia (Table 3). The RR remained unchanged after adjustment for gestational age and birth weight. There was no difference between births occurring before or after the women's first psychiatric admission (P= .61). After exclusion of children of women who had their first psychiatric admission in relation to pregnancy or death of child, the RR decreased to 2.34 (95% CI, 1.34-4.07). Analysis by GEE failed to change the results. After exclusion of children with malformations for the years 1983-1992, the RR of postneonatal death increased from 4.14 (95% CI, 2.04-8.42) to 5.71 (95% CI, 2.66-12.26). After exclusion of children who died of sudden infant death syndrome (SIDS), the adjusted RR of postneonatal death was 1.59 (95% CI, 0.71-3.59).Table 6. Primary Causes of Death in Children Who Died PostneonatallyCause of DeathNo. (%)* of Postneonatal DeathsSchizophrenic GroupControl GroupCongenital malformations3 (0.14)102 (0.08)Infections2 (0.09)46 (0.04)Sudden infant death syndrome10 (0.46)124 (0.10)Others1 (0.04)52 (0.04)Total16 (0.73)324 (0.26)*Percentage of all children at risk.As seen in Table 3, children of women with schizophrenia had a statistically significant increased risk of SIDS. Adjustment for birth weight and gestational age failed to change the results. There was no difference in risk between children delivered before or after the mothers' first psychiatric admission (P= .90). Analysis by GEE made no difference.COMMENTWe found a small increase in the risk of congenital malformations in children of women with schizophrenia. Jablensky et alreported similar findings. Sobelreported an increased risk of congenital malformations among children of women with schizophrenia, but no statistical analysis was performed. Paffenbarger et alfound no difference in the frequency of congenital malformations between the offspring of women with postpartum psychoses and the offspring of control women, but no data were provided. Miller et alfound no difference in risk of congenital malformations among children of female psychiatric patients compared with children born to the general population. McNeil et alfound no difference in the frequency of malformations including minor physical anomalies among children of women with schizophrenia compared with children of control women.We found an increased risk of postneonatal death, which was largely explained by SIDS, among children of women with schizophrenia. There was no statistically significant increased risk of stillbirth or neonatal death. Most previous studies that investigated fetal or infant death were small studies without adjustment for potential confounders. Some found a tendency toward an increased risk of fetal, perinatal, or neonatal death among children of women with schizophrenia or other mental diseases.Others found a tendency toward a lower risk.Modrzewskafound an increased risk of stillbirth and infant death among 553 children of parents with schizophrenia. Also, Jablensky et alreported an increased risk of infant death.This study is considerably larger than previous studies investigating these outcomes, and it is the only study in which adjustment for some of the potential confounders was possible. Unfortunately, we had no information about socioeconomic status, smoking status, substance abuse, and psychopharmacological treatment, which may lead to residual confounding. Small differences in risks could not be detected in this data set because of the relatively few events. It is possible that there would be statistically significant differences in the risks of stillbirth and neonatal death in a larger data set.The control group was a random sample of all births in Denmark during the study period, which minimized the risk of selection bias. Probably some of the women with schizophrenia were also included in the control group. If that was the case for 10% of the women with schizophrenia, their deliveries would amount to approximately 0.2% of the births in the control group, which would be of minimal importance. The women with schizophrenia were selected by the criteria that they were admitted and correctly diagnosed with schizophrenia at least once during 1973 to 1993. It is not known how many patients with schizophrenia were never admitted as inpatients, but probably it is a minority.It could be suspected that complications during pregnancy or delivery, or infant death, might lead to an increased risk of psychiatric admission for schizophrenia. This could lead to selection bias. However, exclusion of births to women who had their first admission in relation to pregnancy, delivery, or death of a child did not essentially change the results. Munk-Jørgensenvalidated the ICD-8diagnosis of schizophrenia in the Danish Psychiatric Central Register, and found a positive predictive value of the diagnosis of approximately 90%. This means that the majority of patients in this study probably were correctly diagnosed as having schizophrenia. There were relatively few births to women with schizophrenia late in the study period (Table 1), which may be explained by the inclusion criteria. Inclusion in the study required an admission with schizophrenia during 1969 to 1993, and a delivery during 1973 to 1993. The chance of having experienced both these events was higher for women who gave birth early in the study period.The National Registry of Congenital Malformations was found to have a completeness of more than 90% according to the cleft lip and palate registration.Other malformations in the registry have never been validated. However, a considerable underreporting has been shown.There is no reason to believe that there was a selectively higher reporting of malformations among children of women with schizophrenia, as their reported malformations were of such types that they would probably also have been diagnosed in other children. The general validity of the registration of causes of death for infants in Denmark is not known,but was approximately 85% of deaths based on autopsy findings.The diagnosis of SIDS cannot easily be validated. There were regional differences in the frequencies of SIDS. These differences were probably partly explained by differences