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J Am Geriatr Soc. Author manuscript; available in PMC 2016 February 22. Published in final edited form as: J Am Geriatr Soc. 2015 September ; 63(9): 1798–1804. doi:10.1111/jgs.13617.
Estimating the Effect of Influenza Vaccination on Nursing Home Residents’ Morbidity and Mortality Aurora Pop-Vicas, MD*,†, Momotazur Rahman, PhD‡, Pedro L. Gozalo, PhD‡, Stefan Gravenstein, MD, MPH*,§,‖, and Vincent Mor, PhD‡,#
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*Warren
Alpert Medical School, Brown University, Providence, Rhode Island †Memorial Hospital of Rhode Island, Pawtucket, Rhode Island ‡Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, Rhode Island §University Hospitals—Case Medical Center, Cleveland, Ohio ‖Medical School, Case Western Reserve University, Cleveland, Ohio #Providence Veteran’s Administration Medical Center, Providence, Rhode Island
Abstract OBJECTIVES—To estimate the effect of influenza vaccination on hospitalization and mortality in nursing home (NH) residents. DESIGN—Retrospective cohort study.
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SETTING—Medicare claims data linked to NH Minimum Data Set assessments and Centers for Disease Control and Prevention (CDC) surveillance data from 122 U.S. cities. PARTICIPANTS—More than 1 million Medicare fee-for-service, long-stay NH residents between 2000 and 2009. MEASUREMENTS—Weekly facility outcome aggregates of NH resident pneumonia and influenza (P&I) hospitalizations and all-cause mortality and city-level P&I mortality as reported by the CDC were created. The seasonal vaccine match rate for influenza A/H1N1, A/H3N2, and B strains was calculated, and each outcome was compared in seasons of high and low match rates using facility fixed-effects regression models separately for full-year and nonsummer months.
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Address correspondence to Vincent Mor, Brown University, 121 South Main Street, Box G-S121, Providence, RI 02912. [email protected]. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Vincent Mor, Pedro Gozalo, and Momotazur Rahman 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: Pop-Vicas, Gravenstein, Gozalo, Mor. Data acquisition: Pop-Vicas, Mor. Data analysis and interpretation: all authors. Drafting of manuscript: Pop-Vicas, Rahman. Critical revision of manuscript for important intellectual content: all authors. Statistical analysis: Gozalo, Rahman. Obtaining funding: Mor. Study supervision: Mor. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Figure S1. Average weekly hospitalization rates and mortality in nursing home (NH) residents during influenza seasons 2000 to 2009. Figure S2. Prevalence of influenza strains positive for A/H3N2 according to match rate during influenza seasons 2000 to 2009. Table S1. Nursing Home Resident Characteristics According to Influenza A/H3N2 Vaccine Match Rate: 2000 to 2009 Please note: Wiley-Blackwell is not responsible for the content, accuracy, errors, or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.
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RESULTS—Average weekly all-cause mortality varied across seasons from 3.74 to 4.13 per 1,000 NH residents per week, and hospitalization for P&I varied from 2.05 to 2.43. Vaccine match rates were invariably high for H1N1 but variable across seasons for the other two types. The association between vaccine match and reduction in overall mortality and P&I hospitalizations was strongest for A/H3N2, the influenza strain typically responsible for the most-severe influenza cases. Given the approximately 130,000 deaths and 77,000 P&I hospitalizations of long-stay NH residents during the 32 nonsummer weeks, the model estimated that a 50-percentage-point increase in the A/H3N2 match rate (from 75%) reduced long-stay NH resident deaths by 2.0% and P&I hospitalizations by 4.2%. CONCLUSION—Well-matched influenza vaccine prevents P&I hospitalizations and mortality in NH residents.
