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Figure.  Trends in Vaccination Rates Before and After Vaccine Lottery Announcements Among US States
Trends in Vaccination Rates Before and After Vaccine Lottery Announcements Among US States

The segmented regression lines show vaccination trends in the weeks before and after each state's vaccine lottery announcement (marked with the vertical black dotted line). The shaded areas represent 95% CIs. The blue line denotes vaccine rates in lottery states, whereas the orange line denotes vaccine rates in non–lottery states.

Table.  Difference in Vaccination Rates Between Lottery and Non–Lottery US States
Difference in Vaccination Rates Between Lottery and Non–Lottery US States
1.
Ohio Department of Health. Governor DeWine announces vaccine incentives, end date for health orders. Accessed July 28, 2021. https://coronavirus.ohio.gov/wps/portal/gov/covid-19/resources/news-releases-news-you-can-use/covid-19-update-05-12-21
2.
Waldrop  T, Riess  R, Alonso  M. New York and Maryland follow Ohio in creating Covid vaccine lottery. Accessed July 28, 2021. https://www.cnn.com/2021/05/20/us/covid-vaccine-lottery-new-york-maryland-ohio/index.html
3.
Walkey  AJ, Law  A, Bosch  NA.  Lottery-based incentive in Ohio and COVID-19 vaccination rates.   JAMA. 2021;326(8):766-767. Published online July 2, 2021. doi:10.1001/jama.2021.11048PubMedGoogle ScholarCrossref
4.
US Centers for Disease Control and Prevention. COVID-19 vaccinations in the United States. Accessed July 27, 2021. https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-Jurisdi/unsk-b7fc
5.
Penfold  RB, Zhang  F.  Use of interrupted time series analysis in evaluating health care quality improvements.   Acad Pediatr. 2013;13(6)(suppl):S38-S44. doi:10.1016/j.acap.2013.08.002PubMedGoogle ScholarCrossref
6.
Zhang  F, Wagner  AK, Ross-Degnan  D.  Simulation-based power calculation for designing interrupted time series analyses of health policy interventions.   J Clin Epidemiol. 2011;64(11):1252-1261. doi:10.1016/j.jclinepi.2011.02.007PubMedGoogle ScholarCrossref
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    Research Letter
    January 4, 2022

    Lottery-Based Incentives and COVID-19 Vaccination Rates in the US

    Author Affiliations
    • 1Section of Pulmonary, Allergy, Sleep, and Critical Care Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
    JAMA Intern Med. Published online January 4, 2022. doi:10.1001/jamainternmed.2021.7052

    On May 12, 2021, Ohio announced a lottery system incentivizing residents to receive COVID-19 vaccinations1; several US states subsequently introduced similar programs.2 Although analysis of vaccination rates from Ohio suggested that lottery-based incentives were not associated with increased vaccination rates,3 responses to lottery programs across other states are unclear. In this study, we assessed changes in COVID-19 vaccination rates across US states with lottery-based vaccine incentives.

    Methods

    We identified lottery states that announced cash prizes for vaccinated individuals from May 24, 2021, to July 19, 2021. States that announced lotteries outside of this period were excluded; other states were non–lottery states. This study was deemed not human participants research by the Boston University Medical Campus institutional review board. Dates were chosen to minimize confounding from vaccine expansion to adolescents (May 10, 2021). Daily rates of first COVID-19 vaccine dose administration per 100 000 persons from May 17, 2021, to July 26, 2021, were obtained from the US Centers for Disease Control and Prevention.4 Using interrupted time series analyses with segmented regression,5 we estimated the (1) immediate level change and (2) trend change in (1) daily vaccination rates in lottery states and (2) differences in daily vaccination rates between lottery and non–lottery states after vaccine lottery announcement (primary analysis), with date and state as random intercepts (eMethods in the Supplement). Sensitivity analyses included (1) using state-reported vaccination data when available and (2) a model with state as a fixed effect. A post hoc sensitivity analysis explored states with more than 3 weeks of prelottery data. We also estimated the cumulative difference in vaccines administered during the postlottery period in lottery states compared with what would have been expected from prelottery trends (eMethods and eTables 1 and 2 in the Supplement). Statistical testing was 2-tailed with an α level of .05 using R, version 4.0.2 (R Project for Statistical Computing).

    Results

    Daily first vaccination rate trends of 15 lottery and 31 non–lottery states are shown in the Figure. Among lottery states, the vaccination rate decreased before lottery announcements (–2.8 [95% CI, –4.2 to –1.4] vaccinations/100 000 people/day); following lottery announcements, vaccine administrations did not significantly increase (–0.4 [95% CI, –23.5 to 22.7] vaccinations/100 000 people), and vaccination trends did not significantly change (0.7 [95% CI, –0.9 to 2.4] vaccinations/100 000/day) compared with prelottery trends.

