School-Located Influenza Vaccination Reduces Community Risk for Influenza and Influenza-Like Illness Emergency Care Visits

Tran, Cuc H.; Sugimoto, Jonathan D.; Pulliam, Juliet R. C.; Ryan, Kathleen A.; Myers, Paul; Castleman, Joan B.; Doty, Randell E.; Johnson, Jackie; Stringfellow, Jim; Kovacevich, Nadia; Brew, Joe; Cheung, Lai Ling; Caron, Brad; Lipori, Gloria; Harle, Christopher A.; Alexander, Charles E.; Yang, Yang; Longini, Ira M.; Halloran, M. Elizabeth; Morris, J. Glenn; Small, Parker A.

doi:10.1371/journal.pone.0114479

Cited by 29 publications

(38 citation statements)

References 19 publications

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“…Data on frequency of medically attended influenzalike illness (ILI) for the 2014-15 influenza season were obtained from Florida's Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) for Alachua County; the 13-county Florida Department of Health Region 3 (2010 population: 2.2 million), which includes Alachua County; and the state of Florida (10,11). ILI-associated emergency department (ED) visits were defined as visits for which a chief complaint contained the word "influenza" or "flu" or the word "fever" plus "cough" and/or "sore throat."…”

Section: The Studymentioning

confidence: 99%

Hemagglutinin Gene Clade 3C.2a Influenza A(H3N2) Viruses, Alachua County, Florida, USA, 2014–15

Lednicky¹,

Iovine²,

Brew³

et al. 2016

Emerg. Infect. Dis.

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Section: The Studymentioning

confidence: 99%

Hemagglutinin Gene Clade 3C.2a Influenza A(H3N2) Viruses, Alachua County, Florida, USA, 2014–15

Lednicky¹,

Iovine²,

Brew³

et al. 2016

Emerg. Infect. Dis.

Self Cite

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“…Volunteers and community grants offset the infrastructure cost of the program. Schoolchildren ineligible for the LAIV were identified and encouraged to receive an inactivated influenza vaccine (IIV) at their medical home [8,9]. To track vaccination coverage among schoolchildren, the Alachua County Health Department (ACHD) and community pediatricians agreed to enter all influenza vaccinations into the vaccine registry.…”

Section: Methodsmentioning

confidence: 99%

“…These programs complement provider-based vaccination by increasing the opportunity for parents to have their child vaccinated at school [8]. Several SLIV programs have achieved high vaccination rates [8–10], and have also documented a reduction in absenteeism [11,12] and influenza transmission [9,13–21]. …”

Section: Introductionmentioning

confidence: 99%

Sustainability of school-located influenza vaccination programs in Florida

Tran

Brew

Johnson

et al. 2016

Vaccine

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Background School-located influenza vaccination (SLIV) programs are a promising strategy for increasing vaccination coverage among schoolchildren. However, questions of economic sustainability have dampened enthusiasm for this approach in the United States. We evaluated SLIV sustainability of a health department led, county-wide SLIV program in Alachua County, Florida. Based on Alachua’s outcome data, we modeled the sustainability of SLIV programs statewide using two different implementation costs and at different vaccination rates, reimbursement amount, and Vaccines for Children (VFC) coverage. Methods Mass vaccination clinics were conducted at 69 Alachua County schools in 2013 using VFC (for Medicaid and uninsured children) and non-VFC vaccines. Claims were processed after each clinic and submitted to insurance providers for reimbursement ($5 Medicaid and $47.04 from private insurers). We collected programmatic expenditures and volunteer hours to calculate fixed and variable costs for two different implementation costs (with or without in-kind costs included). We project program sustainability for Florida using publicly available county-specific student populations and health insurance enrollment data. Results Approximately 42% (n = 12,853) of pre-kindergarten – 12th grade students participated in the SLIV program in Alachua. Of the 13,815 doses provided, 58% (8042) were non-VFC vaccine. Total implementation cost was $14.95/dose or $7.93/dose if “in-kind” costs were not included. The program generated a net surplus of $24,221, despite losing $4.68 on every VFC dose provided to Medicaid and uninsured children. With volunteers, 99% of Florida counties would be sustainable at a 50% vaccination rate and average reimbursement amount of $3.25 VFC and $37 non-VFC. Without volunteers, 69% of counties would be sustainable at 50% vaccination rate if all VFC recipients were on Medicaid and its reimbursement increased from $5 to $10 (amount private practices receive). Conclusions and relevance Key factors that contributed to the sustainability and success of an SLIV program are: targeting privately insured children and reducing administration cost through volunteers. Counties with a high proportion of VFC eligible children may not be sustainable without subsidies at $5 Medicaid reimbursement.

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“…Consistently, several studies (Peters et al 2014;Schanzer et al 2011;Stockmann et al 2013;Timpka et al 2012) have reported earlier epidemics in school-age children than in other age groups. Furthermore, in England (Pebody et al 2015) and in Florida (Tran et al 2014), vaccination of school age children has been shown to reduce influenza incidence in all age-classes as well reducing excess respiratory mortality, stressing the role of children in influenza transmission. We have not found any statistical association between demographic covariates and epidemic onset dates.…”

Section: Impact Of Mobility Flows and Demographic Covariatesmentioning

confidence: 99%

Peer Review #1 of "Linking influenza epidemic onsets to covariates at different scales using a dynamical model (v0.3)"

Reich¹

2018

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Background. Evaluating the factors favoring the onset of influenza epidemics is a critical public health issue for surveillance, prevention and control. While past outbreaks provide important insights for understanding epidemic onsets, their statistical analysis is challenging since the impact of a factor can be viewed at different scales. Indeed, the same factor can explain why epidemics are more likely to begin i) during particular weeks of the year (global scale); ii) earlier in particular regions (spatial scale) or years (annual scale) than others and iii) earlier in some years than others within a region (spatiotemporal scale). Methods. Here, we present a statistical approach based on dynamical modeling of infectious diseases to study epidemic onsets. We propose a method to disentangle the role of covariates at different scales and use a permutation procedure to assess their significance. Epidemic data gathered from 18 French regions over 6 epidemic years were provided by the Regional Influenza Surveillance Group (GROG) sentinel network. Results. Our results failed to highlight a significant impact of mobility flows on epidemic onset dates. Absolute humidity had a significant impact, but only at the spatial scale. No link between demographic covariates and influenza epidemic onset dates could be established. Discussion. Dynamical modelling presents an interesting basis to analyze spatiotemporal variations in the outcome of epidemic onsets and how they are related to various types of covariates. The use of these models is quite complex however, due to their mathematical complexity. Furthermore, because they attempt to integrate migration processes of the virus, such models have to be much more explicit than pure statistical approaches. We discuss the relation of this approach to survival analysis, which present significant differences but may constitute an interesting alternative for non-methodologists.

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School-Located Influenza Vaccination Reduces Community Risk for Influenza and Influenza-Like Illness Emergency Care Visits

Cited by 29 publications

References 19 publications

Hemagglutinin Gene Clade 3C.2a Influenza A(H3N2) Viruses, Alachua County, Florida, USA, 2014–15

Hemagglutinin Gene Clade 3C.2a Influenza A(H3N2) Viruses, Alachua County, Florida, USA, 2014–15

Sustainability of school-located influenza vaccination programs in Florida

Peer Review #1 of "Linking influenza epidemic onsets to covariates at different scales using a dynamical model (v0.3)"

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