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Keywords influenza vaccination; nursing home; hospitalization; vaccine match rate
INTRODUCTION Influenza causes significant morbidity, mortality, and healthcare costs,1–5 most of which elderly adults incur.4 Nursing home (NH) residents are especially vulnerable given their immune senescence, multimorbidity, and institutional quarters.6
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Vaccination remains the mainstay in prevention,7 recommended in the United States for all individuals aged 6 months and older. Nonetheless, some question the vaccine’s effectiveness in elderly adults.8,9 A meta-analysis that applied strict study inclusion criteria found no randomized placebo-controlled trials of influenza vaccine of individuals aged 65 and older and concluded that evidence is lacking in elderly adults of protection against laboratoryconfirmed influenza.10 Because randomized trials of influenza vaccine are no longer considered ethical,11 welldesigned observational studies are needed to inform the debate about the effectiveness of influenza vaccination policies directed toward protecting elderly adults. Numerous cohort and case–control studies have documented vaccine-associated reductions in influenzarelated hospitalizations and deaths in community-dwelling elderly adults12,13 and NH residents,14 although it is likely that these studies overestimate vaccine benefits because of frailty selection bias,15,16 or other unmeasured confounders.17 Studies that adjust for the effect of such unmeasured confounders report only small effects on hospitalizations and little or no mortality benefit in elderly adults.18
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These conflicting results may stem partially from the complexities of circulating seasonal influenza. Vaccine effectiveness depends on annual variations in influenza incidence, circulating strain virulence, and the quality of the vaccine-to–circulating strain match.19 In mild seasons, or when the quality of the vaccine match to circulating strain is good, too-few influenza cases may occur to permit detection of significant differences in vaccineassociated outcomes using an ecological model. To observe the vaccine-derived benefits that should be higher in seasons with a good vaccine match,20 it is necessary to study multiple
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influenza seasons. Furthermore, to overcome potential selection inherent in frailty bias, it is necessary to study populations with high rates of influenza vaccination rather than comparing vaccinated and unvaccinated individuals. NH residents, among the frailest of adult populations, with universal access to vaccine were studied. Influenza vaccination is a reportable quality indicator, ensuring higher vaccination coverage in NHs than in other community settings. Data on resident mortality and morbidity throughout the year and excluding the summer months, when influenza circulation is lowest, were used. National trends in mortality and influenza-related hospitalizations of NH residents were explored using a regression model that accounts for annual variation in prevailing strain distribution and vaccine match over 9 years.
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Study Design and Participants This was a retrospective cohort study of long-stay NH residents in the 122 U.S. cities under influenza surveillance by the Centers for Disease Control and Prevention (CDC). Sample and Data
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Using the Medicare enrollment file, quarterly mandatory NH resident Minimum Data Set (MDS) assessments, and Medicare inpatient hospital claims, weekly measures were created for each facility between October 2000 and March 2009. The Residential History File algorithm21 that concatenates MDS assessments and Medicare inpatient claims along with dates of death and Medicare Advantage health insurance coverage was used to identify all fee-for-service (FFS) long-stay residents (≥100 consecutive days in NH with only 10 intervening days in hospital) and aggregated counts of the number of deaths and of hospitalizations for pneumonia and influenza (P&I) per week per NH facility that long-stay NH residents experienced. Weekly data on the numbers of P&I city-level deaths that the CDC reported and published online were also abstracted as a point of comparative reference.22 Outcome Variables
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Three different outcome variables were calculated per facility week: all cause mortality (deaths per 1,000 long-stay FFS residents per week), hospitalization rate (hospitalizations due to P&I per 1,000 long-stay FFS NH residents), and city-level P&I mortality (CDCreported influenza-attributable deaths in a given week per 100,000 population in CDC surveillance cities).23 Hospitalizations were considered due to P&I if the primary discharge diagnosis was for any acute or chronic respiratory disease (International Classification of Diseases, Ninth Revision codes 460–466, 480–488, 490–496, 500–518).24 The rate of hospitalization for causes other than P&I was also calculated as a control measure; these were not expected to be responsive to vaccination. Main Explanatory Variable The main explanatory variable was the match rate between the prevailing seasonal influenza strains in circulation and the influenza vaccine used in a given season. This approach was
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recently used to compare one poorly matched year with several others in estimating the effect of vaccination in a population undergoing hemodialysis.25 A match rate variable was generated for each strain included in the yearly influenza vaccine (A/H1N1, A/H3N2, B). These match rates were calculated based on the CDC’s annual antigenic characterization summary as the percentage of circulating strains found to be genetically similar to the vaccine-included strains for that season.22
