Social anxiety symptoms across diagnoses among outpatients attending a tertiary care mood and anxiety disorders service.

BACKGROUND: Social phobia is a common, persistent and disabling anxiety disorder in which co-existing depressive symptoms are common. However the prevalence of social anxiety symptoms in patients with other mood and anxiety disorders is uncertain. METHOD: In consecutive patients attending a tertiary referral mood and anxiety disorders service, depressive symptoms were assessed by the Montgomery-Asberg Depression Rating Scale (MADRS) and social anxiety symptoms by the Liebowitz Social Anxiety Scale (LSAS). The Clinical Global Impression of Severity (CGI-S) was completed following the appointment. RESULTS: 75 patients (48 women, 27 men; mean age 45.9 years) completed the study. 38 had a single diagnosis and 37 co-morbid diagnoses: 15 patients had bipolar disorder, 35 unipolar depressive disorder, 19 an anxiety disorder, and 6 other disorders. Independent samples t-tests and one-way between-subjects ANOVA revealed that the severity of social anxiety symptoms but not depressive symptoms was significantly greater in patients with co-morbid diagnoses (LSAS 73.7 vs 54.2, t(72)=2.44, p<.05; MADRS 21.9 vs 18.0, t(73)=1.76, p=.08; CGI-S 3.7 vs 3.2, t(73)=2.64, p<.05); and in anxiety disorders than in unipolar depression or bipolar disorder (respectively; LSAS 78.8 vs 59.4 vs 50.0, F(2, 65)=3.13, p=.05; MADRS 22.2 vs 19.8 vs 17.5, F(2, 66)<1, ns; CGI-S 3.9 vs 3.3 vs 3.1, F(2, 66)=5.43, p<.01). In the overall sample, correlation coefficients were MADRS and LSAS, R(2)=0.2628, p<.001; MADRS and CGI-S, R(2)=0.5863, p<.001; and LSAS and CGI-S, R(2)=0.327, p<.001. Correlations between MADRS and LSAS scores were higher in bipolar disorder (R(2)=0.4900, p<.01) than in unipolar depression (R(2)=0.376, p<.01) or anxiety disorders (R(2)=0.0041, ns). LIMITATIONS: Small size of convenience sample undergoing varying treatments within a single specialist tertiary referral centre. CONCLUSIONS: There was only a moderate correlation between depressive and social anxiety symptoms across a range of diagnoses. Depressive and social anxiety symptoms were most severe but least well correlated among tertiary care outpatients with anxiety disorders, emphasising the need for comprehensive evaluation and treatment.

Injury surveillance in Trinidad: an accident and emergency based injury surveillance system at the San Fernando General Hospital.

OBJECTIVE: Injuries are a significant cause of morbidity and mortality worldwide. Injuries disproportionately affect people living in low and middle income countries, including the Caribbean; however little is known about the epidemiology of injuries in these areas. An accident and emergency (A&E) department injury surveillance system was established at the San Fernando General Hospital, Trinidad and Tobago, to address this important data gap. METHODS: A detailed overview of the objectives, data collection methods, and inherent strengths and limitations of this surveillance system are presented, along with results of an analysis of data collected during the first three years of operations (from 2002 to 2004). RESULTS: Trained hospital staff collect a variety of injury/poisoning, demographic and clinical data on nearly 20,000 patients presenting each year with injury to the A&E Department. The total number of injuries in men was almost twice that in women. The majority of injuries were seen in those 25-44-years of age. Falls, other blunt force, stab/cut, traffic injury and poisoning represented the leading causes of injury. Nearly half of all the injuries occurred in the home, with the street/highway and work environments also accounting for an appreciable number of injuries. The majority of injuries were reported as unintentional. CONCLUSION: Injuries represent an important population health and health services issue in South Trinidad. Data from the A&E Department injury surveillance system represent an important resource to inform evidence-based health policy decisions on injury prevention and public health resource allocation.

Case-control study of radon and lung cancer in New Jersey.

Radon is known to cause lung cancer in humans; however, there remain uncertainties about the effects associated with residential exposures. This case-control study of residential radon and lung cancer was conducted in five counties in New Jersey and involved 561 cases and 740 controls. A year long alpha-track detector measurement of radon was completed for approximately 93% of all residences lived in at the time of interview (a total of 2,063). While the odds ratios (ORs) for whole data were suggestive of an increased risk for exposures >75 Bq m(-3), these associations were not statistically significant. The adjusted excess OR (EOR) per 100 Bq m(-3) was -0.13 (95% CI: -0.30 to 0.44) for males, 0.29 (95% CI: -0.12 to 1.70) for females and 0.05 (95% CI: -0.14 to 0.56) for all subjects combined. An analysis of radon effects by histological type of lung cancer showed that the OR was strongest for small/oat cell carcinomas in both males and females. There was no statistical heterogeneity of radon effects by demographic factors (age at disease occurrence, education level and type of respondent). Analysis by categories of smoking status, frequency or duration did not modify the risk estimates of radon on lung cancer. The findings of this study are consistent with an earlier population-based study of radon and lung cancer among New Jersey women, and with the North American pooling of case control radon seven studies, including the previous New Jersey study. Several uncertainties regarding radon measurements and assumptions of exposure history may have resulted in underestimation of a true exposure-response relationship.

The use of biologically based cancer risk models in radiation epidemiology.

Biologically based risk projection models for radiation carcinogenesis seek to describe the fundamental biological processes involved in neoplastic transformation of somatic cells into malignant cancer cells. A validated biologically based model, whose parameters have a direct biological interpretation, can also be used to extrapolate cancer risks to different exposure conditions with some confidence. In this article biologically based models for radiation carcinogenesis, including the two-stage clonal expansion (TSCE) model and its extensions, are reviewed. The biological and mathematical bases for such models are described, and the implications of key model parameters for cancer risk assessment examined. Specific applications of versions of the TSCE model to important epidemiological datasets are discussed, including the Colorado uranium miners' cohort; a cohort of Chinese tin miners; the lifespan cohort of atomic bomb survivors in Hiroshima and Nagasaki; and a cohort of over 200,000 workers included in the National Dose Registry (NDR) of Canada.