Systematic Review / Advancements in medicine have resulted in decreased neonatal mortality and morbidity associated with congenital anomalies (CA). Unfortunately, the advantages of these developments have been confined to high-income countries (HICs), demonstrated by the comparatively high incidence of congenital anomalies in low and low-middle-income countries (LLMICs). Evidence suggests that neonates in LLMICs encounter considerably more barriers to care than those in HICs due to a malfunctioning referral system and poorly implemented health policies that hinder the timely provision of care. As many CA are now accepted as surgically treatable, the purpose of this study was to understand what inhibits the success of a neonate from obtaining surgery in LLMICs and how that could be improved. Seven databases were searched in this systematic review to identify articles on neonates with surgically treatable CA. A total of 370 studies were identified for screening; 16 were included in the final analysis. Studies were screened and selected individually by two researchers based on the research question, and all disagreements were resolved jointly. Studies were reviewed for factors affecting the delivery of surgical treatment and were then coded as a barrier or a facilitator. Barriers to care were identified in every study, and suggested facilitators were offered by the authors, but these facilitators were not tested in the studies. This study contributes to the literature by providing additional detail on what is known about the surgical referral system in LLMICs. The study findings will inform policymakers and local governments of the realities faced by neonates and their caregivers while navigating through the surgical referral system and establish the need for alternate policy implementation strategies. / Thesis / Master of Science (MSc) / Congenital anomalies (CA) have been identified as a significant contributor to the global burden of disease, accounting for 25.3-38.8 million disability-adjusted life-years worldwide. Many CA have been classified as surgically treatable however, approximately 295,000 neonates die annually due to these conditions. As 94% of CA occur in low- and low- middle-income countries (LLMICs), this study aims to elucidate any barriers and facilitators that may influence accessing surgical treatment. A systematic review has been selected to synthesize the literature regarding what is known about accessing surgery for neonates with CA in LLMICs.

Novel clustering methods are presented and applied to financial data. First, a scan-statistics method for detecting price point clusters in financial transaction data is considered. The method is applied to Electronic Business Transfer (EBT) transaction data of the Supplemental Nutrition Assistance Program (SNAP). For a given vendor, transaction amounts are fit via maximum likelihood estimation which are then converted to the unit interval via a natural copula transformation. Next, a new Markov type relation for order statistics on the unit interval is developed. The relation is used to characterize the distribution of the minimum exceedance of all copula transformed transaction amounts above an observed order statistic. Conditional on observed order statistics, independent and asymptotically identical indicator functions are constructed and the success probably as a function of the gaps in consecutive order statistics is specified. The success probabilities are shown to be a function of the hazard rate of the transformed transaction distribution. If gaps are smaller than expected, then the corresponding indicator functions are more likely to be one. A scan statistic is then applied to the sequence of indicator functions to detect locations where too many gaps are smaller than expected. These sets of gaps are then flagged as being anomalous price point clusters. It is noted that prominent price point clusters appearing in the data may be a historical vestige of previous versions of the SNAP program involving outdated paper "food stamps". The second part of the project develops a novel clustering method whereby the time series of daily total EBT transaction amounts are clustered by periodicity. The schemeworks by normalizing the time series of daily total transaction amounts for two distinct vendors and taking daily differences in those two series. The difference series is then examined for periodicity via a novel F statistic. We find one may cluster the monthly periodicities of vendors by type of store using the F statistic, a proxy for a distance metric. This may indicate that spending preferences for SNAP benefit recipients varies by day of the month, however, this opens further questions about potential forcing mechanisms and the apparent changing appetites for spending.

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