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Because all long-stay NH residents are frail and at risk of succumbing to seasonal influenza, it was decided not to standardize for case mix. The population characteristics of long-stay FFS residents in study facilities in good versus moderate versus poor match years were contrasted. The vaccination rate, which varies according to city and influenza season, was calculated from Online Survey and Certification Reporting System (OSCAR) data, which are reported annually, concurrent with NH inspections. Because the OSCAR vaccination rate varies according to the month of survey (more-complete and -accurate for surveys done in winter than summer), the vaccination rates reported in surveys between October and February were used to calculate the average yearly NH vaccination rate in each of the 122 CDC cities. The prevalence rate per strain was calculated as the percentage of samples that tested positive per year for specific strains in a given CDC division of the country as identified in the National Respiratory and Enteric Virus Surveillance System laboratories, which report the number of respiratory specimens tested and the number found positive for influenza types A and B each week. The CDC performs further antigenic characterization of a sample subset, allowing yearly match rate calculations for each strain.26 Statistical Analysis
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Outcomes are determined according to the interaction of three variables: the match rate of the influenza vaccination with circulating strains in the corresponding influenza season and city, the magnitude of the prevalence of influenza, and the vaccination rate. Ideally, the effect of all these three variables on outcomes should be examined, but it was found in the a priori analysis that influenza prevalence and match rate were correlated and endogenous—as vaccine match rate increased, the prevalence of influenza decreased (Figure S2, Appendix). Therefore, because the match rate can affect prevalence rates, it was decided to include only match rates and vaccination rates in the regression analyses. Influenza vaccination was incorporated as a government-promulgated measure of NH quality in 2005 and was observed to increase over time. Such changes are likely to be associated with market characteristics and NH quality, which may affect the outcome directly, but vaccination rates are not likely to be related to match rates and will not necessarily bias that estimate of effect if included in the analytical model. With these factors in mind, the main statistical model can be described as:
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Here Outcomeitw is the outcome variable for facility i in week w of influenza season t. The betas are coefficients of primary interest. MR_AH1tw, MR_AH3tw and MR_Btw represent the match rate of A/H1N1, A/H3N2, and B virus, respectively. VACCit represents the city-level vaccination rate of facility i in influenza season t. Because match rates vary only by t, influenza season fixed effects cannot be included, although a linear trend was included, represented by gamma. Thetas represent facility fixed effects. In the case of analyses performed using the CDC-supplied counts of deaths per week outcome, city fixed effects replaced facility fixed effects. Because influenza prevalence is low to nonexistent in the summer, all models were estimated separately with and without summer months (weeks 21– 39), consistent with a previously developed approach.27
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A series of sensitivity analyses were performed to determine how the model behaved under alternative specifications. First, rather than using a linear time trend, a more-restrictive yearfixed-effect term was used that capitalizes on the fact that influenza seasons overlap calendar years, making it possible to differentiate between influenza seasonal match and year. Based upon the estimates from the main statistical model, the number of long-stay NH residents’ lives saved and P&I hospitalizations avoided in an influenza season when the match increases by 50% (from s typical poor match rate of 25% to s good match rate of 75%) assuming that there are approximately 1 million long-stay NH residents in the United States on any given day was predicted. All analyses were performed using Stata version 12.0 (Stata Corp LP, College Station, TX). Ethics Statement
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The Brown University institutional review board approved this study, which relied upon Data Use Agreement 20296 with the Centers for Medicare and Medicaid Services to access the identifiable health data.
RESULTS
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Table 1 presents the match rate between the vaccine and the most prevalent circulating influenza strains per season along with the average annual vaccination rate and the outcome rates for the three outcome variables plus the control non-influenza-like-illness hospitalization rate. The match rate for A/H1N1 was largely invariant and quite high except for the 2007–08 season, when it was moderately good (66%). The match rate for B was generally low, although in one season, it was excellent and in another moderately good. The match rate for A/H3N2 was highly variable over the nine seasons, with three seasons being excellent, two moderately good and, four poor. The relationship between strain prevalence and match rate over the nine seasons was examined using standardized prevalence rates to plot them on the same vertical axis. A relationship could not be established for influenza B and H1N1, whereas A/H3N2 influenza prevalence was negatively correlated with the match rate, and the match rate explained approximately 42% of the variation in H3N2 prevalence rate (Figure S2, Appendix), substantiating the decision to exclude prevalence rates from the main statistical model.
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Because the match rate was consistently high for H1N1, and the 1 year of only moderate match could introduce idiosyncratic noise to the analyses, it was decided not to include the match rate for H1N1 in the main statistical model. Average weekly mortality varied from 3.74 to 4.13 per 1,000 long-stay NH residents per week; and the P&I hospitalization rate per 1,000 long-stay NH residents ranged from 2.05 to 2.43 (Table 1). CDC-reported P&I mortality fluctuated between 1.32/100,000 to 1.60/100,000 individuals. Overall mortality and P&I hospitalization rates per 1,000 longstay NH residents mirrored seasonal P&I mortality that the CDC reported in the 122 sentinel monitoring cities (Figure S1, Appendix).
