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Title: A statistical analysis of low birthweight in Glasgow
Author: Murray, Barbara A.
ISNI:       0000 0001 3435 5481
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 1999
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The percentage of singleton livebirths resulting in low birthweight deliveries has remained constant in the last 20 years, with between 6 and 10% of singleton pregnancies resulting in such a delivery. Low birthweight infants have been shown to develop medical problems in infancy and childhood, such as visual impairment, lower IQs and neuromotor problems, and as such it is important to identify those pregnancies that may result in low birthweight infants. This thesis considers factors that may be related to low birthweight, and uses these factors in the construction of a model to predict the probability of a woman delivering a low birthweight infant in order to identify high risk mothers. One factor that may be thought of as being related to low birthweight is deprivation. In this thesis a new deprivation measure is proposed which updates previous work in the area by using the 1991 small area census data to create a continuous deprivation measure, based on postcode area of residence, within the Greater Glasgow Health Board. This new measure of deprivation is included in the model referred to above. As there are many possible risk factors involved in modelling the probability of delivering a low birthweight infant multiple comparisons are involved in the production of the model and it is important to produce a model that incorporates most of the relevant factors and relatively few of the unimportant factors. The first order Bonferroni bound is one method used to correct for multiple comparisons by giving an upper bound on the actual p-value. This thesis considers the second order Bonferroni bound which gives a lower bound on the p-value and, when used in conjunction with the first order bound, gives a better correction method than the first order bound alone. These two bounds are then extended into logistic regression models.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: HA Statistics