Quantification of replication present in HIV reports and effect of patient movement between wards on MRSA infection

• Main Author: Huo, Wenwen
• Published: University of Strathclyde 2014
• Subjects: 510
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.618885

Outbreaks of widely spread infectious diseases, such as Human Immunodefficiency Virus (HIV), Severe Acute Respiratory Syndrome (SARS) and Swineflu (H1N1) and hospital acquired infections, such as Meticillin-resistant Staphylococcus Aureus (MRSA) and Clostridium Diffcile, are serious health problems which have been tackled by the World Health Organization and international health protection agencies. Various statistical analyses have contributed a remarkable effect on providing scientific evidence on which to base political decisions and infection control strategies. In this project, we focused on two infectious diseases: HIV and MRSA and the research project is divided into two separate parts. One is the quantification of replication in HIV anonymous test reports and the other is the effect of patient movement between wards on the acquisition of MRSA. The first research project is concerned with the analysis of an anonymous HIV test dataset. The data is collected as a set of birthdays and it is possible that there is repeated sampling of the same person. The aim is to quantify the amount of replication in the HIV data using a maximum likelihood technique and then give the confidence intervals for the estimated amount of replication using the bootstrap method. The data were provided by the Public Health Laboratory Service (PHLS), Colindale, London in 1994, who were interested in a statistical method to estimate multiple counting that possibly existed in the database. The data consists of individual records of the number of AIDS cases diagnosed, with birthdates from 1901 to 1973. There were two datasets provided by the PHLS, one of which contained 1,134 records and was provided in 1991. The other dataset was provided in 1994 with the sample size 17,137. An estimate of the true number of distinct individuals as well as the percentage of replication was obtained by programming the maximum likelihood calculation in the languages R and C. This technique is based upon evaluation of the probability that two records with the same birthdate represent two separate individuals as opposed to the same person reported twice. The results for the 1991 dataset showed that there were five out of sixteen birth years (i.e. 31.25% of the observed records in the 1991 dataset) with replication in the true number of distinct individuals. In the results of the 1994 dataset, the majority of the birth years (57/73) recorded the correct number of distinct individuals in the observations. The 95% confidence intervals for the estimated amount of replication were calculated by applying a parametric bootstrap method. The results show that the birth years in the 1991 dataset with non-zero estimated amount of replication (the birth years of 1931, 1934, 1935, 1943 and 1944) have comparatively wide 95% bootstrap confidence intervals, which implies higher uncertainty of the true amount of replication. A similar conclusion was obtained from the results of 95% bootstrap con dence intervals for the 1994 dataset. Comparing the results within the same birth years recorded in the 1991 dataset and the 1994 dataset, the data indicate that the confidence intervals for the 1994 dataset are mainly narrower than the corresponding ones in the 1991 dataset. The conclusion of this study illustrates the drawback of recording the HIV patients only with date of birth, which has now been improved by combining with 'Soundex' codes for the surname and gender. The second part of the project aims to estimate the impact of patient movement within a hospital on the risk of MRSA acquisition by using data from the MRSA screening admission and discharge studies in Scotland which took place in two hospitals in 2010. The data consist of an admission-only database (7,181 patients), a discharge-only database (2,432 patients) and a combined admission-discharge cohort (2,792 patients). The third database has complete information on MRSA status on admission, on discharge, as well as data on the wards the patient was in while in hospital. In order to understand the effect of potential risk factors on MRSA acquisition, a multivariate logistic regression model was constructed to analyse the effects of the number of wards a patient was in on MRSA acquisition as well as other risk factors. Receiver Operating Characteristic (ROC) curves were plotted and the individual area under the curve (AUC) was also calculated for indicating the reliability and the accuracy of the prediction of the models. Furthermore, we modelled the dynamic patient movement and assessed the effect of being in a ward with MRSA by imputing the unknown date of transfer, simulating the missing length of stay (along with the simulation envelope). The timelines of MRSA infection and carriage pressure in each ward of the two hospitals were then mapped for all patients in the three databases, imputing where necessary. Patient movement was measured as a volume indicator in terms of the frequency of ward to ward transfer and as cohabiting in the same ward. By using logistic regression within a bootstrap simulation, we estimated the odds ratio of acquisition of MRSA association with being in a ward with MRSA present, which was given by averaging the estimated effects from the fitted models, and generating the 95% confidence intervals. The results indicate that the number of wards that patients had moved through and patients being in a ward with MRSA present do not affect the risk of acquiring MRSA significantly over and above the patient level risk factors such as age and the presence of open wounds or catheters. Some further work which can be done in an MRSA screening programme is suggested as an implementation study.