Health administrative data can be a valuable tool for disease surveillance and research. Few studies have rigorously evaluated the accuracy of administrative databases for identifying rheumatoid arthritis (RA) patients. Our aim was to validate administrative data algorithms to identify RA patients in Ontario, Canada.
We performed a retrospective review of a random sample of 450 patients from 18 rheumatology clinics. Using rheumatologist-reported diagnosis as the reference standard, we tested and validated different combinations of physician billing, hospitalization, and pharmacy data.
One hundred forty-nine rheumatology patients were classified as having RA and 301 were classified as not having RA based on our reference standard definition (study RA prevalence 33%). Overall, algorithms that included physician billings had excellent sensitivity (range 94-100%). Specificity and positive predictive value (PPV) were modest to excellent and increased when algorithms included multiple physician claims or specialist claims. The addition of RA medications did not significantly improve algorithm performance. The algorithm of "(1 hospitalization RA code ever) OR (3 physician RA diagnosis codes [claims] with =1 by a specialist in a 2-year period)" had a sensitivity of 97%, specificity of 85%, PPV of 76%, and negative predictive value of 98%. Most RA patients (84%) had an RA diagnosis code present in the administrative data within ±1 year of a rheumatologist's documented diagnosis date.
We demonstrated that administrative data can be used to identify RA patients with a high degree of accuracy. RA diagnosis date and disease duration are fairly well estimated from administrative data in jurisdictions of universal health care insurance.
An expert evaluation of identifiability of cardiovascular diseases was carried out together with a clinical and functional examination of certain groups of miners of basic underground occupations at different ages and lengths of service, that showed a high incidence of cardiovascular diseases along with a low informative value of methodical approaches, indices and criteria used for their diagnosis in conducting preliminary and periodic health check-ups. To improve the quality of diagnosis of diseases of the circulatory system it is necessary that standardized methods of investigation should be employed together with consistent indices of high informative value as well as a purposive training of physicians.
The authors represented integral evaluation of sanitary and epidemiologic well-being of people residing in coal miner towns. This evaluation plays an important role in formation of general including children's, occupational and infectious morbidity.
The authors represented experience of contemporary activities of Occupational center in Rostov region, demonstrated efficiency of thorough medical examinations carried by mobile clinical and diagnostic laboratories, suggested 4-levels structure of occupational service organization.
The level and qualitative spectrum of spontaneous chromosomal aberrations (CA) were comparatively analyzed in the lymphocytes of 655 children and adolescents from the Kemerovo Region. The presented sample was divided into 3 groups according to the type of an inhabited locality: 1) small miner's towns; 2) large industrial towns; and 3) rural localities. The maximum frequency of CA (3.77 +/- 0.22%) was noted in a group of dwellers in the miner's towns; its minimum frequency (2.68 +/- 0.17%) among the rural inhabitants. The significant clastogenic effects (including the markers of radiation exposure) were detected in the miner's towns located in the southern part of the region, which represented mountain and submountain areas. At the same time, in the northern and western parts of the Kemerovo Region, the average frequencies of CA in children and adolescents did not exceed the control background values. Thus, the residence in the inhabited localities specializing in mining is not a factor of absolute toxicogenetic risk.
The utilisation of data mining methods has become common in many fields. In occupational accident analysis, however, these methods are still rarely exploited. This study applies methods of data mining (decision tree and association rules) to the Finnish national occupational accidents and diseases statistics database to analyse factors related to slipping, stumbling, and falling (SSF) accidents at work from 2006 to 2007. SSF accidents at work constitute a large proportion (22%) of all accidents at work in Finland. In addition, they are more likely to result in longer periods of incapacity for work than other workplace accidents. The most important factor influencing whether or not an accident at work is related to SSF is the specific physical activity of movement. In addition, the risk of SSF accidents at work seems to depend on the occupation and the age of the worker. The results were in line with previous research. Hence the application of data mining methods was considered successful. The results did not reveal anything unexpected though. Nevertheless, because of the capability to illustrate a large dataset and relationships between variables easily, data mining methods were seen as a useful supplementary method in analysing occupational accident data.