in autopsy rates and interpretations of autopsy findings, which may not reflect genuine differences in rates.As there are probably also regional differences in the incidence of schizophrenia,it cannot be excluded that some of the observed increased risk of SIDS in children of women with schizophrenia was explained by these regional differences.We previously found that children of women with schizophrenia generally had lower birth weight and lower gestational age.These differences did not explain the increased risk of postneonatal death and SIDS. We also found a tendency toward a lower Apgar score in the children of women with schizophrenia,which might indicate that the children were in poorer condition at birth and thus at increased risk of death. It is not known how many of these children actually lived with their mothers, but it is likely that the women with schizophrenia were less capable of taking care of their infants compared with other women. Their children might therefore be in a poorer nutritional condition and have poorer general health compared with other children. Women with schizophrenia may also have an inadequate reaction if their children become ill, which might lead to insufficient medical treatment and increased risk of death. Maternal substance abuse and especially maternal smoking are known risk factors for SIDS.As women with schizophrenia are more likely to be smokers or substance abusers compared with other women,this could explain some of the increased risk of SIDS in this study. In a Danish study, Geertinger and Theiladefound that schizophrenia and SIDS had a tendency to occur in the same families. They suggested a genetic association between schizophrenia and SIDS, which might be an explanation for the findings in this study. However, co-occurrence in families might also be caused by shared environmental risk factors. It is also possible that some of the SIDS deaths were disguised homicides, which may occur more frequently among children of women with schizophrenia.It might be suspected that the occurrence of congenital malformations and infant deaths was increased among children of women with schizophrenia due to the administration of antipsychotic drugs during pregnancy or lactation. Altshuler et alconcluded in a meta-analysis that first-trimester exposure to low-potency antipsychotic medications may lead to a small increase in the risk of congenital malformations. Little is known about risks associated with prenatal exposure to high-potency antipsychotic drugs and of newer antipsychotic drugs. However, most studies found that administration of haloperidol during pregnancy did not increase risk of congenital malformations.There may be an association between genetic or environmental risk factors contributing to schizophrenia and to congenital malformations. This hypothesis was supported by the findings of an increased prevalence of minor physical abnormalities or congenital malformations among people with schizophreniaand also among their relatives.We found no difference in the risk of death or congenital malformations between children who were born before or after their mothers' first psychiatric admission. This is probably due to the environmental risk factors such as smoking and socioeconomic disadvantages that may be present before the first admission.It also suggests that use of antipsychotic drugs during pregnancy is not a major risk factor for these outcomes.The most important clinical implication of our findings is that pregnant women with schizophrenia, like other pregnant women, should be encouraged to refrain from smoking. Another clinical implication is that women with schizophrenia who keep custody of their children should have access to close supervision by health personnel to secure the well-being of the child and thereby prevent potentially avoidable infant deaths.The association between congenital malformations and schizophrenia was weak in this study, a finding that should be tested in future studies. The association between schizophrenia and SIDS might be explained by common etiological factors and should be further investigated.AJablenskySZubrickVMorganCBowerTPinderThe offspring of women with schizophrenia and affective psychoses: a population study.Schizophr Res.2000;41:8.BBennedsenAdverse pregnancy outcome in schizophrenic women: occurrence and risk factors.Schizophr Res.1998;33:1-26.JMFriedmanJEPolifkaThe effects of neurologic and psychiatric drugs on the fetus and nursing infant.Baltimore, Md: Johns Hopkins University Press; 1998.LLAltshulerLCohenMPSzubaVKBurtMGitlinJMintzPharmacologic management of psychiatric illness during pregnancy: dilemmas and guidelines.Am J Psychiatry.1996;153:592-606.BIsmailECantor-GraaeTFMcNeilMinor physical anomalies in schizophrenic patients and their siblings.Am J Psychiatry.1998;155:1695-1702.ABGoodmanCongenital anomalies in relatives of schizophrenic probands may indicate a retinoid pathology.Schizophr Res.1996;19:163-170.PMunk-JørgensenPBMortensenThe Danish Psychiatric Central Register.Dan Med Bull.1997;44:82-84.LBKnudsenJOlsenThe Danish Medical Birth Registry.Dan Med Bull.1998;45:320-323.KChristensenLBKnudsenRegistration of congenital malformations in Denmark.Dan Med Bull.1998;45:91-94.BEBennedsenPBMortensenAVOlesenTBHenriksenPreterm birth and intra-uterine growth retardation among children of women with schizophrenia.Br J Psychiatry.1999;175:239-245.GSBerkowitzEPapiernikEpidemiology of preterm birth.Epidemiol Rev.1993;15:414-443.MEStokesCSDavisGGKochCategorical Data Analysis Using the SAS System.Cary, NC: SAS Institute Inc; 1995.DESobelInfant mortality and malformations in children of schizophrenic women.Psychiatr Q.1961;35:60-63.RSPaffenbargerCHSteinmetzBGPoolerRTHydeThe picture puzzle of the postpartum psychoses.J Chron Dis.1961;13:161-173.WHMiller JrJDBloomMPResnickChronic mental illness and perinatal outcome.Gen Hosp Psychiatry.1992;14:171-176.TFMcNeilGBlennowLLundbergCongenital malformations and structural developmental anomalies in groups at high risk for psychosis.Am J Psychiatry.1992;149:57-61.MBågedahl-StrindlundMentally ill mothers and their children: an epidemiological study of