Modeling H1N1 Vaccination Rates. N. Ganesh, Kennon R. Copeland, Nicholas D. Davis, National Opinion Research Center at the University of Chicago

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1 Modeling H1N1 Vaccination Rates N. Ganes, Kennon R. Copeland, Nicolas D. Davis, National Opinion Researc Center at e University of Cicago James A. Singleton, Tammy Santibanez, Centers for Disease Control and Prevention ABSTRACT: Te National 2009 H1N1 Flu Survey, a RDD landline and cellular telepone survey at operated from October 2009 roug June 2010 by NORC for e Centers for Disease Control and Prevention, tracked H1N1 (and seasonal influenza) vaccination coverage nationally on a weekly basis. National-level direct estimates for various sociodemograpic groups and geograpies were produced weekly based on a rolling sample of respondents. To reduce variability of estimates and trends, we estimated H1N1 vaccination rates using a survival analysis approac. In is paper, we consider parametric and non-parametric models wi date of vaccination eier interval censored or imputed, and we evaluate our models to select e "best" model at fits e data. Introduction In July 2009, e Advisory Committee on Immunization Practices (ACIP) issued recommendations for use of e influenza A (H1N1) 2009 monovalent vaccine (Centers for Disease Control and Prevention, 2009). Recognizing at e vaccine supply would not be ample immediately but would grow over time, ACIP identified: 1) initial target groups, consisting of approximately 159 million persons; and 2) a limited vaccine subset of e target groups, initially estimated at 42 million persons, to receive first priority wile e H1N1 vaccine supply was limited (CDC, 2009). ACIP recommended expanding vaccination to e rest of e population as vaccine supplies increased. To provide bo timely estimates of vaccination coverage and reliable estimates of coverage in priority populations (e.g., e initial target groups and e limited vaccine subset) roug e influenza season, CDC contracted wi NORC at e University of Cicago to design and implement e 2009 National H1N1 Flu Survey (NHFS). Te NHFS, conducted beginning e first week of October, 2009 and continuing roug e last week of June, 2010, provided weekly and monly estimates of H1N1 and seasonal flu vaccination coverage for e population along wi information concerning knowledge, attitudes, and beaviors related to e H1N1 influenza virus and its prevention. Weekly national estimates were generated wiin e four to six days following e end of eac survey week. Monly estimates at bo national and state levels were generated wiin e days following e end of eac survey mon. Te weekly and monly estimates were based upon relatively small samples and erefore subject to large sampling variability, resulting in unsmoo and unstable trends across time. Tis paper discusses modeling efforts undertaken to provide more stable and smoo vaccination coverage rate estimates. 5263

2 Summary of e NHFS Sample Design Te NHFS consists of a national random-digit dialed telepone survey based on a rolling weekly sample of landline and cellular telepones contacted to identify residential ouseolds (referred to as e NHFS sample). Wiin eac contacted NHFS sample ouseold, one adult and one cild (if present) are randomly selected for interview. Monly targets for e NHFS sample were establised to acieve a total of approximately 4,889 completed adult interviews from landline ouseolds and 1,111 completed adult interviews from cellular-pone-only (CPO) or cellular-pone-mainly (CPM) ouseolds 1, or approximately 6,000 total adult interviews. Te landline NHFS sample is augmented wi a sample of cildren aged <18 years identified during screening for e National Immunization Survey (referred to as e NIS sample). Te target sample size of completed adult interviews wiin eac telepone status group was allocated approximately equally across states. Deviations in state sample sizes were based upon e proportion minority (Hispanic, non-hispanic Black) population in eac state; states wi iger minority populations were allocated a sligtly larger sample. Sample for e NHFS was released on a weekly basis, wi eac released panel remaining active for five weeks. Eac sampled telepone number continued to be called across e five weeks until e number was resolved as non-residential, ere was a confirmed refusal, or a completed interview was obtained. Completed interviews obtained wiin a survey week, defined as Sunday roug Saturday, were en used in generating e estimates for at survey week. As sown in Figure 1, e estimates for a given survey week were us based upon completed interviews from five panels. For example, estimates for week ending November 7 were constructed from e interviews completed during at week as a result of e panels (2-6) released in weeks ending October 10 roug November 7. 1 A ouseold is cell pone only if ere is a cell pone but no landline telepone in e ome, and is cell pone mainly if ere is a landline number available in addition to a cell pone but e respondent reports at it would be unlikely for e landline to be answered if it were to ring. 5264