    Vaccination rate trends were similar between lottery and non–lottery states before lottery announcements (–0.5 [95% CI, –1.7 to 0.82] vaccinations/100 000 people/day; P = .69). After lottery announcements, there was no significant difference in vaccination level change (1.1 [95% CI, –21.7 to 23.8] vaccinations/100 000 people; P = .92) and no change in trend in vaccination rate difference (0.4 [95% CI, –1.1 to 2] vaccinations/100 000 people/day; P = .59) between lottery and non–lottery states. Sensitivity analyses with (1) state-reported data from 4 lottery states and (2) states as fixed effects showed similar results (Table). A sensitivity analysis of states with more than 3 weeks of prelottery data found no significant difference in vaccination levels and a borderline significant increase in trend after lottery announcements (P = .05). In the primary analysis (all states), the estimated cumulative difference in vaccines administered during the 28-day postlottery period in lottery states compared with what would have been expected without lottery adoption was 190 vaccines per 100 000 people (–1063 to 1484 vaccines/100 000 persons); in a post hoc sensitivity analysis (states with >3 weeks of prelottery data), the estimated change in vaccinations was 1092 per 100 000 people (–616 to 2800 vaccines/100 000 persons).

    Conclusions

    This study did not find evidence that vaccine lottery incentive programs in the US were associated with significantly increased rates of COVID-19 vaccinations. These findings expand on similar findings from the first state vaccination lottery.3 The models may be underpowered to rule out small to moderate increases in vaccination rates.6 The findings depend on the accuracy of US Centers for Disease Control and Prevention vaccine data; however, sensitivity analyses using data reported by a subsample of states that reported daily vaccination rates resulted in similar findings. Given the lack of a strong association between state lottery-based vaccine incentives and increased vaccination rates, further studies of strategies to increase vaccination rates are needed.

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    Article Information

    Accepted for Publication: October 14, 2021.

    Published Online: January 4, 2022. doi:10.1001/jamainternmed.2021.7052

    Corresponding Author: Anica C. Law, MD, MS, Boston University School of Medicine, 72 East Concord St, R304, Boston, MA 02118 (anicalaw@bu.edu).

    Author Contributions: Dr Bosch and Mr Peterson had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Law, Walkey, Bosch.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: All authors.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: All authors.

    Administrative, technical, or material support: Peterson, Walkey.

    Supervision: Law, Walkey.

    Conflict of Interest Disclosures: Dr Law reported grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Walkey reported grants from the NIH’s Community Engagement Alliance Against Covid-19 and Gilead Leveraging Informatics outside the submitted work. No other disclosures were reported.

    Funding/Support: Dr Law was funded by NIH grant K23HL 153482 and the Boston University School of Medicine Department of Medicine Career Investment Award. Dr Walkey was funded by NIH grants R01HL139751, R01HL151607, R01HL136660, and OT2HL156812-01. Dr Bosch was funded by NIH grant 1F32GM133061-01.

    Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Data Sharing Statement: The data and associated analyses from this article are available at https://github.com/nabosch/Bosch-Lab.

    References
    1.
    Ohio Department of Health. Governor DeWine announces vaccine incentives, end date for health orders. Accessed July 28, 2021. https://coronavirus.ohio.gov/wps/portal/gov/covid-19/resources/news-releases-news-you-can-use/covid-19-update-05-12-21
    2.
    Waldrop  T, Riess  R, Alonso  M. New York and Maryland follow Ohio in creating Covid vaccine lottery. Accessed July 28, 2021. https://www.cnn.com/2021/05/20/us/covid-vaccine-lottery-new-york-maryland-ohio/index.html
    3.
    Walkey  AJ, Law  A, Bosch  NA.  Lottery-based incentive in Ohio and COVID-19 vaccination rates.   JAMA. 2021;326(8):766-767. Published online July 2, 2021. doi:10.1001/jama.2021.11048PubMedGoogle ScholarCrossref
    4.
    US Centers for Disease Control and Prevention. COVID-19 vaccinations in the United States. Accessed July 27, 2021. https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-Jurisdi/unsk-b7fc
    5.
    Penfold  RB, Zhang  F.  Use of interrupted time series analysis in evaluating health care quality improvements.   Acad Pediatr. 2013;13(6)(suppl):S38-S44. doi:10.1016/j.acap.2013.08.002PubMedGoogle ScholarCrossref
    6.
    Zhang  F, Wagner  AK, Ross-Degnan  D.  Simulation-based power calculation for designing interrupted time series analyses of health policy interventions.   J Clin Epidemiol. 2011;64(11):1252-1261. doi:10.1016/j.jclinepi.2011.02.007PubMedGoogle ScholarCrossref
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