Brownwright, Tenley
(2020)
Using high-resolution measles vaccination coverage data improves detection of spatial heterogeneity and measles outbreak risk in the US and Africa.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Measles outbreaks burden both low- and middle-income countries (LMICs) and high-income countries (HICs), including the United States. Although these outbreaks happen locally, usually due to pockets of low vaccination coverage, and though data is often collected locally, this data is often available to researchers only at an aggregated, low-resolution level. This diminishes the strength of spatial analyses, particularly those that determine heterogeneity and clustering. We collected and used high-resolution measles coverage data and performed spatial clustering and prediction analyses, as well as a longitudinal analysis on US school vaccination exemption law, to determine whether analyses using high-resolution data could perform well compared to those using the low-resolution data typically available. With Demographic and Health Surveys (DHS) data, we mapped clusters of low vaccination coverage in a 10-country area of East Africa and determined the covariates associated with low coverage. Using the Lexis Nexis database, we examined the effect of vaccination exemption law changes by state on coverage rates in the US over a six-year period. Finally, we created a database of publicly available high-resolution school vaccination coverage data and used this to create two models predicting counties at high risk for measles outbreak in the United States. Together, these papers show that high resolution data is better at finding areas of local, low-coverage clustering as well as predicting outbreak risk. Health departments and surveys often already collect this data; improved availability of high-resolution data will create new opportunities to improve understanding of disease risk and to detect geographic communities at increased risk of outbreaks. This in turn will allow public health practitioners to improve efficiency of resource use by better targeting their efforts, decreasing the impact of possible outbreaks or preventing outbreaks altogether.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
| Creators | Email | Pitt Username | ORCID  |
|---|
| Brownwright, Tenley | teb44@pitt.edu | teb44 | |
|
| ETD Committee: |
|
| Date: |
29 January 2020 |
| Date Type: |
Publication |
| Defense Date: |
24 October 2019 |
| Approval Date: |
29 January 2020 |
| Submission Date: |
3 October 2019 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
121 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Epidemiology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
measles, vaccination, spatial epidemiology, United States, Africa, data, modeling |
| Date Deposited: |
29 Jan 2020 21:12 |
| Last Modified: |
01 Jan 2022 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/38018 |
Available Versions of this Item
-
Using high-resolution measles vaccination coverage data improves detection of spatial heterogeneity and measles outbreak risk in the US and Africa. (deposited 29 Jan 2020 21:12)
[Currently Displayed]
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}