antenatal care consumption, obstetric conditions, and neonatal health.Acta Psychiatr Scand.1986;74:32-40.RORiederDRosenthalPWenderHBlumenthalThe offspring of schizophrenics: fetal and neonatal deaths.Arch Gen Psychiatry.1975;32:200-211.GWredeSAMednickMOHuttunenCGNilssonPregnancy and delivery complications in the births of an unselected series of Finnish children with schizophrenic mothers, II.In: Watt NF, Anthony EJ, Wynne LC, Rolf JE, eds. Children at Risk for Schizophrenia.London, England: Cambridge University Press; 1984:515-525.LJMillerMFinnertySexuality, pregnancy, and childrearing among women with schizophrenia-spectrum disorders.Psychiatr Serv.1996;47:502-506.MZaxAJSameroffHMBabigianBirth outcomes in the offspring of mentally disordered women.Am J Orthopsychiatry.1977;47:218-230.KModrzewskaThe offspring of schizophrenic parents in a North Swedish isolate.Clin Genet.1980;17:191-201.PMunk-JørgensenFaldende førstegangsindlæggelsesrater for skizofreni i Danmark 1970-1991 [Decreasing first admission rates for schizophrenia in Denmark 1970-1991] [thesis].Copenhagen, Denmark: University of Copenhagen, Dept of Psychiatric Demography; 1995.Sundhedsstyrelsen [The National Board of Health]Misdannelser [Malformations].In: Sundhedsstyrelsen [The National Board of Health], ed. Medicinsk fødsels-og misdannelsesstatistik 1994 og 1995 [Medical birth and malformation statististics 1994 and 1995]. Copenhagen, Denmark: Sundhedsstyrelsen [The National Board of Health]; 1997:45-57.KJuelKHelweg-LarsenThe Danish registers of causes of death.Dan Med Bull.1999;46:354-357.LBKnudsenKHelweg-Larsen[Frequency of causes of death related to sudden infant death syndrome in Denmark during the period 1972-1983].Ugeskr Laeger.1990;152:1164-1167.EMSchelinPMunk-JørgensenAVOlesenRegional differences in schizophrenia incidence in Denmark.Acta Psychiatr Scand.2000;101:293-299.BEBennedsenPBMortensenAVOlesenTBHenriksenMFrydenbergObstetric complications in women with schizophrenia.Schizophr Res.2001;47:167-175.HRAndersonDGCookPassive smoking and sudden infant death syndrome: review of the epidemiological evidence.Thorax.1997;52:1003-1009.CCBlackwellDMWeirThe role of infection in sudden infant death syndrome.FEMS Immunol Med Microbiol.1999;25:1-6.HEJefferyAMegevandHPageWhy the prone position is a risk factor for sudden infant death syndrome.Pediatrics.1999;104:263-269.JAMorrisThe common bacterial toxins hypothesis of sudden infant death syndrome.FEMS Immunol Med Microbiol.1999;25:11-17.IFaresKMMcCullochTNRajuIntrauterine cocaine exposure and the risk for sudden infant death syndrome: a meta-analysis.J Perinatol.1997;17:179-182.PGeertingerPTheiladeVuggedød og skizofreni, I.Månedsskr Prakt Lægegern.1984;9:553-560.CDalmanPAllebeckJCullbergCGrunewaldMKosterObstetric complications and the risk of schizophrenia: a longitudinal study of a national birth cohort.Arch Gen Psychiatry.1999;56:234-240.KCMurphyMJOwenMinor physical anomalies and their relationship to the aetiology of schizophrenia [editorial].Br J Psychiatry.1996;168:139-142.MFGreenPSatzCChristensonMinor physical anomalies in schizophrenia patients, bipolar patients, and their siblings.Schizophr Bull.1994;20:433-440.BJessen-PetersenPsykotiske patienter med misbrugsproblemer [Psychotic patients with abuse problems] [PhD dissertation].Copenhagen, Denmark: University of Copenhagen, FADL's Forlag; 1994.BPDohrenwendSSchwartzSocioeconomic status and psychiatric disorders.Curr Opin Psychiatry.1995;8:138-141.PAllebeckCAdamssonAEngstromURydbergCannabis and schizophrenia: a longitudinal study of cases treated in Stockholm County.Acta Psychiatr Scand.1993;88:21-24.Accepted for publication December 21, 2001.This study was supported by the Theodore and Vada Stanley Foundation, National Institute of Mental Health grant MH 53188, the Health Insurance Fund, and the Danish Medical Research Council.Presented as a poster at the 10th Biennial Winter Workshop on Schizophrenia, Davos, Switzerland, February 5-11, 2000.Drs Bennedsen and Mortensen participated in all processes of the study. Dr Henriksen took part in discussions about analyses, and reporting of the study. Statistical analysis was done by Ms Olesen. The manuscript was written by Dr Bennedsen, and edited by the other authors.Corresponding author: Birgit E. Bennedsen, MD, PhD, Department of Psychiatric Demography, Psychiatric Hospital in Aarhus, Aarhus University Hospital, Skovagervej 2, DK-8240 Risskov, Denmark.

Sherbourne, Cathy D.; Wells, Kenneth B.; Duan, Naihua; Miranda, Jeanne; Unützer, Jürgen; Jaycox, Lisa; Schoenbaum, Michael; Meredith, Lisa S.; Rubenstein, Lisa V.

doi: 10.1001/archpsyc.58.7.696pmid: 11448378

BackgroundThis article addresses whether dissemination of short-term quality improvement (QI) interventions for depression to primary care practices improves patients' clinical outcomes and health-related quality of life (HRQOL) over 2 years, relative to usual care (UC).MethodsThe sample included 1299 patients with current depressive symptoms and 12-month, lifetime, or no depressive disorder from 46 primary care practices in 6 managed care organizations. Clinics were randomized to UC or 1 of 2 QI programs that included training local experts and nurse specialists to provide clinician and patient education, assessment, and treatment planning, plus either nurse care managers for medication follow-up (QI-meds) or access to trained psychotherapists (QI-therapy). Outcomes were assessed every 6 months for 2 years.ResultsFor most outcomes, differences between intervention and UC patients were not sustained for the full 2 years. However, QI-therapy reduced overall poor outcomes compared with UC by about 8 percentage points throughout 2 years, and by 10 percentage points compared with QI-meds at 24 months. Both interventions improved patients' clinical and role outcomes, relative to UC, over 12 months (eg, a 10-11 and 6-7 percentage point difference in probable depression at 6 and 12 months, respectively).ConclusionsWhile most outcome improvements were not sustained over the full 2 study years, findings suggest that flexible dissemination of short-term, QI programs in managed primary care can improve patient outcomes well