3 Figure 1:NHFS Rolling Sample Design Figure 1 Week End Date Panel Oct 3 Oct 10 Oct 17 Oct 24 Oct 31 Nov 7 Nov 14 Nov 21 Nov 28 1 X X X X X 2 X X X X X 3 X X X X X 4 X X X X X 5 X X X X X 6 X X X X 7 X X X 8 X X 9 X Estimates for a survey mon were based upon all completed interviews from survey weeks wi end dates wiin e survey mon (e.g., November 2009 survey mon consisted of survey weeks ending Nov 7, Nov 14, Nov 17, and Nov 24; sample completed Nov were part of survey week ending Dec 1 and us included in December 2009 survey mon). NHFS Data Collection Between e first week of October, 2009 and e last week of May, 2010, a total of 897,169 telepone lines were drawn from e NHFS sample frame. Of ese, 670,841 were landlines and 226,328 were cell pones. Among e 670,841 landline telepones sampled, e percent at were identifiable as residential, non-residential, or non-working numbers, known as e resolution rate, was 77.5%. Among identified residential telepones, e percent completing e screener to determine e presence of an eligible adult was 99.6%, wi 43.4% of sample adults in screened and eligible ouseolds being classified as completed adult interviews. Te product of e resolution rate, e screener completion rate, and e interview completion rate, known as e CASRO response rate, was 34.0% for e landline sample. Of e 226,328 cell pone lines, 53.3% were resolved, 85.7% of personal- use lines completed e screener, and 55.9% of eligible adults completed e survey, leading to a CASRO rate of 25.5%. A total of 63,659 completed interviews from e combined NHFS samples ad been collected as of e last week of May, Te cell telepone sample accounted for 12,662 (19.9%) of e NHFS completes. 5265

4 Statistical Meods for Obtaining Estimates for e NHFS Persons wi completed interviews from a given survey week (mon) were weigted to reflect: 1) sample selection (random selection of telepone numbers, selection of adult/cild wiin ouseold, number of telepone lines on wic e sampled person could be reaced); 2) combination of panels, NHFS and NIS samples (for cildren), and landline/cell telepone samples; and 3) ratio adjustment to population controls. Ratio adjustment was carried out roug a raking approac using race/enicity, age, gender, and region (state for monly weigting) as e raking dimensions. Estimated H1N1 and seasonal vaccination coverage were derived using e sample weigts and data on reported vaccination status. Estimated H1N1 and seasonal vaccination coverage were derived using e sample weigts and data on reported vaccination status. To summarize e statistical meodology by wic vaccination coverage rates and eir standard errors are estimated from e sample, let Y vij be an indicator of vaccination status for e v influenza vaccine, for e j person in e i sampled ouseold in e stratum of e NHFS sampling design, wic is equal to 1 if e person reports a vaccination for e v influenza vaccine (H1N1, seasonal) and 0 oerwise. Also, let W denote e final sampling weigt for is sampled person. Letting and Yˆ v W i, j ij Tˆ W ij i, j Y vij Ten e national estimator of vaccination coverage rate for vaccine v may be expressed as Letting ˆ v Yˆ v Tˆ. ij Z vij W ij Y ˆ vij v, Tˆ Z vi Z vij, j and 5266

5 Z v Z vi i, m an estimator of e variance of e estimated vaccination coverage rate can be expressed as ˆ m Z Z 2. v v m 1 vi v In ese equations, m denotes e number of sampled ouseolds containing persons wi completed interviews in e stratum. Note at ese formulae extend to estimates for any subgroup, s, of e population. A similar approac is taken for derivation of vaccination coverage estimates for a survey mon. Weekly H1N1 vaccination coverage estimates and associated 95% CIs for e total population and for cildren between e ages of 6 mons and 17 years for October Week 1 roug June Week 4 are sown in Figures 2 and 3. As can be seen, e vaccination coverage estimate trend is not monotonically increasing, wic must be e case for e underlying population vaccination coverage, due to independent samples from week-toweek and sampling variability. In addition, e trend is very unstable. Figures 2 and

