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1.
Indian Pediatr ; 1992 Feb; 29(2): 195-201
Article in English | IMSEAR | ID: sea-6878

ABSTRACT

Fifty seven male children between ages of 10-16 years engaged in the brassware industry at Moradabad in Northern India were studied for occupational morbidity. The finding were compared with those obtained in the children (n = 29) engaged in other ancillary units which did not involve exposure to the metal fumes and dust in their work environment. The study showed a high prevalence of respiratory morbidity in the children engaged in the main units in comparison to those employed in the ancillary units (40.3 vs 27.6%; p less than 0.05). This was associated with significantly higher prevalence of pulmonary impairment in the former group (21.0%) particularly demonstrating restrictive ventilatory abnormality (10.5%) followed by bronchial obstruction (7.0%). The high respiratory morbidity may be attributed to chronic exposure to the fumes and dust of the metals such as nickel, chromium and cadmium. The children employed in the ancillary as well as in the main units showed high prevalences of musculo-skeletal disorders (27.6 and 22.8%) which may be caused by sustained faulty posture adopted during work and physical stress.


Subject(s)
Adolescent , Child , Child Welfare , Humans , India/epidemiology , Male , Metallurgy , Occupational Diseases/epidemiology
2.
Indian J Chest Dis Allied Sci ; 1989 Apr-Jun; 31(2): 91-7
Article in English | IMSEAR | ID: sea-29548

ABSTRACT

A multivariate logistic model for measuring and comparing pneumoconiosis risk is described. In the first stage variables are screened on the basis of contributed variability via Pearson Chi Square statistic. Age, dust years and pack years so chosen as explanatory variables are fitted in the above model. The coefficients are estimated as linear discriminant function co-efficient. The model gives quite a good fit between observed and expected frequencies. Dust years discriminate maximum between the normal and pneumoconiosis group. Nearly sixty per cent of the variation is explained by these variables.


Subject(s)
Adult , Female , Humans , India/epidemiology , Logistic Models , Male , Mining , Multivariate Analysis , Risk Factors , Silicosis/epidemiology , Smoking
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