Geospatial Patterns in Human Papillomavirus Vaccination Uptake: Evidence from Uninsured and Publicly Insured Children in North Carolina

Abstract

Background: Human papillomavirus (HPV) vaccination coverage is far below the national objective set by Healthy People 2020. This paper explores spatial patterns in HPV vaccination uptake.Methods: Secondary data for publicly funded HPV vaccinations among age-eligible children from 2008 through 2013 from the North Carolina Immunization Registry (NCIR) were used in 2014 in an ecological analysis at the ZIP code tabulation area (ZCTA) level. We tested for spatial autocorrelation in unadjusted HPV vaccination rates using choropleth maps and Moran's I. We estimated nonspatial and spatial negative binomial models with spatially correlated random effects adjusted for demographic, economic, and healthcare variables drawn from the

Exploring variation in human papillomavirus vaccination uptake in Switzerland: a multilevel spatial analysis of a national vaccination coverage survey

“…We found three studies that used spatial autocorrelation models, published since 2011, all from limited geographical areas in the USA. The first targeted uninsured and publicly insured children in North Carolina, 17 the second used data from an internet survey from the Twin Cities Metropolitan Area of Minnesota 22 and the third was based on seven Minnesota counties. 16 In these studies, substantial spatial variation remained after adjusting for their covariates.…”

Geospatial patterns of human papillomavirus vaccine uptake in Minnesota

“… 44–46 Although the existence of these disparities is well documented, the overall average effects (ie, national level data) can mask variation across local areas. 47 48 For example, in a traditional regression analysis where minority girls live in regions with systematically different rates of HPV vaccine uptake, and the region is not controlled for, one could erroneously conclude that racial ‘disparities’ exist when in fact where people live (eg, the context of their neighbourhood) is the significant factor associated with vaccination. Thus, ignoring geography (ie, the spatial dependence of the data) may lead to incorrect inference.…”

“…However, to the best of our knowledge, this is only the second study to examine HPV vaccination at such a small areal unit. 48 Another limitation is that this study did not directly adjust for the income of participants, as this information was not available. However, accounting for spatial dependence in this study sample likely incorporates some of the variability for unmeasured factors such as income.…”

Countering vaccine hesitancy through immunization information systems, a narrative review

“…In this review, we synthetized results from 13 studies. [119][120][121][122][123][124][125][126][127][128][129][130][131] Eccles et al, using a geographical visualization method and IIS, assessed how geographical distribution of those who refuse vaccine had changed during a certain period and over time, identifying specific areas of non-vaccinated. 125 This geographical distribution has high public health impact, both to identify areas with health systems or ethnic-religious barriers and to identify areas with sanitary issues.…”

“…The geographical areas, based on ZIP code, tended to have vaccination coverage similar to their neighbors. 130,144 Geolocalization could also be useful to drive allocation of scarce governmental resources in initiatives where it is needed most. 120 Indeed, during an epidemic outbreak, vaccine campaign not only needs to first target people with a higher risk (for complications or for epidemiological reasons) but it also needs to take into account the geographical distribution of the outbreak.…”