Medicine & Health

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  • (2023) Kotevski, Damian
    Thesis
    Head and neck cancer (HNC) is a complex disease with diversity in treatment modality and survival by anatomical site of origin. There is limited knowledge of the utility of oncology information systems (OIS) for the collection and reporting of HNC data during routine clinical practice to investigate prognostic factors and predict head and neck cancer-specific survival (HNCSS). Routinely collected structured data was extracted from an OIS from seven major hospitals in Australia for patients diagnosed with HNC between 2000 and 2017 and treated with definitive radiotherapy. Deaths were obtained from the National Death Index via record linkage, and HNCSS was measured from the date of diagnosis until death from HNC. Open-source machine learning and nomogram models were used to predict HNCSS and perform multivariable analysis to identify prognostic factors. Descriptive and survival analysis was used to identify inter-hospital variation in data collection, primary radiotherapy treatment, and survival. A random sample of clinical radiation oncology documents from an OIS were anonymised using a customised open-source tool (Microsoft Presidio) to evaluate the use of unstructured information for medical research. Not all user-defined fields were routinely completed and not all hospitals relied solely on the OIS, with one hospital collecting disease information in a parallel database. However, structured information collected in a standardised way with minimal missing data during routine clinical practice in an OIS can be used to predict two-year HNCSS with high performance. Evidence of inter-hospital variation in data completeness, primary radiotherapy dose, and five-year HNCSS was detected. The presence of missing data in the OIS reduced the number of predictors for prognostic analysis and prevented exploratory analysis to explain differences in survival by hospital. Lastly, the application of the anonymisation tool on unstructured clinical information sourced from an OIS demonstrated safe and secure use for some fields and a need to improve the detection and removal of person names. Data mining techniques for unstructured data or strategies to improve structured data collection should be explored to enable the development of prediction models using more complete data, patients, and variables, followed by external validation to confirm model performance.

  • (2022) Okuba, Tolesa
    Thesis
    Child growth failure (CGF) is associated with high morbidity which can predispose children to impaired cognitive development. Despite decades of interventions, a high level of CGF has persisted in Ethiopia. A likely key reason for this situation is the undetermined role of water, sanitation, and hygiene (WASH) on child growth. The overarching aim of this thesis was to examine the effects of WASH on child growth in Ethiopia. Data were extensively analysed from the Ethiopia Demographic and Health Survey (EDHS) from 2000 to 2016, and a systematic review was conducted for the thesis. Logistic regression models were fitted to assess the association of access to household WASH facilities with child growth outcomes. We conducted a systematic review and meta-analysis of WASH interventions, separately, and when combined with nutrition. To estimate trends of CGF, we used adjusted margins of predicted probabilities. Socioeconomic inequalities in CGF were estimated using a concentration curve and indices. Children with access to improved household WASH facilities were 33% less likely to have stunting. Non-randomized controlled trial studies (non-RCTs) showed an effect of WASH interventions alone on height-for-age (HAZ) (Mean difference (MD)= 0.14; 95% CI: 0.08 to 0.21) while RCTs did not. WASH alone of non-RCTs and RCTs that were delivered over 18 to 60 months indicated an effect on HAZ (MD = 0.04; 95% CI: 0.01 to 0.08). RCTs showed an effect on children < 2 years (MD = 0.07; 95% CI: 0.01 to 0.13). WASH combined with nutrition showed an effect on HAZ compared with no intervention (MD = 0.13; 95% CI: 0.08 to 0.17) and on weight-for-age (WAZ) (MD = 0.09; 95% CI: 0.05 to 0.13). There was evidence of a decline in levels of CGF between 2000 and 2016 in Ethiopia. In particular, there was a greater steady decline between 2005 and 2011 compared with other periods. Access to improved household WASH facilities mainly contributed to the reduction of CGF. Between 2000 and 2016, the concentration index increased from -0.072 to -0.139 for stunting, -0.088 to -0.131 for underweight and -0.015 to -0.050 for wasting. Key socioeconomic predictors of these inequalities were identified through decomposition analyses. Socioeconomic status of the household, geographic region, antenatal care (ANC), parental education and access to household WASH facilities largely contributed to the inequalities. Access to improved household WASH facilities was strongly associated with reduced odds of stunting. WASH interventions alone improved HAZ when delivered over 18 to 60 months and in the first 1000 days of a child’s life. The effect was stronger when WASH was combined with nutrition interventions. Integrated WASH with nutrition interventions may be an effective way of improving child growth outcomes. Improving identified predictors of socioeconomic status would most likely reduce inequalities in CGF.