| dc.creator |
Cadena, Jose |
|
| dc.creator |
Falcone, David |
|
| dc.creator |
Marathe, Achla |
|
| dc.creator |
Vullikanti, Anil |
|
| dc.date |
2019-02-13T14:08:50Z |
|
| dc.date |
2019-02-13T14:08:50Z |
|
| dc.date |
2019-02-04 |
|
| dc.date |
2019-02-10T04:20:34Z |
|
| dc.date.accessioned |
2023-03-01T18:53:32Z |
|
| dc.date.available |
2023-03-01T18:53:32Z |
|
| dc.identifier |
BMC Medical Informatics and Decision Making. 2019 Feb 04;19(1):28 |
|
| dc.identifier |
http://hdl.handle.net/10919/87567 |
|
| dc.identifier |
https://doi.org/10.1186/s12911-018-0706-7 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/281735 |
|
| dc.description |
Background
Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public health risk. Prior methods have used publicly-available school immunization data for analysis (except for a few, which use private healthcare patient records). School immunization data has limited demographic information—as a result, such analyses are not able to provide demographic characteristics of significant clusters. Further, the resolution of the clusters identified by prior methods is limited since they are typically restricted to disks or well-rounded shapes.
Methods
We use realistic population models for Minnesota (MN) and Washington (WA) state, which provide a model of activities for all individuals in the population. We combine this with school level immunization data for these two states, to estimate vaccine coverage at the level of census block groups. A scan statistic method defined on networks is used for finding significant clusters of under-immunized block groups, without any restrictions on shape. Further we provide the demographic characteristics of these clusters.
Results
We find 2 significant under-vaccinated clusters in MN and 3 in WA. These are very irregular in shape, in contrast to the circular disks reported in prior work, which rely on the SatScan approach. Some of the clusters found by our method are not contained in those computed using SatScan, a state-of-the-art software tool used in similar studies in other states.
Conclusions
The emergence of under-immunized clusters is a growing concern for public health agencies because they can act as reservoirs of infection and increase the risk of infection into the wider population. Higher resolution clusters computed using our network based approach and population models provide new insights on the structure and characteristics of such clusters and enable targeted interventions. |
|
| dc.description |
Published version |
|
| dc.format |
application/pdf |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.rights |
Creative Commons Attribution 4.0 International |
|
| dc.rights |
http://creativecommons.org/licenses/by/4.0/ |
|
| dc.rights |
The Author(s) |
|
| dc.title |
Discovery of under immunized spatial clusters using network scan statistics |
|
| dc.title |
BMC Medical Informatics and Decision Making |
|
| dc.type |
Article - Refereed |
|
| dc.type |
Text |
|