Causal symptom attributions in somatoform disorder and chronic pain

Objective

Somatoform disorders (SFD) are defined by symptoms that lack medical explanation. This study examined the type and pattern of patients' causal attributions using a new semistructured interview technique

Methods

The Causal Attributions Interview allows to assess and weigh 15 common explanations of physical symptoms. Attributions given by 79 patients with SFD were compared with those obtained from 187 chronic pain patients.

Results

The test-retest reliabilities of the interview-elicited attributions were satisfactory to good. SFD patients attributed most of their symptoms to mental/emotional problems (46.9%) and somatic disease (41.1%), while the pain sample preferred physical overloading/exhaustion (56.1%), daily hastiness/time pressure (41.7%), somatic disease (39.6%), and weather influence (39.0%). On average, SFD patients chose 2.57 and pain patients 3.86 different attributions for each symptom. These numbers were substantially larger than those of initial spontaneous attributions. Correspondence analysis revealed underlying dimensions with three groups labeled “environmental,” somatic,” and “psychological/stress.” While pure environmental attributions were rare (1.1%), somatic factors were chosen for 28.3% of the symptoms, psychological/stress for 22.1%, and the combination of both for 25.6%. Approximately 10% were attributed in a multicausal sense to all three groups. Depression was found to correlate positively with psychological/stress and negatively with somatic attributions.

Conclusion

The results do not support the perspective that SFDs generally result from poor acknowledgement of emotional factors. SFD and chronic pain showed distinguishable attributional patterns.     

The impact of different causal models on estimated effects of disinfection by-products on preterm birth

Background

Previous epidemiologic studies of preterm birth and drinking water disinfection by-products (DBP) reported inconsistent results especially for third trimester exposures. These inconsistencies may have been due to differences in the underlying causal model assumed and methodological issues, including the method of analysis (cumulative vs. density-sampling of controls and matching on gestational age) and appropriate control of confounding.

Methods

We use data from previously published research to illustrate how different causal models, methods of analysis, and the choice of covariates to control impact results.

Results

Exposure at high measured TTHM levels (≥60 μg/l) during the last trimester – with cumulatively sampled controls – corresponded to negative effect estimates when comparing preterm to term births and averaging exposure over different length periods. In contrast, density-sampling of controls with an exposure truncated at 36 weeks gestation and adjustment for possible confounding by exposures experienced in prior trimesters led to moderate changes in risk at the highest level of exposure averaged over the four weeks prior to birth.

Conclusions

We recommend that future research on an exposure to DBPs and risk of preterm birth explore the sensitivity of their findings to different model specifications, specifically: (1) cumulative vs. density-sampling of controls when evaluating third trimester or whole pregnancy exposures, taking into account exposure-averaging length; (2) short-term peak exposures vs. long-term exposures; and (3) adjustment for exposure during prior pregnancy periods when evaluating later trimester exposures to account for possible ‘priming’ effects of early exposures.     

The impact of causal attributions on diagnosis and successful referral of depressed patients in primary care

Despite growing concerns of over-treatment, the under-diagnosis and undertreatment of major depressive disorders is still prevalent. Causal attributions are thought to be involved in help seeking behavior, time to diagnosis and the chance for successful referral. Yet, little is known about the extent to which these processes are influenced by causal attributions. 120 patients, involved in the nationwide second Dutch National Survey of General Practice (Schellevis, Westert, & Bakker, 2005), with a current DSM-IV diagnosis of depression, severe depression or with a depression lasting over six months, completed a causal attributions inventory. Demographic and clinical data from the survey, and causal attribution scores were used as independent variables in association with getting a diagnosis of depression from the general practitioner, or being in treatment by a mental health care provider for more than 3 sessions. Causal attributions related to intrapsychic fears were significantly associated with getting a diagnosis of depression and successful referral. Causal attributions related to childhood were also positively associated with successful referral. In association models derived from all the demographic and clinical data available in the survey, causal attributions substantially contributed to the explained variance, 55% and 39% respectively. The findings suggest causal attributions have a statistically significant impact on time to diagnosis and the chance of successful referral. Using the Causal Attribution Inventory with high-risk patients in primary care might enhance the chance of detection and successful referral of depressed patients. Schellevis, F. G., Westert, G. P., & De Bakker, D. H. (2005). The actual role of general practice in the dutch health-care system. Results of the second dutch national survey of general practice. Medizinische Klinik (Munich), 100(10), 656-661.     

