@article{Goldlust_Lee_Bansal_2017, title={Assessing the distribution and drivers of vaccine hesitancy using medical claims data}, volume={9}, url={https://ojphi.org/ojs/index.php/ojphi/article/view/7590}, DOI={10.5210/ojphi.v9i1.7590}, abstractNote={<div style="left: 90px; top: 320.758px; font-size: 14.1667px; font-family: sans-serif; transform: scaleX(1.08129);" data-canvas-width="63.778333333333336">Objective</div><div style="left: 105px; top: 335.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00852);" data-canvas-width="378.81808333333333">The purpose of this study was to investigate the use of large-scale</div><div style="left: 90px; top: 352.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.983925);" data-canvas-width="395.1933333333332">medical claims data for local surveillance of under-immunization</div><div style="left: 90px; top: 369.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.01655);" data-canvas-width="394.13224999999994">for childhood infections in the United States, to develop a statistical</div><div style="left: 90px; top: 385.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02893);" data-canvas-width="394.4566666666664">framework for integrating disparate data sources on surveillance of</div><div style="left: 90px; top: 402.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.982347);" data-canvas-width="395.4766666666666">vaccination behavior, and to identify the determinants of vaccine</div><div style="left: 90px; top: 419.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00787);" data-canvas-width="110.16">hesitancy behavior.</div><div style="left: 90px; top: 450.758px; font-size: 14.1667px; font-family: sans-serif; transform: scaleX(1.11768);" data-canvas-width="82.63416666666666">Introduction</div><div style="left: 105px; top: 465.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.01135);" data-canvas-width="378.96966666666657">In the United States, surveillance of vaccine uptake for childhood</div><div style="left: 90px; top: 482.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.968408);" data-canvas-width="394.96525">infections is limited in scope and spatial resolution. The National</div><div style="left: 90px; top: 499.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02825);" data-canvas-width="394.6408333333334">Immunization Survey (NIS) - the gold standard tool for monitoring</div><div style="left: 90px; top: 515.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.984758);" data-canvas-width="395.77416666666653">vaccine uptake among children aged 19-35 months - is typically</div><div style="left: 90px; top: 532.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.984776);" data-canvas-width="297.2875">constrained to producing coarse state-level estimates.</div><div style="left: 387.491px; top: 532.751px; font-size: 8.5px; font-family: serif;">1</div><div style="left: 391.719px; top: 532.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.97055);" data-canvas-width="91.19933333333331">In recent years,</div><div style="left: 90px; top: 549.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.986901);" data-canvas-width="393.04991666666655">vaccine hesitancy (i.e., a desire to delay or refuse vaccination, despite</div><div style="left: 90px; top: 565.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.981666);" data-canvas-width="197.96925">availability of vaccination services)</div><div style="left: 287.907px; top: 566.084px; font-size: 8.5px; font-family: serif;">2</div><div style="left: 292.12px; top: 565.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.97215);" data-canvas-width="190.88166666666663">has resurged in the United States,</div><div style="left: 90px; top: 582.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02859);" data-canvas-width="394.2243333333334">challenging the maintenance of herd immunity. In December 2014,</div><div style="left: 90px; top: 599.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.9526);" data-canvas-width="394.96383333333307">foreign importation of the measles virus to Disney theme parks in</div><div style="left: 90px; top: 615.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.96404);" data-canvas-width="395.0474166666667">Orange County, California resulted in an outbreak of 111 measles</div><div style="left: 90px; top: 632.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.01347);" data-canvas-width="342.26525">cases, 45% of which were among unvaccinated individuals.</div><div style="left: 432.245px; top: 632.751px; font-size: 8.5px; font-family: serif;">3</div><div style="left: 436.495px; top: 632.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02732);" data-canvas-width="47.684999999999995">Digital</div><div style="left: 90px; top: 649.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02661);" data-canvas-width="394.55866666666674">health data offer new opportunities to study the social determinants</div><div style="left: 90px; top: 665.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.951895);" data-canvas-width="394.6238333333332">of vaccine hesitancy in the United States and identify finer spatial</div><div style="left: 90px; top: 682.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.98259);" data-canvas-width="395.4270833333333">resolution clusters of under-immunization using data with greater</div><div style="left: 90px; top: 699.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.0046);" data-canvas-width="253.3425">clinical accuracy and rationale for hesitancy.</div><div style="left: 343.291px; top: 699.417px; font-size: 8.5px; font-family: serif;">4</div><div style="left: 90px; top: 730.758px; font-size: 14.1667px; font-family: sans-serif; transform: scaleX(1.07287);" data-canvas-width="58.23916666666666">Methods</div><div style="left: 105px; top: 745.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02213);" data-canvas-width="381.84975000000003">Our U.S. medical claims data comprised monthly reports of</div><div style="left: 90px; top: 762.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.07911);" data-canvas-width="398.5338333333332">diagnosis codes for under-immunization and vaccine refusal</div><div style="left: 90px; top: 779.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02696);" data-canvas-width="394.45666666666625">(Figure 1). These claims were aggregated to five-digit zip-codes by</div><div style="left: 90px; top: 795.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.977269);" data-canvas-width="393.1391666666665">patient age-group from 2012 to 2015. Spatial generalized linear mixed</div><div style="left: 90px; top: 812.