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Our data suggest that this is what occurred in the 4 pediatric practices during the vaccine shortage. These practices immunized few healthy children after the ACIP priority-group announcement.


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This study has several limitations. Because it was observational rather than experimental, we cannot definitively attribute the practices' success in following priority group recommendations to the practices' system.

Besides the practices' ability to identify and recall children with HRCs, other factors such as media coverage of illness and deaths due to influenza, parents' attitudes regarding influenza vaccine, and the timing or the severity of the influenza season may have influenced the immunization rates that we observed.

Finally, the study sites were metropolitan private practices serving a mostly privately insured and middle— to high—socioeconomic status population; therefore, our results may not be generalizable to other populations. Our study also has several strengths, which include our ability to compare immunization delivery between seasons by analyzing data from the same practices and our ability to assess immunization rates by using data from the IIS and a shared billing database.

Our results show that practices with a system to identify and recall children with HRCs were able to follow the ACIP priority group recommendations during the influenza vaccine shortage.

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An electronic billing database or an electronic medical record is an important tool to efficiently identify and quantify children with HRCs in a practice. The billing or medical record databases can be linked to electronic IISs so that children's medical records can be flagged and the children can be recalled. Practices can also use data from databases and IISs to rapidly assess their performance on immunization measures and to help with quality improvement efforts.

Electronic systems integrating billing databases, medical records, and immunization information may prove to be an extremely valuable tool that enables providers to identify and recall their highest-priority patients quickly in the face of a vaccine shortage or an outbreak situation. Correspondence: Mandy A.

Author Contributions: The authors 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. Study concept and design : Allison, Daley, and Kempe.


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Acquisition of data : Daley, Barrow, and Kempe. Drafting of the manuscript : Allison. Statistical analysis : Crane, Beaty, and Allred. Obtained funding : Daley and Kempe. Administrative, technical, and material support : Daley, Barrow, and Kempe. Study supervision : Daley and Kempe. Disclaimer: The contents of this report are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the AAMC.

Arch Pediatr Adolesc Med. All Rights Reserved. Save Preferences. Privacy Policy Terms of Use. Twitter Facebook Email. This Issue. Citations View Metrics. Mandy A. Study setting and population. Study population.

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Data sources. Statistical analysis. View Large Download. Accessed October 18, Identification and recall of children with chronic medical conditions for influenza vaccination. Implementation of universal influenza immunization recommendations for healthy young children: results of a randomized, controlled trial with registry-based recall.

Geneva, Switzerland World Health Organization;. Comparison of pediatricians' measured and reported adherence to national guidelines for PCV7 for healthy children and measured adherence for high-risk children during the national shortages. Influenza among healthy young children: changes in parental attitudes and predictors of immunization during the to influenza season.

Misperceptions about influenza vaccination among parents of healthy young children. Learn more. Sign in to access your subscriptions Sign in to your personal account. Create a free personal account to download free article PDFs, sign up for alerts, and more. Purchase access Subscribe to the journal. Sign in to download free article PDFs Sign in to access your subscriptions Sign in to your personal account. Get free access to newly published articles Create a personal account or sign in to: Register for email alerts with links to free full-text articles Access PDFs of free articles Manage your interests Save searches and receive search alerts.


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Get free access to newly published articles. Create a personal account to register for email alerts with links to free full-text articles. Sign in to save your search Sign in to your personal account. Create a free personal account to access your subscriptions, sign up for alerts, and more. Purchase access Subscribe now. For simulations, the baseline transmission parameter values were: basic reproduction number R 0 of 1.

Demographic and behavioural data used to derive age-specific average number of contacts per week was obtained for the GVRD [ 39 , 42 ]. We set the start time of the epidemic to September 6th, which corresponds to the start of school in Vancouver. As of August 31st there had been only a total of laboratory-confirmed cases of pH1N1 influenza since April of in British Columbia [ 48 ]. We assumed that the number of actual currently infected cases on September 6th was We then distributed them through the age and behaviour compartments randomly with probably weighted by population fraction and contact rate in each compartment.