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The mix of individuals whose mortality and hospital use were tracked did not meaningfully vary as a function of the annual match rate of the most-variable strain: H3N2 (Table S1, Appendix). The average age of the cohort was 77.2; 33.0% were male and 20.4% black. Diabetes mellitus, dementia from any cause, stroke, congestive heart failure, and emphysema or chronic obstructive pulmonary disorder were the most-common comorbid conditions, present in 29.7%, 14.0%, 17.0%, 13.2%, and 12.1% of the residents, respectively. The average ADL function score was 14.7 (range 0–28, with higher scores reflecting greater dependence on NH staff to perform daily duties).
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Table 2 presents the results of the regression model estimating the effect of the match between the vaccine and the two influenza strains on the outcomes. Results are presented separately for the full year and for the nonsummer months, and all models were controlled for the linear trend of calendar year, using NH and city fixed effects. For every percentage point improvement in the A/H3N2 match rate, mortality and P&I hospitalization rates during nonsummer months declined by approximately 0.002 deaths per 1,000 NH residents. A smaller effect can be seen in CDC-reported P&I city-wide mortality, which dropped by 0.0007 per 100,000 population for every 1% improvement in the match rate between the vaccine produced for use in any given year and A/H3N2 prevailing strain. Hospitalization of long-stay NH residents for primary diagnoses that did not include P&I revealed no statistically significant effect of the match rate for A/H3N2. The match rate for influenza strain B was unrelated to long-stay NH resident mortality or nonsummer P&I hospitalization rate. Further exploring the relationship between influenza prevalence and P&I hospitalization rate, Figure 1 reveals that, in years with a poor match, nonsummer weeks during which there was a high influenza A/H3N2 prevalence, P&I hospitalization rates were higher, but this relationship was largely absent in good match years.
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Table 3 presents the effect of vaccination on the outcomes studied, after controlling for the effect of match rate. As expected, increasing vaccination rates are associated with lower mortality and P&I hospitalizations, whereas the non-P&I hospitalizations are unaffected. A sensitivity analyses was performed, altering the model specification. Rather than using a linear calendar year time trend, the more-conservative year fixed-effect approach was used, which would necessarily absorb the effect of the match rate except for the fact that influenza
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seasons span 2 calendar years. The main results pertaining to the match rate of A/H3N2 for the nonsummer season remained the same.
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The model predicts that, for the estimated 1 million long-stay NH residents in U.S. nursing homes on any given day, a 1-percentage-point increase in vaccine match results in 1.6 fewer deaths and 2 fewer hospitalizations in an average nonsummer week. When the A/H3N2 match rate switches from a bad match rate of 25% to a good match rate of 75%, the model predicts approximately 80 fewer deaths and 100 fewer P&I hospitalizations per nonsummer week, which amounts to approximately 2,560 fewer deaths and 3,200 fewer P&I hospitalizations cumulatively over the 32 nonsummer weeks in a season. Given the approximately 130,000 deaths and 77,000 P&I hospitalizations of long-stay NH residents during the average influenza season, the model estimates that a 50-percentagepoint increase in the A/H3N2 match rate reduces long-stay NH resident deaths by 2.0% and P&I hospitalizations by 4.2%.
CONCLUSION
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This study took advantage of the random variation over multiple influenza seasons in the match rate between circulating and vaccine influenza strains to analyze the effect of vaccination on mortality and P&I hospitalization outcomes in the long-stay NH population. P&I hospitalizations and all-cause deaths in long-stay NH residents varied with the match between the vaccine and prevalent circulating strains. When the A/H3N2 match was excellent, P&I hospitalizations and deaths were significantly lower. All-cause mortality was estimated to be 2.0% less and P&I hospitalizations 4.2% less during an A/H3N2 predominant influenza season with a good (75%) match than in a year with a poor (25%) match. This equates to approximately 2,560 lives saved and more than 3,200 hospitalizations prevented annually for the U.S. long-stay NH resident population. The strongest association was observed between vaccine match and reduction in P&I hospitalizations and mortality for A/H3N2, the influenza strain typically responsible for the highest level of morbidity and mortality due to influenza.3,28 There was insufficient variability in the match rate for the H1N1 influenza strain to be able to estimate its effect properly because, in the nine seasons studied, the match rate fell below 90% in only 1 year. As for influenza strain B, the match rate was generally poor, with only one excellent and two moderately matched years.