after program termination. Models that support integrated psychotherapy and medication-based treatment strategies in primary care have the potential for relatively long-term patient benefits.DEPRESSIVE disorders and symptoms are prevalent among primary care patients, can persist for years, and are associated with decrements in functioning and wellbeing.Depression is expected to become the second leading cause of disability worldwide over the next decade.Most persons with depression receive their care in primary health care settings,yet only 50% are recognized as depressed.Because rates of appropriate treatments for depression are moderate to low in such settings,improving quality of care is essential for limiting the dysfunction associated with depression. This article addresses whether dissemination of short-term, guideline-based quality improvement (QI) interventions for depression to primary care practices improves patients' clinical outcomes and quality of life over 2 years, relative to usual care (UC).We have evaluated the impact over 1 year of disseminating 2 QI interventions for depression in diverse primary care practices, one with enhanced resources for psychotherapy and one with enhanced resources for medications. Both encouraged initiation and adherence to appropriate treatments for depression. The interventions increased patient and provider knowledge about depression and its treatment, and provided practices with enhanced resources for appropriate care for 6 to 12 months. Both approaches improved treatment rates at 6 months and to a lesser degree at 12 months.Combined, the interventions improved clinical outcomes and health-related quality of life (HRQOL) over 1 year.Few studies have examined 2-year effects on patient outcomes of short-term QI interventions for depression. Such programs could lead to prolonged improvements through several mechanisms. First, appropriate treatments could have direct long-term benefits. In clinical trials, however, treatments primarily shorten recovery time and prolong periods between episodes.After several months to a year, clinical outcomes are often equivalent for treatment and control groups.Second, greater patient or provider knowledgecould lead to higher treatment rates and better outcomes for subsequent episodes. This seems unlikely since QI programs for depression in primary care do not seem to affect long-term provider practice patterns.Third, QI programs that encourage but do not mandate treatment may result in variable rates of entry into treatment over time. The long-term benefits for a cohort could represent short-term benefits for some individuals (eg, the sickest) and later benefits for others (eg, those with subthreshold depression).In this article, we examine whether dissemination of a short-term QI intervention benefits patient health status beyond 1 year. We estimate differences between intervention and control clinic patients in accumulated outcome benefits and compare different patterns of long-term outcomes. We examine effects on clinical outcomes, as well as on HRQOL. We hypothesize that the interventions decrease clinical symptoms, but not necessarily the probability of being depressed at the end of 2 years, as depression is often recurrent and the interventions were only short-term. Because a random half of the medication-resource intervention had 6 months of additional intervention activities, we thought that this intervention might have the best outcomes at 24-month follow-up.MATERIALS AND METHODSEXPERIMENTAL DESIGN AND SAMPLEPartners in Care is a group-level, randomized controlled trial conducted in 6 diverse managed primary care organizations, 1 with 2 separate regions.All 7 had a carve-out mental health plan; 4 had in-house mental health providers; 2 had multiple provider groups; 3 had been established 15 or more years; and the percentage of patients capitated ranged from 50% to 100%. Forty-six of 48 primary care clinics and 181 of 183 clinicians participated. Clinics were matched into blocks of 3 clusters each, based on clinician specialty mix, patient demographics, and presence of on-site mental health clinicians. Within blocks, clinic clusters were randomized to UC or 1 of 2 QI improvement programs: nurse managers for medication follow-up (QI-meds) or access to trained psychotherapists (QI-therapy).Study staff screened 27 332 consecutive patient visitors in participating clinics over a 5- to 7-month period. Patients were eligible if they were positive on a depression screener and intended to use the clinic for their main care during the next 12 months. Probable depression was defined (using stem items from the World Health Organization's 12-month Composite International Diagnostic Interview [CIDI]) if the patient reported 2 weeks or more of depressed mood or loss of interest in pleasurable activities over the last year or persistent depression over the year, plus reported having at least 1 week of depression in the last 30 days. Patients were ineligible if not insured by a plan or public-pay arrangement that covered the mental health specialty group that was trained for the intervention, or if they were younger than 18 years or did not speak English or Spanish.Of the 27 332 completing the screener, 3918 were potentially eligible. Of the 2417 present to confirm insurance eligibility (some left), 241 were ineligible. Of those who read the informed consent, 70% (N = 1356) enrolled. Patients consented to participate in the study using procedures approved by RAND's Institutional Review Board and those of participating managed care organizations. The enrolled sample includes 443 UC, 424 QI-meds, and 489 QI-therapy patients.INTERVENTIONSThe intervention goal was to increase the percentage of depressed patients who receive appropriate treatment, within a feasible practice budget. Most intervention features were common across QI-meds and QI-therapy with a few features unique to each (Table 1).Table 1. Features Common and Unique to the Intervention Arms*FeaturesQI-MedsQI-TherapyPractices committed in-kind resourcesYesYesExpert leaders (primary care physicians, nurse supervisor, and mental health specialists) trained