Each result we show in the following represents the mean of simulations starting with different random initial conditions. We further assume that the rest of the population is completely susceptible. Although there was pH1N1 activity in the GVRD in Spring which would result in some background immunity, it was quite low, as evidenced by numbers of laboratory-confirmed cases and reported hospitalizations [ 48 ]. We therefore assumed the effect of background immunity was negligible. The time to administer vaccine across the population was assumed to be 8 weeks.

Vaccine distribution spanned this roll-out period and resulted in final coverage levels in different age groups described below. For results shown below, we assumed the daily number of vaccinations gradually decreased throughout the campaign. However using different vaccination rates gave quantitatively and qualitatively similar results; see Appendix, Additional file 1 for details and additional information.

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We assumed that there was no intra-group age prioritization for vaccine distribution among those who were eligible to receive the vaccine. We assumed a 2-week delay between vaccine receipt and development of a protective immune response [ 49 ]. Although this number may seem high at first, studies of both the efficacy and the effectiveness of the pH1N1 vaccine used in Canada have shown remarkably high levels of protection [ 45 , 46 , 50 ].

Further, a recent study with over participants in Canada showed that this vaccine was highly effective at preventing laboratory-confirmed pH1N1 influenza [ 50 ]. Although as mentioned here there is strong evidence that the protection offered by the pH1N1 vaccine predominantly distributed in Canada was extremely high, we nevertheless also performed extensive sensitivity analyses of our results and conclusions assuming much lower values of vaccine protection see Figures S5 and S6, Additional file 1.

We considered four different vaccination strategies.

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However, each of these scenarios simulated different patterns of vaccine distribution across age groups. For the baseline 8-week campaign length, vaccination was completed by the end of the week of December 14, We tested the robustness of projections to model assumptions by performing sensitivity analyses over plausible ranges of parameter values. A range of values for R 0 , latent period, infectious period, vaccine efficacy, and vaccination campaign lengths see Table 1 were tested in the absence of vaccination where appropriate and in the presence of each of the three vaccination strategies.

For each vaccination strategy, we also tested the effect of varying the start date of vaccination campaigns under baseline transmission parameter values. Model outputs were assessed for vaccination campaigns initiated at the beginning of each week from July 5, , to November 22, Finally, we also assessed the impact of using different pH1N1 age-specific mortality profiles on our results.

Although true pH1N1 infection incidence is difficult to determine, the recorded spread of pH1N1 through different age groups in the GVRD starting in the early autumn of was closely reproduced by the model using the baseline parameter values for pH1N1 Figure 1. Age distribution of reported cases and comparison to model predictions. Population denominators for the given age groups were derived from census data for the GVRD [ 41 ]. Vaccination began the week of October 26, and continued for 8 weeks, to obtain the actual coverage levels outlined in Table 2.

The resulting epidemic curves assume R 0 of 1. Age-specific daily incidence of pH1N1 cases. The number of new cases per day per , individuals is presented in the absence solid lines and presence dotted lines of pH1N1 vaccination. Figure 2 shows the impact of simulating the actual GVRD pH1N1 vaccination campaign to the baseline model Actual Coverage strategy initiated October 26, , dashed lines. This intervention reduced the simulated cumulative attack rate from As expected, earlier implementation of the Actual Coverage strategy resulted in smaller final attack rates Figure 3.

Initiation of vaccination campaigns in the presence of moderate levels of circulating pH1N1, but prior to the epidemic peak, had a modest but detectable impact on final attack rates. Additionally, distribution of vaccine in a shorter period of time resulted in a greater reduction in attack rates for a given vaccination campaign start date.

Effect of vaccination campaign start date on overall attack rate.

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For a given vaccination campaign start date, the percent reduction in final attack rate relative to that observed in the absence of vaccination is presented for campaign lengths of between 4 and 12 weeks. Vaccination campaigns were implemented weekly, starting July 5, , with the last campaign started November 22, The start of the Vancouver influenza season on September 6, is indicated by a vertical line. All simulations assumed R 0 of 1.