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Biomedical subjects

D C Reddy

Publications and source records attributed to D C Reddy.

At least 37 records · Page 2Linked to original sources
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Descriptive epidemiology of acute respiratory infections among under five children in an urban slum area.

A study was conducted in Sunderpur, Varanasi to study the magnitude of the problem of acute Respiratory Infections among under five children in an urban slum and the clinical profile of it in order to understand the pattern of disease presentation for identifying methods of early diagnosis and timely intervention. 150 under five children were selected by stratified random sampling method and were observed for 52 weeks at weekly interval to record the illnesses. In total 661 episodes were observed in 5623 child-weeks of observation giving an episode rate of 6.11 per child per year. ARI accounted for 67% of all morbidities. Mean duration of all the episodes taken together was 8.15 + 5.44 days. Majority of the episodes (88.96%) were confined to the Upper Respiratory Tract only. Most commonly occurring clinical features were rhinorrhea, nasal stuffiness and cough. 61.4% of all the episodes terminated within seven days, and only 26.2% continued for two weeks.

Absenteeism↗
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Extents of contamination of top milk and their determinants in an urban slum of Varanasi, India.

A community based study to examine the extent of contamination of supplementary milk feeds of 149 children aged 6-24 months was conducted in a semi urban slum of Varanasi, India. Out of 201 children, 149 top milk samples were collected directly from the feeding utensils into a sterile vial and subjected to bacteriological analysis. Overall, 53.7% of milk samples were contaminated by bacteria and among them 16.1% were potentially enteropathogenic in nature. The distribution of pathogens was E. coli (13.4%), Klebsiella spp (5.4%), Enterobacter spp. (5.4%), Pseudomonas aeruginosa (4.7%), Shigella spp. (2.7%) and others (22.1%). The rate of contamination was significantly higher (p < 0.001) in lower income group (73.4%), lower caste (69.6%) and in case of illiterate mothers (69.3%). Bivariate analysis indicated that wherever the afore mentioned parameters of hygiene were adverse, isolation rates increased multifoldely. Multiple logistic regression analysis indicated that the probability of a milk sample being positive for bacterial contamination was higher by 20 times when unclean utensils were used, by 3 times if mothers hands were dirty and by 2.8 time if the mothers were illiterate. The odds of contamination by pathogens was 25.7 times higher if the feeding utensils were dirty.

Animals↗
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Estimating true burden of disease detected by screening tests of varying validity.

Timely and accurate information on disease load is essential for planning health programs. Unfortunately, complexity, cost and need of skilled personnel limit the use of screening tools of high validity in developing countries. The disease load estimated with tools of low validity differs considerably from true disease load, particularly for diseases of extreme levels of prevalence/incidence. A tool of 70% sensitivity and specificity may yield a prevalence/incidence rate of 34% (CI: 32.23-35.67%) for a disease whose true rate is only 10.0% (CI: 8.94-11.06%). We proposed a procedure to derive the true estimate in such cases, based on the concepts of sensitivity and specificity of a diagnostic/screening test. It is applied on two sets of real data--one pertaining to incidence rate of low birth weight (LBW) and the other to prevalence rate of obesity--where multiple screening tests of varying validity were used to estimate the magnitude. Different screening tests yielded widely varying incidence/prevalence rates of LBW/obesity. The prevalence/incidence rates derived by using the proposed estimation procedure are similar and close to the true estimate obtained by screening tests considered as gold standard. Further, sample size determined on the basis of the results of a tool of low validity may be either larger or smaller than the required sample size. Estimation of true disease load enables determination of correct sample size, thus improving the precision of the estimate and, in some instances, reducing the cost of investigation.

Cost of Illness↗