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These results support the contention that influenza vaccination is an important primary prevention strategy for NH residents despite the poorer vaccine responsiveness of elderly adults.7,29,30 These results are in contrast to the recent literature suggesting limited, or no, vaccine benefits for older adults.10,18,25,31 Critics who cast doubt on vaccine effectiveness in older adults do so for three main reasons. First, there are few randomized controlled trials confirming a reduction in P&I and mortality, and the NH population is underrepresented in the few that have been conducted. Second, most trials define influenza cases according to positive serology, which can introduce bias in case detection, especially for frail elderly adults because placebo-controlled studies with the more-sensitive polymerase chain reaction or culture-confirmed influenza are lacking.10 Finally, the effect of confounding due to selection bias is traditionally cited as a bias in observational studies of influenza vaccination.8,17
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The approach used in this study overcomes selection bias because the vaccine effect is being tested on the entire population, not just those vaccinated. Furthermore, vaccine access is not pertinent in the NH setting, where the documented rate of vaccination in long-stay residents improved over the course of the study and approached 90% in NHs by 2010.32 The vaccine distributed at the beginning of an influenza season is manufactured each year to match the anticipated rather than the known influenza strains of the upcoming season; the actual match to the upcoming season’s circulating strains varies. As such, variation in the match rate is akin to subjecting the entire population to a natural experiment, as previously suggested.25,33 The advantage of studying the effect of the vaccine match on the long-stay NH population is that this population has high vaccination rates, particularly since 2005–06, when NHs were required to offer influenza vaccine to NH residents. Although laboratory confirmation was not provided, because biologically plausible differences between P&I outcomes and nonP&I hospitalizations were demonstrated, unrelated seasonal viral activity is not as likely to confound the findings. A previous study, accounting for the effect of antigenic match between vaccines and circulating strains, found no differences in vaccine-derived clinical benefits for individuals undergoing hemodialysis when three well-matched seasons were compared with a mismatched one.25 The current study used data from nine influenza seasons with greater heterogeneity in match rate rather than four seasons with a binary match rate. Furthermore, the effect of vaccination was tested in a population with uniformly high vaccination rates rather than a smaller population with less than a 50% vaccination rate, although like the previous study,25 the frailty bias common in observational studies was overcome by comparing population outcomes, regardless of individuals’ vaccination status.
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These results are broadly generalizable because a similar pattern of P&I mortality reduction was reported for the 122 CDC sentinel cities. Because this is an entirely independent data source, it is encouraging that these results are consistent. Also, the current study demonstrated that the vaccine match rate did not affect hospitalization for illnesses not related to influenza, which provides a “control” indicating that this was not an idiosyncratic change in NH hospitalization rates that increased during this period.34 Finally, the estimate of a 2.0% vaccine-associated annual mortality reduction in NH residents falls under the 10% threshold of overall seasonal influenza-attributable mortality,35 giving greater “face validity” to the model.
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This study has several limitations. First, detailed data on which individuals in the NH population were vaccinated in U.S. NHs is not available until after October 2006, meaning that estimates of the proportion of long-stay residents vaccinated every year were necessarily crude. Additionally, the evidence in the hospital sector is compelling; vaccination of healthcare workers contributes to reductions in influenza and its healthcare consequences,36 but no such data on NH staff vaccination rates are broadly available. Although the overall vaccination rates for NH residents increased steadily over the study period, it is possible that some of the vaccine-associated clinical benefits were the result of increasing regional herd immunity and subsequent decreased influenza exposure rather than individually conferred vaccine protection. The primary outcome variable, hospitalization for P&I according to Medicare claims, is less specific than laboratory-confirmed influenza,
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meaning that influenza-associated morbidity may have been overestimated in this study, although a precise assessment of influenza morbidity based upon laboratory-confirmed influenza is not feasible in large observational studies. Highly accurate diagnostic modalities such as reverse transcription polymerase chain reaction and viral culture are not available for clinical use in most NH-affiliated laboratories. Furthermore, frail elderly adults can have more-subtle clinical manifestations during influenza infection than younger adults; these signs of disease are often underrecognized, and few NH residents are tested for influenza.37 Finally, the effect of being vaccinated cannot be compared with that of no vaccination, something only possible in a large-scale clinical trial, which most would agree is unethical at this juncture. Despite all these limitations, because good and poor match seasons occur randomly over time, it is likely that these limitations are similarly present during seasons with low and high influenza activity, providing validity to these findings based on match rate comparisons.