in assessment and treatment of depressionYes (psychiatrist)Yes (psychologist)Local staff trained by expert leadersYes (nurses in management of medications)Yes (therapists in CBT)Expert leader educates clinic clinicians (using lecture slides, manuals, pocket-reference cards provided by study)YesYesClinicians receive manuals on depressionYesYesStudy screens and enrolls patients at clinicsYesYesClinics given lists of study patientsYesYesNurse specialist assesses and educates enrolled patients (patients given pamphlets and videotape on depression)YesYesPrimary care clinician uses nurse specialist information to formulate treatment plan with the patientYesYesNurse specialist provides 10-minute postvisit educationYesYesNurse specialist sets up follow-up visit with primary care clinicianYesYesNurse specialist available for 6- or 12-month follow-up of medicationYesNoStudy CBT-trained therapy available at reduced copay ($0-$10 instead of $20-$30)No†Yes‡Primary clinicians available for at least 1 follow-up visit if patient is willing; more as neededYesYesLocal experts monitor intervention staffYesYes*QI indicates quality improvement; Meds, medications; CBT, cognitive behavioral therapy.†Psychotherapy available if requested at patient's regular co-payment arrangement. Nurse specialist would set up appointment with therapist.‡Brief (4-session) CBT suggested as an option for patients with minor depression; study therapists were trained in CBT by one of the authors (J.M.).QI-MedsNurse specialists were trained to present antidepressant medications and psychotherapy as equally effective treatments for depression during an initial patient assessment. The primary care clinician used the nurse specialist's assessment information to formulate a treatment plan with the patient. For patients given medication, the nurse specialist's task was to contact the patient monthly for 6 or 12 months (randomized at the patient level) and help primary care providers with management of antidepressant medications. A psychiatric expert was available for consultation to the nurse. Patients who preferred counseling were referred to the usual options for psychotherapy that were available to their practice (with regular co-pay levels). Patients could also choose no treatment and refuse to see the nurse. In the first and second 6 months of the study, 51% and 43% of QI-meds patients received some antidepressant (J.U., written communication, October 2000); 30% and 29% received at least 4 psychotherapy sessions (L.J., written communication, October 2000).QI-TherapyThe primary care clinician used the nurse specialist's initial assessment information to formulate a treatment plan with the patient. Patients whose clinician determined that psychotherapy was appropriate were referred to study cognitive behavioral therapy (CBT)–trained therapists at a reduced co-pay. The local psychotherapists provided individual and group CBTfor 12 to 16 sessions. Brief CBT (4 sessions) was suggested as an option for patients with current symptoms that did not meet criteria for major disorder. Medication treatment from their regular primary care providers was available to patients who preferred that form of treatment, but nurse specialists did not provide monthly medication management follow-up. Again, patients could choose no treatment, refuse to see the nurse, or opt to see a nonstudy therapist at usual co-pay. In the first and second 6 months of the study, 39% and 35% of QI-therapy patients received some antidepressant; 38% and 34% received at least 4 psychotherapy sessions.Common Intervention FeaturesPractices committed in-kind resources to support half of participation and intervention costs and identified a local expert team, including a primary care and a mental health provider and a nurse for training in implementing the interventions in their sites. Experts were trained in clinician education and team management. Nurses were trained to educate patients using a patient brochure and videotape, assess patient symptoms and functioning, facilitate referral, and enhance the work of the primary care provider. Seventy-three percent of patients had initial contact with the nurse specialist.The expert leaders were asked to provide clinicians with monthly or bimonthly lectures over 6 months, and were provided with teaching slides and copies of clinician manuals and pocket reminder cards on assessment and treatment of depression for clinicians. Local intervention leaders were trained to provide academic detailing as needed. The leaders were asked to hold monthly meetings to review care of study patients and intervention progress. Primary care providers were asked to meet initially with each patient to decide on an appropriate course of treatment and conduct at least 1 follow-up visit if the patient was willing. Practices could modify the approach to fit their goals and resources.UC ClinicsThe UC clinics received the Agency for Health Care Policy and Research depression practice guidelines by mail.MEASURESA computer-assisted CIDI for depression, administered by bachelor's-level graduates who had experience in word processing and fluency in speaking and writing English and/or Spanish, was given at baseline and again 24 months later. Other measures were gathered from the screener and follow-up self-administered mailed surveys. Response rates were 95% and 85% for the baseline and 24-month CIDI, and 90%, 86%, 84%, 83%, and 85% for the baseline, 6-, 12-, 18-, and 24-month mailed surveys, respectively.Disease-Specific OutcomesWe use 3 depression status measures: (1) a dichotomous indicator of having probable depression during each 6-month interval, based on a repeat of the screener measure dropping the dysthymia item; (2) a 23-item version of the Center for Epidemiologic Studies Depression Scale,included in each follow-up (this version dropped 6 items from the original scale and added items to approximate the symptoms of major depression in DSM-IV); and (3) the depression section of the full 12-month CIDI administered at the 24-month follow-up. We categorize patients by the CIDI as having 12-month major depressive or dysthymic disorder in the second