About
I am a Ph.D. candidate in Population, Health & Place in the Spatial Sciences Institute at the University of Southern California. My research is focused on understanding the facilitators and barriers to quality health services, and I aim to improve access for hard-to-reach and conflict-affected populations in low- and middle-income countries. I have worked with with international non-governmental organizations on health system intervention and evaluations, and my experiences living and working in Central Asia, East Africa, and West Africa have shaped my research questions.
My research emphasizes that physical accessibility and service quality are both necessary for utilization, but not independently sufficient. I study the multidimensional relationship between health services and potential clients. Where possible, I employ spatial methods to yield insights into the spatial heterogeneity of health determinants, outcomes, and their relationship.
Research areas
Health service access & utilization
This area of research focuses on quality, modes of delivery, and facilitators and barriers of health services. Despite global progress on the delivery of healthcare in under- resourced settings, some populations remain underserved. Some barriers to care are related to personal factors, such as knowledge, attitudes or personal experience with the health system. Others are related to the ability of health systems to meet the needs of the population. Finally, there are structural and geographic determinants of health risks. I have published on the ways health service delivery modes can influence quality and more effectively distribute the burden of care.
Measurement error in health surveys
This domain emphasizes the importance of quality data. Some data sources, such as the Demographic and Health Surveys, are widely used to justify and target health interventions. While these remain gold-standard data sources, recognizing potential sources of bias is important for framing inferences found in these data. My work in this area emphasizes how point estimates can be systematically biased, particularly indicators derived from sensitive questions.