6 Smooed Estimates of Vaccination Coverage In an attempt to provide more stable estimates of e vaccination coverage rates, bo for individual time periods and across time, two approaces were undertaken to combine data across week. Te callenge in combining data across weeks is at e reference period varies, as respondents were asked eir vaccination status as of e date of e interview. However, respondents reporting aving received a vaccination were also asked mon of vaccination, wic is used in e two approaces. Composite Estimation Te first approac entailed generation of composite estimates (Scaible, 1978, Wolter, 1979) of monly vaccination coverage for eac mon prior to e current survey week using data from e current survey week along wi all oer survey weeks following e end of eac individual mon, and incorporating ose monly coverage estimates wi weekly estimates of e interim vaccination coverage to a specific survey week to generate wat was referred to as an enanced weekly vaccination coverage estimate. Extending e notation provided for e official weekly estimates, let ˆ vmw be e estimated vaccination coverage for a prior mon, m, based upon sample data from e w survey week, ˆ vw be e estimated vaccination coverage for e current mon based upon e sample data from e w survey week, and n w be e number of completed interviews from e w survey week. Ten e composite vaccination coverage estimate for e m mon can be expressed as 5268

7 were n ˆ w vm w W m n w W m w ˆ vmw W m represents e set of survey weeks following e estimated variance of e composite estimate for v ˆ vm w W m n w w ww m n, 2 v ˆ vmw Te enanced vaccination coverage estimate for e expressed as ˆ vw w ˆ vm mm w ˆ vw,. m mon, and e m mon can be expressed as w survey week can en be were M represents e set of mons prior to e w survey week, and e estimated variance of e vaccination coverage can be expressed as (under e assumption of zero correlation between monly estimates) vˆ vˆ vˆ. vw mm w Potential Recall Error vm vw Reviewing e values, ˆ vmw, across weeks, it was observed at e values increased across time for October, and decreased for November, December, and January. Based on is finding, it is eorized at respondents were experiencing difficulty recalling mon of vaccination e longer from e vaccination date e interview occurred. As e interview asks Since September, ave you received an H1N1 vaccination? and en follows wi In wic mon did your receive your vaccination?, if e respondent as difficulty recalling e mon but knew it was in e past, ey may ave used e initial question as an ancor from wic to answer e mon question. Figures 4 and 5 sow e estimated vaccination coverage rate for October for Nov week 1 to Dec week 4, and for Dec week 4 to Jun week 4, respectively. As can be seen, e estimates were rougly constant roug Dec week 4, and ave been generally increasing since Dec week

8 Figures 4 and 5 Figures 6 and 7 sow e estimated vaccination coverage rate for November for Dec week 2 to Jan week 5, and for Jan week 5 to Jun week 4, respectively. As can be seen, e estimates were rougly constant roug Jan week 5, and ave been generally decreasing since Jan week 5. Similar results were observed for December and January. 5270

9 Figures 6 and 7 Based on e findings relative to potential recall error, e enanced estimation was modified to utilize data from only ose survey weeks determined to ave provided accurate recall of e vaccination rate for e mons of October roug January. Enanced Weekly Estimates Enanced weekly H1N1 vaccination coverage estimates and associated 95% CIs for e total and cild populations for October Week 1 roug June Week 4 are sown in Figures 8 and 9, respectively, along wi e official weekly estimates. As can be seen, e enanced vaccination coverage estimate trend is smooer and e individual estimates ave narrower CIs an e official estimates. However, e estimates are not constrained to be non-decreasing. 5271

