Title:
Evaluating access to care and utilization for chronic pediatric conditions
Evaluating access to care and utilization for chronic pediatric conditions
| dc.contributor.advisor | Serban, Nicoleta | |
| dc.contributor.advisor | Swann, Julie | |
| dc.contributor.author | Garcia, Erin | |
| dc.contributor.committeeMember | Griffin, Paul | |
| dc.contributor.committeeMember | Fitzpatrick, Anne | |
| dc.contributor.committeeMember | Cummings, Janet | |
| dc.contributor.department | Industrial and Systems Engineering | |
| dc.date.accessioned | 2018-01-22T21:08:49Z | |
| dc.date.available | 2018-01-22T21:08:49Z | |
| dc.date.created | 2017-12 | |
| dc.date.issued | 2017-08-24 | |
| dc.date.submitted | December 2017 | |
| dc.date.updated | 2018-01-22T21:08:49Z | |
| dc.description.abstract | Physical and mental health can each have a huge impact on a child’s daily life, and whether or not their medical conditions are properly treated can have a lifelong impact on their overall health. In this thesis, we focus on two chronic pediatric conditions, asthma (a physical health condition) and depression (a mental health condition). We aim to evaluate selected aspects of the current state of the health care system with respect to these conditions. In Chapter 2, we first calculate the census tract level distance to receive asthma specialist care for children in fourteen states using a centralized optimization model to assign patients to providers. From these distances, we identify which states have better access to specialist care, and identify areas in which there is no access to care. For two states, we use this measure of access to care as a predictor in logistic regression models to determine the statistical significance of geographic access to asthma specialist care in estimating the rate of severe asthma outcomes (ED visits and hospitalizations). In Chapter 3 we extend the optimization model to account for visits for asthma care that are met by both primary care and asthma specialist providers and divide the children with asthma into the Medicaid and non-Medicaid population. Using CMS MAX data, we determine the capacity for pediatric asthma visits for individual providers as well as for each provider type. We then compute the census tract level distance for Medicaid and non-Medicaid children to receive primary and specialist care from the optimization model output, as well as the percent of the need in each census tract that is unmet. In Chapter 4 we establish a depression baseline for the Medicaid population using the CMS MAX data. The baseline includes treated prevalence and utilization for Medicaid children age 12-17 in twelve states from 2005 to 2012. The treated prevalence and utilization are presented at the state level and by patient stratifications within each state. In Chapter 5 we use a matching procedure to create a data set of Medicaid children that simulates paired data. Each child with depression is matched to a child without depression but who otherwise has similar characteristics. Using these pairs of children, we compare the visits, prescription fills, and Medicaid charge amounts for non-depression related health care in 2010 and 2011 in order to quantify the differences in health care utilization and expenditure between the depression and non-depression populations. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/59198 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Pediatric asthma | |
| dc.subject | Pediatric depression | |
| dc.subject | Spatial access | |
| dc.title | Evaluating access to care and utilization for chronic pediatric conditions | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Serban, Nicoleta | |
| local.contributor.corporatename | H. Milton Stewart School of Industrial and Systems Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isAdvisorOfPublication | 63115986-db70-4c06-87c4-dab394286f67 | |
| relation.isOrgUnitOfPublication | 29ad75f0-242d-49a7-9b3d-0ac88893323c | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| thesis.degree.level | Doctoral |