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.982494);" data-canvas-width="395.6013333333335">models were used to generate county-level maps for surveillance</div><div style="left: 90px; top: 829.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.03882);" data-canvas-width="394.73291666666637">of under-immunization and to identify the determinants of vaccine</div><div style="left: 90px; top: 845.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.993287);" data-canvas-width="393.1264166666666">hesitancy, such as income, education, household size, religious group</div><div style="left: 90px; top: 862.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.0067);" data-canvas-width="396.18500000000034">representation, and healthcare access. We developed a Bayesian</div><div style="left: 90px; top: 879.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.03392);" data-canvas-width="396.9060833333333">modeling framework that separates the observation of vaccine</div><div style="left: 90px; top: 895.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00403);" data-canvas-width="393.69875">hesitancy in our data from true underlying rates of vaccine hesitancy</div><div style="left: 90px; top: 912.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.959571);" data-canvas-width="395.0233333333332">in the community. Our model structure also enabled us to borrow</div><div style="left: 90px; top: 929.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02967);" data-canvas-width="394.3291666666668">information from neighboring counties, which improves prediction</div><div style="left: 90px; top: 945.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.987827);" data-canvas-width="393.12216666666666">of vaccine hesitancy in areas with missing or minimal data. Estimates</div><div style="left: 90px; top: 962.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.996464);" data-canvas-width="393.40408333333323">of the posterior distributions of model parameters were generated via</div><div style="left: 90px; top: 979.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00095);" data-canvas-width="266.7866666666666">Markov chain Monte Carlo (MCMC) methods.</div><div style="left: 90px; top: 1010.76px; font-size: 14.1667px; font-family: sans-serif; transform: scaleX(1.08488);" data-canvas-width="51.17">Results</div><div style="left: 105px; top: 1025.71px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02225);" data-canvas-width="379.40883333333346">Our modeling framework enabled the production of county-level</div><div style="left: 90px; top: 1042.38px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.01758);" data-canvas-width="396.6864999999998">maps of under-immunization and vaccine refusal in the United</div><div style="left: 90px; top: 1059.04px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02625);" data-canvas-width="394.23283333333353">States between 2012-2015, the identification of geographic clusters</div><div style="left: 90px; top: 1075.71px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00237);" data-canvas-width="395.8874999999999">of under-immunization, and the quantification of the association</div><div style="left: 90px; top: 1092.38px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.0176);" data-canvas-width="396.4895833333329">between various epidemiological factors and vaccination status.</div><div style="left: 90px; top: 1109.04px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.0087);" data-canvas-width="393.8319166666666">In addition, we found that our model structure enabled us to account</div><div style="left: 90px; top: 1125.71px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.01929);" data-canvas-width="394.2625833333332">for spatial variation in reporting vaccine hesitancy, which improved</div><div style="left: 90px; top: 1142.38px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00059);" data-canvas-width="85.00000000000001">our estimation.</div><div style="left: 90px; top: 1174.09px; font-size: 14.1667px; font-family: sans-serif; transform: scaleX(1.10336);" data-canvas-width="85.01416666666667">Conclusions</div><div style="left: 105px; top: 1189.04px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.993211);" data-canvas-width="380.9459166666665">Our work demonstrate the utility of using large-scale medical</div><div style="left: 90px; top: 1205.71px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.02368);" data-canvas-width="394.4566666666666">claims data to improve surveillance systems for vaccine uptake and</div><div style="left: 510px; top: 319.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.997915);" data-canvas-width="393.3615833333333">to assess the social and ecological determinants of vaccine hesitancy.</div><div style="left: 510px; top: 335.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.0641);" data-canvas-width="397.6186666666666">We describe a flexible, hierarchical modeling framework for</div><div style="left: 510px; top: 352.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00149);" data-canvas-width="395.788333333333">integrating disparate data sources, particularly for data collected</div><div style="left: 510px; top: 369.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.985044);" data-canvas-width="392.98333333333323">through different measurement processes or at different spatial scales.</div><div style="left: 510px; top: 385.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.01287);" data-canvas-width="390.0210833333333">Our findings will enhance our understanding of the causes of under-</div><div style="left: 510px; top: 402.376px; font-size: 14.1667px; font-family: serif; transform: scaleX(0.963373);" data-canvas-width="394.72583333333347">immunization, inform the design of vaccination policy, and aid in</div><div style="left: 510px; top: 419.043px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.03583);" data-canvas-width="394.42833333333357">the development of targeted public health strategies for optimizing</div><div style="left: 510px; top: 435.709px; font-size: 14.1667px; font-family: serif; transform: scaleX(1.00076);" data-canvas-width="88.11666666666666">vaccine uptake.</div><div style="left: 510px; top: 689.394px; font-size: 12.5px; font-family: serif; transform: scaleX(0.96989);" data-canvas-width="394.325">Figure 1. Instances of vaccine refusal (per 100,000 population) for United</div><div style="left: 510px; top: 702.728px; font-size: 12.5px; font-family: serif; transform: scaleX(1.00118);" data-canvas-width="295.79999999999984">States counties in 2014 as observed in medical claims data.</div>}, number={1}, journal={Online Journal of Public Health Informatics}, author={Goldlust, Sandra and Lee, Elizabeth and Bansal, Shweta}, year={2017}, month={May} }