10 Figures 8 and

11 Survival Estimation A second approac to generating monly estimates of vaccination coverage entailed application of a weigted Kaplan-Meier survival approac (Klein and Moescberger, 1997). For e Kaplan-Meier approac, e "censor" and "time-to-event" variables were defined as follows: 1. If a person was vaccinated in a mon prior to e mon of interview, en e "censor" variable was set to "not censored" and e "time-to-event" variable was set to e mon of vaccination. 2. Instead, if a person was vaccinated in e same mon as e mon of interview, en e "censor" variable was set to "censored" and e "time-to-event" variable was set to e mon preceding e mon of interview. 3. If a person was unvaccinated as of e day of e interview, en e "censor" variable was set to "censored" and e "time-to-event" variable was set to e mon preceding e mon of interview. For persons at were unvaccinated as of e day of e interview, eir "time-to-event" variable was set to e mon preceding e mon of interview as it is possible for ese cases to be vaccinated after e day of e interview but prior to e end of e mon of interview. To be consistent wi is definition, persons at were vaccinated on e mon of interview were also defined to be unvaccinated (i.e., "censored") as of e mon preceding e mon of interview. Tis monly survival analysis approac was used by e CDC to estimate influenza vaccination coverage by state and selected population groups (Centers for Disease Control and Prevention, 2010a; Centers for Disease Control and Prevention, 2010b). In order to obtain estimates for e most recent interview mon (i.e., mon of June), for cases at were interviewed in e mon of June, e censor and time variables were defined as follows: 1. If a person was vaccinated in e mon of June, en e "censor" variable was set to "not censored" and e "time-to-event" variable was set to mon of June. 2. If a person was unvaccinated as of e day of e interview, en e "censor" variable was set to "censored" and e "time-to-event" variable was set to e mon of June. Tis approac underestimates cumulative vaccination coverage as of e end of e most recent interview mon, because it assumes all persons unvaccinated as of e date of eir interview remained unvaccinated by e end of e mon. As mentioned previously, survey weigts were taken into account wen using e Kaplan-Meier approac in SUDAAN. In particular, e monly weigts from eac survey mon were appropriately normalized using e number of interviewed adults/cildren in eac state and mon. Tese normalized weigts, and e previously defined "censor" and "time-to-event" variables were used in SUDAAN to produce e monly estimates of vaccination coverage. KM monly H1N1 vaccination coverage estimates and associated 95% CIs for e total and cild populations for October roug April are sown in Figures 10 and 11, respectively, along wi e official monly estimates. As can be seen, e KM monly 5273

12 vaccination coverage estimates are greater an e official monly vaccination coverage estimates, as is expected given e official monly estimates represent e vaccination coverage as of approximately e mid-point of e survey mon wereas e KM and enanced monly vaccination coverage estimates represent e vaccination coverage as of e last day of e calendar mon. It can also be seen at e KM monly estimates yield smaller CI wids an does e official monly estimates. Figures 10 and

13 KM monly H1N1 vaccination coverage estimates and associated 95% CIs for e total and cild populations for October roug April are sown in Figures 12 and 13, respectively, along wi e enanced monly estimates. As can be seen, e enanced monly vaccination coverage estimates are greater an e KM monly vaccination coverage estimates, as is expected given e enanced monly estimates account for e observed recall error in reporting mon of vaccination. It can also be seen at e enanced and KM monly estimates yield similar CI wids. 5275

14 Figures 12 and

15 Discussion Survival estimates provide e opportunity to reduce variability and stabilize trends for survey estimates over time. In addition, a given generation of survival estimates yield non-decreasing estimates for a given trend, wic meets wi users expectations 2. Monly survival estimates for vaccination rates are easily derived using standard statistical software packages, suc as SUDAAN. Survival estimates are not, owever, appropriate for dynamic coorts of e population, suc as women pregnant during e vaccination period. To increase e usability of e data, finer granularity is needed, suc as weekly or daily survival estimates. Suc granularity is possible wi survival approaces, assuming survey data on week/date of vaccination are available or can be reliably modeled. As to application for monitoring of vaccination rates, e composite estimates appear preferable to e weekly estimates based solely on completed interviews during e survey week. For monly analysis, survival estimates appear preferable. Furer work is needed on e application of survival meods in deriving weekly and daily estimates, wi particular empasis on e problem of accounting for recall error in e survival estimates. References Centers for Disease Control and Prevention. Use of influenza A (H1N1) 2009 monovalent vaccine: recommendations of e Advisory Committee on Immunization Practices (ACIP), MMWR 2009;58(No. RR-10). Centers for Disease Control and Prevention (2010a). Interim results: state-specific influenza A (H1N1) 2009 monovalent vaccination coverage United States, October 2009-January MMWR 2010;59: Centers for Disease Control and Prevention (2010b). Interim results: state-specific seasonal influenza vaccination coverage United States, August 2009-January MMWR 2010;59: Klein, J.P. and Moescberger, M.L. (1997), Survival Analysis: Tecniques for Censored and Truncated Data, Springer: New York. Scaible, W.L. (1978) Coosing Weigts for Composite Estimators for Small Area Statistics. Proceedings of e Section on Survey Researc Meods, American Statistical Association, Wolter, K.M. (1979). Composite Estimation in Finite Populations, Journal of e American Statistical Association, 4: It sould be noted at survival estimates are only non-decreasing wiin a given estimation run. As new survival estimates are created across time, e user sees and compares estimates from different estimation runs, and e revealed series is not necessarily non-decreasing. 5277