Epidemiologic Correlates of Chronic Respiratory Infections in Adults an Urban Slum of Bhubaneswar, Odisha, India
Bibliographic record
Abstract
INTRODUCTION: Urbanization is marked by emergence of slum areas, wherein migrants from the nearby villages stay in temporary establishments for seeking avenues of employment. These dwellings lack most of the essential living conditions like safe water, healthy living conditions and access to health services. This population is also vulnerable to social evils like alcohol and tobacco addiction. KIMS, a medical college in Bhubaneswar, Odisha, India has an Urban Health Training Centre (UHTC) that caters to a slum population of nearly 12,000 population for their primary health needs. Out of the monthly average of 200 patients /day, it is observed that respiratory symptoms complaints were highest both among the young and adults. Hence the study was conceived with the following objectives. AIMS & OBJECTIVES: To assess the cumulative prevalence of Chronic Respiratory Infections and prevalence of specific respiratory symptoms in urban slum population along with a gender break up. To assess the association between the living conditions, tobacco use with the respiratory manifestations if any. METHODOLOGY: A house to house survey was done wherein all population aged 20-64years were interviewed using a pretested predesigned questionnaire adapted from questionnaire used in International Union Against Tuberculosis and Lung Diseases (IUATLD) study. Given the prevalence of nearly 4%, with 95% CI and a relative error of 20%, the optimum sample is considered to be nearly 400. For the study Chronic Respiratory Infections (CRI) was defined as reporting of 3 or more episodes of some specific common respiratory symptoms in last one year or a diagnosed case of asthma/COPD, warranting medical treatment. RESULTS: The cumulative prevalence for CRI was seen as 11.7%, dry cough being the most common complaint ie 23.25% in both sexes. Smoking (both men and women), overcrowding and history of TB were seen as positive predictors of the disease. Use of wood or kerosene in case of women gave an OR of 6.1 times for manifestation of the disease. CONCLUSION: Chronic Respiratory Illness is a major burden for slum dwellers and they should be educated against the risk factors and take appropriate precautions to safeguard against the illness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".