follow-up year or no disorder in that year.Functioning and Well-being OutcomesWe examined the physical and mental health composite scores from the 12-Item Short-Form Health Survey (SF-12), a widely used measure of global physical and mental HRQOL.Both composites include symptom and disability items. In addition, we derived a 4-item role limitations scale (α = .67) using responses to the 12-Item Short Form Health Survey (SF-12).Overall Poor OutcomeSeveral of our outcome measures (probable depression, Center for Epidemiologic Studies Depression Scale, and the mental health composite score) can be considered alternative measures of similar constructs. We constructed a measure of overall poor outcome for each time point that classified patients as depressed if they scored in the depressed range on all 3 measures, vs 2 or fewer measures. For the mental health composite, we counted as depressed anyone who scored more than 1 SD below the general population mean of 50, while for the Center for Epidemiologic Studies Depression Scale, we used a cutoff equivalent to the standard of 16. Samejima's graded Item Response Theory Modelwas used to determine that a cut point of 20 on this modified version of the Center for Epidemiologic Studies Depression Scale is equivalent to the standard cut point of 16 for identifying probable depression.CovariatesWe measured age, sex, education, household wealth, ethnicity, marital status, a count of chronic medical conditions, depression diagnostic status at baseline, presence of comorbid anxiety disorder, and, for some analyses, an indicator of whether the baseline survey was completed within 30 days of the screener.DATA ANALYSISWe conducted intent-to-treat analyses, controlling for the covariates listed above plus global physical and mental HRQOL from the screener. Cross-sectional analyses of intervention effects on 24-month CIDI disorder status (N = 1156) are specified as individual 2-level mixed-effects linear regression models (PROC MIXED in SAS version 6.12). Individuals are nested within clinics, to account for possible intracluster correlation at the clinic level. We specified a 3-level mixed-effects linear regression model for time-trend analyses. Repeated measurements were nested within individuals, and individuals nested within clinics. For dichotomous outcomes, we used the linear probability model as an approximation to the logistic regression model,to avoid technical complications in the latter because of possible deviations from the assumed normality for the individual level random effects.For probable depression, we specified a linear time trend over the follow-up waves. The time-trend analysis includes individuals who responded to at least 1 wave of follow-up (n = 1248). For all other outcome measures, we specified a spline model, with a linear segment between baseline and the first follow-up for initial improvement, and another linear segment for the subsequent follow-ups; the 2 linear segments are specified to join at the first follow-up. We used the sample of respondents with at least 1 wave of data for the spline model (n = 1299).For each outcome, we derived standardized predictions of intervention effects. Regression parameters and each individual's actual values for all covariates other than intervention status are used to create predicted values for each patient, first as a QI-meds subject, then as a QI-therapy subject, then as a UC subject. The 3 sets of predictions are averaged across the entire sample, respectively. For time-trend models, we plotted the predicted outcomes for each intervention group over time. The gap between intervention and UC curves at each time point represents the intervention effect. To determine whether intervention effects differed by initial patient disorder status, we tested the interaction between intervention groups and disorder status.The data are weighted for the probability of nonenrollment and wave nonresponse to the eligible sample. Multiple imputation for missing items was used at each wave.The predicted outcomes across 5 randomly imputed data sets were averaged and SEs were adjusted for uncertainty caused by imputation.Significance was determined at an α of P= .05, 2-tailed test, a conservative level given that we did not expect the interventions to have a negative effect on outcomes. Our overall poor outcome measure is presented as an integrative measure to take into account multiple comparisons.RESULTSFor the sample in the time-trend analysis, about half had 12-month depressive disorder (double depression, major depression, or dysthymia only) at baseline (Table 2). Patients were receiving fairly low rates of counseling or antidepressant medications.The QI-therapy patients were slightly older than UC patients and more likely than UC and QI-meds patients to be female. College graduates were less prevalent in UC than in intervention groups. To adjust for any possible confounding factors with the intervention effects, these variables are controlled for as covariates in the analyses.Table 2. Weighted Baseline Characteristics of 1299 Intervention and Control Patients*CharacteristicUC (N = 430)QI-Meds (N = 405)QI-Therapy (N = 464)Female, %69.166.775.8Mean (SD) age, y42.2 (13.9)44.0 (14.7)44.9 (16.0)Married, %53.455.355.3Education, %<High school20.216.219.2High school33.629.326.5Some college31.231.632.6College15.022.921.6Ethnic group, %Hispanic30.825.732.0African American8.76.26.5Other minority5.46.56.8White55.061.654.6Disorder status (from CIDI), %Double depression10915Major depression394342Dysthymia only243Depressive symptoms and lifetime major depression261820Depressive symptoms without lifetime major depression232520MCS-12,† mean (SD)36.4 (10.9)36.0 (10.8)34.9 (10.4)PCS-12,‡ mean (SD)44.4 (11.6)45.2 (11.7)45.2 (11.7)Chronic conditions, No.020.722.122.7125.425.023.4219.419.220.0≥334.633.633.9Anxiety disorder, %43.043.243.4Current alcohol abuse, %§786Treatment 6 mo before baseline, %Any counseling263228Any antidepressant262729Both counseling and antidepressant131815*UC indicates usual care; QI, quality improvement; Meds, medications; CIDI, Composite International Diagnostic Interview; MCS-12, 12-item Mental Health Composite Score; and PCS-12, 12-item Physical Health Composite Score.