Gene expression meta-analysis identifies chromosomal regions and candidate genes involved in breast cancer metastasis

Breast cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth whereas others are causal for the various steps of metastasis. In a fraction of tumors deregulation of the same genes might be caused by epigenetic modulations, point mutations or the influence of other genes. We have investigated the relation of gene expression and chromosomal position, using eight datasets including more than 1200 breast tumors, to identify chromosomal regions and candidate genes possibly causal for breast cancer metastasis. By use of “Gene Set Enrichment Analysis” we have ranked chromosomal regions according to their relation to metastasis. Overrepresentation analysis identified regions with increased expression for chromosome 1q41–42, 8q24, 12q14, 16q22, 16q24, 17q12–21.2, 17q21–23, 17q25, 20q11, and 20q13 among metastasizing tumors and reduced gene expression at 1p31–21, 8p22–21, and 14q24. By analysis of genes with extremely imbalanced expression in these regions we identified DIRAS3 at 1p31, PSD3, LPL, EPHX2 at 8p21–22, and FOS at 14q24 as candidate metastasis suppressor genes. Potential metastasis promoting genes includes RECQL4 at 8q24, PRMT7 at 16q22, GINS2 at 16q24, and AURKA at 20q13. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Authors’ contributions  M. Thommassen and T. A. Kruse designed the study, Q. Tan developed methods for statistical analysis and M. Thommassen performed data analysis. An erratum to this article can be found at     

Maternal Smoking During Pregnancy and Birthweight: A Propensity Score Matching Approach

There is accumulated evidence of the existence of a deleterious effect of smoking on birth outcomes. Understanding the effect of smoking on pregnancy is a critical issue because of the public policy implications for dissuading maternal smoking. We explore this issue by using the propensity score method and compare that with parametric estimators. First we estimate the treatment effect of smoking during pregnancy on different birth outcomes, by race. Then, we extend the method to the case of the multi-treatment “intensity of smoking.” The deleterious effect of smoking is found robust to the different estimation methods used.      

68例产后出血相关因素的临床分析及护理对策

目的 探讨产后出血发生的原因,制定防治及护理对策.方法 选取2008年10月～2012年11月发生产后出血的68例患者的临床资料进行回顾性分析.结果 导致68例患者产后出血的主要原因依次为：宫缩乏力47例（69.12%）、胎盘因素13例（19.12%）、软产道裂伤5例（7.35%）和凝血功能障碍3例（4.41%）.结论 加强孕产妇产前保健,对引起产后出血高危孕妇,积极采用临床、护理干预,可降低产后出血率.     

Role and limitations of epidemiology in establishing a causal association

Cancer risk assessment is one of the most visible and controversial endeavors of epidemiology. Epidemiologic approaches are among the most influential of all disciplines that inform policy decisions to reduce cancer risk. The adoption of epidemiologic reasoning to define causal criteria beyond the realm of mechanistic concepts of cause-effect relationships in disease etiology has placed greater reliance on controlled observations of cancer risk as a function of putative exposures in populations. The advent of molecular epidemiology further expanded the field to allow more accurate exposure assessment, improved understanding of intermediate endpoints, and enhanced risk prediction by incorporating the knowledge on genetic susceptibility. We examine herein the role and limitations of epidemiology as a discipline concerned with the identification of carcinogens in the physical, chemical, and biological environment. We reviewed two examples of the application of epidemiologic approaches to aid in the discovery of the causative factors of two very important malignant diseases worldwide, stomach and cervical cancers. Both examples serve as paradigms of successful cooperation between epidemiologists and laboratory scientists in the pursuit of the understanding of cancer etiology.     

Chasing change talk: The clinician's role in evoking client language about change

Client “change talk,” or language in favor of changing a target behavior, is a hypothesized active ingredient of motivational interviewing that can predict actual behavioral change. This study isolated and manipulated change talk in a context resembling a psychotherapeutic encounter, comparing its prevalence in two conditions: change talk evocation (CT) and functional analysis (FA). Using a single-baseline (ABAB) design, clinicians alternated between CT and FA, consequating change talk only in the CT condition. Clinicians were 9 clinical psychology graduate students, and clients were 47 undergraduates with concerns about drinking. The hypothesis that greater Percentage Change Talk would be observed in CT than in FA was supported, t(46) = 6.561, p < .001, d = 1.19. A rationale for the development of a behavioral rating system to evaluate clinicians' proficiency in recognizing, responding to, and evoking client change talk is presented.     

Assumptions when Analyzing Randomized Experiments with Noncompliance and Missing Outcomes

Randomized trials often suffer from a number of complications, notably noncompliance with assigned treatment and missing outcomes. In this paper, basic complications and associated assumptions are catalogued and discussed. Both noncompliance and missing outcomes are posttreatment variables, and therefore adjusting for noncompliance or missing outcomes requires careful analysis. The approach we follow differs from that based on standard structural econometrics models where non intuitive distributional and statistical assumptions are usually introduced to identify parameters of interest from observed data. The assumptions we discuss are instead scientific and foster understanding of how identification from observed data is achieved, regardless of the approach used for inference. We illustrate such assumptions in the case of compliance behavior, assumed to be dichotomous (all or none), and response behavior, also assumed to be dichotomous (respondent or nonrespondent). Starting from simple examples, we review and propose different sets of exclusion restrictions, which limit the number of potential outcomes, and discuss which assumptions seem to be more appropriate for different settings. An important lesson is that there are no universally appropriate assumptions. Different scientific settings support different assumptions as appropriate.