ABSTRACT
Background: Birth weight is probably the single most important factor that affects neonatal mortality, infant and childhood morbidity in both developed and developing countries. Objective of the study was to formulate a model for prediction of fetal weight at term based on individualized fetal growth parameters.Methods: 131 participant low risk gravidas were enrolled into the study. The participants underwent an ultrasound 7 days prior to delivery. All fetal variables of growth of a random 100 participants were incorporated in an equation derived using multiple regressions to predict birth weight at term. The new equation was then prospectively applied to another 31 pregnant women for validation. The diagnostic performance of the new regression formula was then compared to the Hadlock formula.Results: The customised birth weight formula predicted a higher accuracy with MPE±SD of 0.790±9. compared to the Hadlock formula with MPE±SD - 4.42±8.73. The new formula also explained a greater variance in birth weight of 56% compared to the Hadlock formula of 49%.Conclusions: The new model based on individualized fetal growth parameters recognizes the capacity to modulate an accurate final birth weight, thus emphasizing the need for customized population specific birth weight formulas.
ABSTRACT
Ensuring quality in healthcare today has become extremely essential to ensure adequate utilization of healthcare services andimproved outcomes. In addition to essential infrastructure in terms of safe and adequate space, knowledgeable and skilled healthworkers, and essential equipment and supply, the healthcare teams and administrators must also acquire knowledge and skills relatedto quality improvement (QI) methodologies. This review describes the role of learning platforms in teaching QI skills to the busyhealthcare teams. Through Review of the published literature, we discuss challenges of learning and applying new skills of QI in day-to-day work by healthcare teams, and how learning platforms can assist in capacity building. There is a significant body of literature onthe role of web-based teaching technology and learning platforms in medical education. Using modern communication technology,learning platforms can be established to bring together the healthcare teams, with QI experts to collaboratively learn, execute andshare their experiences in improving quality of care in their own healthcare settings.
ABSTRACT
BACKGROUND & OBJECTIVE: Information on hepatitis C virus (HCV) infection in pregnant women in India is scanty. This study was carried out to investigate the prevalence of HCV within an obstetric population in north India and to identify the various risk factors for the viral infection. METHODS: A total of 8130 pregnant women from antenatal clinic were subjected to anti-HCV testing by third generation ELISA. Anti-HCV positive seropositive women were further tested for HCVRNA, hepatitis B and HIV. The women were evaluated for the presence of following known risk factors for HCV infection. RESULTS: Eighty four (1.03%) pregnant females had HCV antibodies. Of these, 46 (54.8%) were positive for HCV-RNA, 4(4.8%) tested positive for HBsAg, while none tested positive for HIV. The mean age and parity of the anti-HCV antibody positive women was 24.36+/-3.6 yr and 0.9+/-0.8, while that of the anti-HCV antibody negative women was 24.13+/-3.6 yr and 0.8+/-0.8 respectively. Of the 84 anti-HCV positive women, 52 (61.9 %) did not have any identifiable risk factors. The risk factors variables did not have significant association with HCV positive status. INTERPRETATION & CONCLUSION: Prevalence of hepatitis C in pregnant women was 1.03 per cent. None of the known risk factors was found to be significantly associated with the HCV infection. Hence case identification and consequent management pose a particular problem and routine screening is not a viable option in our resource- poor setting.