BACKGROUND: To study mortality rate and causes of death among all hospitalized opioid addicts treated for self-poisoning or admitted for voluntary detoxification in Oslo between 1980 and 1981, and to compare their mortality to that of the general population. METHODS: A prospective cohort study was conducted on 185 opioid addicts from all medical departments in Oslo who were treated for either self-poisoning (n = 93, 1980), voluntary detoxification (n = 75, 1980/1981) or both (n = 17). Their median age was 24 years; with a range from 16 to 41, and 53% were males. All deaths that had occurred by the end of 2000 were identified from the Central Population Register. Causes of death were obtained from Statistics Norway. Standardized mortality ratios (SMRs) were computed for mortality, in general, and in particular, for different causes of death. RESULTS: During a period of 20 years, 70 opioid addicts died (37.8%), with a standardized mortality ratio (SMR) equal to 23.6 (95% CI, 18.7-29.9). The SMR remained high during the whole period, ranging from 32.4 in the first five-year period, to 13.4 in the last five-year period. There were no significant differences in SMR between self-poisonings and those admitted for voluntarily detoxification. The registered causes of death were accidents (11.4%), suicide (7.1%), cancer (4.3%), cardiovascular disease (2.9%), other violent deaths (2.9%), other diseases (71.4%). Among the 50 deaths classified as other diseases, the category "drug dependence" was listed in the vast majority of cases (37 deaths, 52.9% of the total). SMRs increased significantly for all causes of death, with the other diseases group having the highest SMR; 65.8 (95% CI, 49.9-86.9). The SMR was 5.4 (95% CI, 1.3-21.5) for cardiovascular diseases, and 4.3 (95% CI, 1.4-13.5) for cancer. The SMR was 13.2 (95% CI, 6.6-26.4) for accidents, 10.7 (95% CI, 4.5-25.8) for suicides, and 28.6 (95% CI, 7.1-114.4) for other violent deaths. CONCLUSION: The risk of death among opioid addicts was significantly higher for all causes of death compared with the general population, implying a poor prognosis over a 20-year period for this young patient group.
The study is based on a sample of 965 children living in Oulu region (Finland), who were monitored for acute middle ear infections from birth to the age of two years. We introduce a nonparametrically defined intensity model for ear infections, which involves both fixed and time dependent covariates, such as calendar time, current age, length of breast-feeding time until present, or current type of day care. Unmeasured heterogeneity, which manifests itself in frequent infections in some children and rare in others and which cannot be explained in terms of the known covariates, is modelled by using individual frailty parameters. A Bayesian approach is proposed to solve the inferential problem. The numerical work is carried out by Monte Carlo integration (Metropolis-Hastings algorithm).
From a total population of 10,766 Swedish 50- to 59-year-old women, 6,917 (64.2%) participated in the Women's Health in Lund Area (WHILA) study, and among them 6,623 (95.7%) answered the questions on alcohol consumption. One out of 4 women (26.0%) consumed no alcohol in an ordinary week (non-drinkers), 57.4% consumed not more than 83 g alcohol, 12.5% consumed 84-167 g and 4.2% consumed 168 g or more. The weekly drinkers had a median consumption of 40.0 g alcohol (range 2.5-1,036.0) and the main sort of alcohol was wine. Comparing the four drinking groups, most differences occurred between the non-drinking and the weekly drinking women. The non-drinkers had lower socio-demographic status, poorer health and more symptoms, especially physical symptoms. In a multivariate logistic regression analysis, most associations between non-drinking and lower socio-demographic status remained.
The Mohawk Nation at Akwesasne is a Native American community located along the St. Lawrence River in New York State, Ontario, and Quebec. One component of a multiphase human health study was to assess the impact of different pathways of human exposure resulting from the off-site migration of polychlorinated biphenyl (PCB) contamination in this area. This paper illustrates how mapped residential information and environmental sampling data can be united to assist in exposure assessment for epidemiologic studies using geographic information system (GIS) technology and statistical methods. A proportional sampling scheme was developed to collect 119 surface soils. Using a method of cross validation, the average estimated error can be computed and the best estimator can be selected. Seven spatial methods were examined to estimate surface soil PCB concentrations; the lowest relative mean error was 0.42% for Inverse 3 nearest neighbor weighted according to the inverse distance, and the highest relative mean error was 4.4% for Voronoi polygons. Residual plots indicated that all methods performed well except near some of the sampling points that formed the outer boundaries of the sampling distribution.