†The MCS-12, from the 36-item Short-Form Health Survey score, is standardized to a general population mean (SD) of 50 (10).‡The PCS-12, from the 36-item Short-Form Health Survey score, is standardized to a general population mean (SD) of 50 (10).§Screener for alcohol abuse/dependence in the past month.TIME TRENDS OVER 2 YEARSThe time-trend plot for having probable depression (Figure 1) replicates previously published results, which show that both QI interventions reduced the likelihood of probable disorder in the first 12 months, relative to UC (ie, a 10-11 and 6-7 percentage point difference between each intervention and UC at 6 and 12 months, respectively). The gap between curves (the intervention effects) narrowed over time; there were no significant differences by intervention status at the 18- and 24-month follow-ups. The trajectory for UC patients was downward, indicating a slow improvement over time of 8 percentage points (51%-43%) during the 18-month follow-up period (slope significant at t32= 2.91, P= .004). The slopes of trajectories differed significantly for QI-meds vs UC (t32= 3.18, P= .003), indicating that the early intervention effect in QI-meds relative to UC diminished over time. The slope of the trajectory for QI-therapy patients is essentially flat and does not differ significantly from that of QI-meds and UC.Figure 1.Time trend for clinical outcomes (N = 1248). A high score means higher probability of depression. An asterisk indicates quality improvement (QI)–meds or QI-therapy is significantly different from usual care (P≤.05).CLINICAL DEPRESSION AT THE END OF 2 YEARSWe also examined end status after 2 years using the full 12-month CIDI assessment of depressive disorder. At the end of 2 years, UC patients had similar levels of current depressive disorder (34%) (95% confidence interval, 29-39) as QI-meds (39%) (95% confidence interval, 34-43) and QI-therapy (31%) (95% confidence interval, 27-36). The QI-meds patients had a higher rate of disorder (39%) than did QI-therapy patients (31%) (t32= 2.16, P= .04) and this difference was similar among patients with 12-month depressive disorder at baseline.FUNCTIONING AND WELL-BEING OUTCOMESThe interventions did not have any effect relative to UC on physical functioning. For emotional well-being (12-item Mental Health Composite Score), patients in the QI-therapy intervention had early (6-month) improvement, relative to UC, which was sustained over the full 2 years of the study (Figure 2). Differences between QI-therapy and UC were significant at each follow-up wave (from t32= 3.11, P= .004, at 6 months, to t32= 2.20, P= .04, at 24 months). In contrast, there were no significant differences in emotional well-being levels between UC and QI-meds patients at any period.Figure 2.Time trend for functioning and well-being outcomes (N = 1299). A high score for the 12-item Mental Health Composite Score means better emotional well-being. A high score for role limitations means more limitations (worse health). An asterisk indicates quality improvement (QI)–meds or QI-therapy is significantly different from usual care (P≤.05). A dagger indicates that QI-therapy is significantly different from QI-meds (P≤.05).Both QI-meds and QI-therapy interventions reduced role limitations, relative to UC, in the first year of the study (from t32= 2.38, P= .02, to t32= 5.49, P= .0001). The impact of QI-therapy on role limitations, relative to UC, continued into the second year of the study (t32= 3.10, P= .004, at 18 months). In addition, QI-therapy patients had fewer role limitations than QI-meds patients at 6 months (t32= 2.56, P= .01) and 12 months (t32= 2.23, P= .03).INTERVENTION EFFECTS BY DISORDER STATUSIn time-trend analyses, the intervention effects relative to UC did not differ significantly for disorder (lifetime or current) vs nondisorder (current symptoms only) patients for any of the outcomes. On 3 outcome measures (probable depression, role limitations, and physical functioning), the positive effects of QI-therapy, relative to QI-meds, were more pronounced among patients with baseline depressive disorder.OVERALL POOR OUTCOMEFor the integrative overall poor outcome measure, QI-meds patients did not differ from UC at any period (Figure 3). In contrast, QI-therapy patients had reduced overall poor outcomes of 8 percentage points, relative to UC, through 24 months (t32= 2.10, t32= 2.33, and t32= 2.22, and P= .04, P= .03, P= .03, at 6, 12, and 18 months, respectively, with borderline significance of P= .06 at 24 months). This is a fairly substantial reduction. For example, a difference of 8 percentage points relative to a 0.41 base rate in UC at 6 months translates into a relative reduction in overall poor outcome of 20%. In addition, QI-therapy patients had reduced overall poor outcomes of 7 and 10 percentage points relative to QI-meds patients at 18 (t32= 2.04, P= .05) and 24 months (t32= 2.41, P= .02). This translates into a relative reduction in overall poor outcome of 19% and 27%, respectively. After 6 months, the slope of the trajectory for QI-therapy patients differs significantly from that of QI-meds (t32= 2.11, P= .04).Figure 3.Time trend for overall poor outcome (N = 1299). A high score means poorer outcomes. An asterisk indicates quality improvement (QI)–meds or QI-therapy is significantly different from usual care (P≤.05). A dagger indicates that QI-therapy significantly is different from QI-meds (P≤.05).COMMENTPreviously, we reported that both QI approaches resulted in improved clinical outcomes and mental HRQOL over 1 year.The present results show that mental HRQOL effects persisted among patients in the QI-therapy approach, but not in the QI-meds approach, for a full 2 years, while improved role function and reduction in overall poor outcomes persisted through 18 months. Access to study resources ended for most patients after 6 months and for all patients after a year. The intervention effects in QI-therapy thus outlasted active study intervention.The QI approaches implemented in Partners in Care focused on empowering primary care practices