We investigated the self-report hypertension variables in the CSHA, recorded in the screening questionnaire and the Self-Administered Risk Factor (SARF) questionnaire. The two questions showed high agreement (phi coefficient 0.83). Each was modestly but significantly associated with other simultaneous reports of heart disease and stroke, and with subsequent mortality. Only the SARF asked questions about treatment; controlling for treatment effects, five-year survival was longest among those with no hypertension and no treatment (mean survival time 1,645 days; 95% CI 1,632 to 1,658), and shortest for those with no reported hypertension who were receiving "antihypertensive" medications presumably prescribed for other cardiovascular disease (mean survival time 1,496 days; 95% CI 1,457 to 1,535). The SARF questions incorporating high blood pressure and treatment appear preferable to assess the risks associated with hypertension.
Two suburban communities in western Canada, with a combined population of 52,000, were affected by a false allegation of increased cancer risk. In 1986, a cancer research agency responded to community concern by conducting a study of cancer incidence (1979 to 1983) and reported elevation on the order of 125% of expected for most sites. Reanalysis of these data several months later revealed an error in the population figure used to calculate the rates. Correction brought the rates into line with Alberta as a whole and comparable to other communities surrounding Edmonton. National media reported the cancer excess but did not report the correction. A Joint Advisory Committee convened at the time by the Minister responsible was a valuable resource for public education. This case study may be useful in the instruction of students and as an example of clerical errors that can indirectly affect an otherwise useful study. Residents of two nearby communities in northern Alberta became concerned about an apparently elevated rate of cancer among adults in their area. Many speculated on an association with the concentration of refineries in eastern Edmonton and petrochemical facilities outside the city in one of the communities. In 1986, the responsible provincial agency conducted a preliminary study of cancer incidence in the area compared to Alberta as a whole. The findings were summarized in a draft document which became widely circulated, although it was never intended to be the definitive report. It was "leaked" from a high government office and became the basis for numerous news stories. A reanalysis of these data revealed that the population figure used to calculate the combined cancer rates for the two communities was valid only for one of them and not the two combined. Correction of this mistake brought the calculated rates into line with those for Alberta as a whole. The original error was shown to be one of interpreting a confusing set of tables that lacked specific instructions and is not likely to be repeated. The original study is now invalidated. Reference to its findings should be made only as it has affected recent local history and public concern. There is certainly no evidence for a serious chemical threat to the health of residents of the area today in the present study and no suggestion of an occupational or environmental factor at work in the past.
Certain diseases and symptoms carry an overrepresentation of cancer. To be able to measure the strength of such an association it is necessary to be able to predict cancer development in the group being observed. A computer program for computers running under the MS DOS operating system has been developed for this purpose. The program is written in the CLIPPER programming language. The estimates are based on incidence and prevalence data from the Swedish Cancer Registry for the years 1958 to 1986. The program also computes confidence intervals based on the Poisson distribution. The results can be printed out or exported to other programs for further analysis.
The objective of this paper is to review capture-recapture (CR) methodology and its usefulness in epidemiology. Capture-recapture is an established and well-accepted sampling tool in wildlife studies, and it has been proposed as a cost-effective demographic technique for conducting censuses. However, the application of CR in the field of epidemiology requires consideration of relevant factors such as the nature of the condition under surveillance, its case definition, patient characteristics, reporting source and propensity for misdiagnosis and underdiagnosis. The use of CR in epidemiology has expanded over the last 10 years and no doubt will continue to be adopted. Although it has a role in public health surveillance, a more traditional approach to disease monitoring seems more advantageous in certain instances.
In computer supported outbreak detection, a statistical method is applied to a collection of cases to detect any excess cases for a particular disease. Whether a detected aberration is a true outbreak is decided by a human expert. We present a technical framework designed and implemented at the Swedish Institute for Infectious Disease Control for computer supported outbreak detection, where a database of case reports for a large number of infectious diseases can be processed using one or more statistical methods selected by the user.
Based on case information, such as diagnosis and date, different statistical algorithms for detecting outbreaks can be applied, both on the disease level and the subtype level. The parameter settings for the algorithms can be configured independently for different diagnoses using the provided graphical interface. Input generators and output parsers are also provided for all supported algorithms. If an outbreak signal is detected, an email notification is sent to the persons listed as receivers for that particular disease.
The framework is available as open source software, licensed under GNU General Public License Version 3. By making the code open source, we wish to encourage others to contribute to the future development of computer supported outbreak detection systems, and in particular to the development of the CASE framework.