to increase exposure of depressed patients to efficacious treatment, through providing training and additional resources. Partners in Care demonstrates a population-based model of disease management that identified a pool of patients at risk for depression who were in different stages of their disease and who were receiving various types of treatment, or no treatment. Increased exposure to appropriate treatment for all of these groups was the primary goal.Quality improvement studies most similar in purpose to Partners in Carehave found improved clinical outcomes over several months to 1 year, with a pattern consistent with clinical trials.Our findings add to this literature in 2 respects. First, benefits of QI for depression extended to functioning and quality-of-life outcomes as well as clinical outcomes. Second, the duration of benefits we observed is longer, extending well beyond the intervention period, for the QI-therapy model. In contrast, the QI-meds intervention had benefits during the intervention period, but no benefit on the overall poor outcome measure. The findings for overall poor outcome suggest that the QI-therapy intervention specifically lowered the likelihood of remaining very sick across multiple domains of clinical and quality-of-life outcomes. The QI programs mainly differed in the extent to which follow-up nurse support was available for medication management, and whether a known efficacious form of psychotherapy was available for patients preferring it. It is possible that stronger interventions yielding higher rates of initial treatment, or continuation of intervention activities, might further enhance long-term outcome improvement.This study differs considerably from a randomized clinical trial, in that opportunities for improved depression care through information and resources, rather than treatment assignment itself, was randomized. As a result, we cannot necessarily attribute prolonged benefits of the therapy-resource intervention to the provision of the psychotherapy itself. All intervention groups, including UC, had access to psychotherapy. However, only QI-therapy clinics had reduced co-pays for therapists rigorously trained in CBT. These therapists remained available to the practices after the intervention was completed. It may be that greater availability of CBT therapy, greater primary clinician confidence in referring to therapists, or greater options to meet patient preferences helped sustain benefits. In our study, 40% of patients in QI-therapy received study CBT,and some patients in QI-meds received some form of counseling or psychotherapy. While QI-therapy patients were more likely than QI-meds patients to receive several therapy sessions, QI-meds patients were more likely to receive either antidepressant medications or counseling. Thus, further work is needed to understand why the QI-therapy intervention had more sustained benefits.The strengths of our study include the clinic-level randomized design, implementation of the interventions by community-based practices, the clinical and demographic diversity of the patients, and the naturalistic practice conditions, including freedom of practices to modify interventions and of patients and clinicians to select treatments.Our study has important limitations. There was sample loss during enrollment and over time, although retention rates were higher than for most studies of this kind. The advantage of time-trend analyses is that it allows one to project trends without the need for complete data at all waves of the study, partially mitigating the impact of wave nonresponse. Data are weighted for probability of enrollment and retention in the panel. The differences between intervention groups and UC were small for some outcomes (eg, SD = 0.25 for the 12-item Mental Health Composite Score), but the clinical significance of this difference is unknown. Results are averaged over patients who have and have not improved and include patients in the intervention condition with no treatment or no use of intervention resources. Even small average differences in HRQOL could be substantial on a societal level when aggregated over many patients.CONCLUSIONSWe found that both QI approaches are feasible and effective for diverse primary care patients under naturalistic practice conditions. While antidepressants are more commonly prescribed in primary care settings as the first line of treatment, there is evidence that many patients prefer counselingand our results suggest that increasing access among primary care patients with depression to effective short-term psychotherapy may result in a more prolonged benefit to patients than does increasing appropriate medication use alone. The study findings emphasize the importance of including clinical and quality-of-life outcomes in QI studies for depression, of long-term follow-up, and of including multiple outcome measures. The investment required to implement our interventions was modest. 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MacArthur Foundation, Chicago, Ill (Dr Wells).We thank Bernadette Benjamin, MS, for excellent programming support; Mureen Carney, MS, for coordinating the data implementation plan and monitoring the study; Daniel McCaffrey, PhD, and Robert Bell, PhD, for statistical advice; and the clinicians and patients who contributed their time and efforts to this study. We also thank the following managed care organizations participating in this study, for providing access to their expertise and patients, implementing interventions, and providing in-kind resources: Allina Medical Group, Twin Cities, Minn; Patuxent Medical Group, Annapolis, Md; Humana Health Care Plans, San Antonio, Tex; MedPartners, Los Angeles, Calif; PacifiCare of Texas, San Antonio; and Valley-Wide Health Services, Alamosa, Colo; and to their associated behavioral organizations: Alamo Mental Health Group, San Antonio; San Luis Valley Mental Health/Colorado Health Networks, San Luis Valley, Colo; and GreenSpring Mental Health Services, Columbia, Md.Corresponding author: Cathy D. Sherbourne, PhD, RAND, 1700 Main St, Santa Monica, CA 90407-2138 (e-mail: [email protected]).