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In summary, well-matched influenza vaccines can significantly reduce morbidity and mortality in NH residents and remain an important disease prevention modality for this vulnerable NH population. If influenza vaccination is beneficial even in this frail population, the effectiveness of seasonal influenza vaccines in healthier older populations may be underestimated.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments Author Manuscript
This study was supported in part by Agency for Health Care Research and Quality Grant R01HS018462. Sponsor’s Role: The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
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Figure 1.
Relationship between influenza A/H3N2 weekly prevalence and weekly pneumonia and influenza (P&I) hospitalization rate for bad vaccine match versus good vaccine match years. Increasing weekly prevalence of influenza strain A/H3N2 affects the rate of P&I hospitalization during nonsummer weeks for well-matched versus poorly matched seasons. In years with a poor match (solid line), weeks with high influenza A/H3N2 prevalence increased P&I hospitalization, but this relationship was barely observable in good match years (dotted line). Match rate varies from 1% to 100%; low (bad) match rate (≥25%) influenza seasons: 2003–04, 2004–05, 2006–07, and 2007–08. LS = long-stay; NH = nursing home.
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H3N2
H1N1 and B
H3N2
H3N2
H3N2
H1N1
H3N2
H1N1
2001–02
2002–03
2003–04
2004–05
2005–06
2006–07
2007–08
2008–09
100
66
90
97
100
100
100
100
95
A/ H1N1 B
17
1.5
23e 100
38.5
24e
61.8
22e 3.1
7.0
72.8
99.6
11.2e
4.8
11
85
100
100
A/ H3N2
66.9
67.2
62.8
59.7
55.2
58.7
58.1
52.7
50.9
Vaccination Rate, %
1.37
1.48
1.32
1.46
1.56
1.60
1.44
1.50
1.55
CDC Average Mortality with P&Ic
2.05
2.30
2.19
2.22
2.43
2.34
2.26
2.34
2.25
Average Hospitalization Rated with P&I
Outcomes
9.16
9.12
8.67
8.46
8.22
7.91
7.80
7.61
7.42
Average Hospitalization Rated without P&I
Number of long-stay FFS residents hospitalized from a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
Poor match year.
e
d
c Number of deaths with pneumonia and influenza (P&I) in a city in a given week per 100,000 city population.
Number of death in long-stay (>100 days) fee-for-service (FFS) residents in a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
b
3.74
3.98
3.88
3.87
4.12
4.07
4.05
4.04
4.13
Average FFS Long-Stay Mortalityb
Match rate between seasonal influenza prevailing strain and vaccine strain.
a
H1N1
Predominant Strain Overall
2000–01
Influenza Season
Match Rate, %a
Seasonal Influenza Prevailing Strain and Vaccine Strain Match Rates and Average Weekly Outcomes in Long-Stay Nursing Home Residents and Centers for Disease Control and Prevention (CDC) Surveillance Cities during Influenza Seasons 2000–2009
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−0.0003
All Seasons, n = 1,034,589
0.0001
−0.0006c
−0.0016b 0.0004
All Seasons, n = 1,034,589
Nonsummer, n = 713,819
0.0002
−0.0020b
Nonsummer, n = 713,819
−0.0004
0.0008
All Seasons, n = 1,034,589
−0.0006
0.0004
Nonsummer, n = 713,819
Average Hospitalization Ratea without P&I
−0.0007c −0.0006
−0.0006d
Nonsummer, n = 34,954
−0.0001
All Seasons, n = 50,838
Centers for Disease Control and Prevention Mortality with P&I
.10.
d
.05,
c
P < b.001,
a Number of long-stay (>100 days) fee-for-service (FFS) residents hospitalized from a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
Model controlled for linear trends for calendar years, nursing home, and city, and is weighted by the number of nursing home residents at risk for hospitalizations and deaths for each week.
P&I = pneumonia and influenza.
B match rate
A/H3N2 match rate
Influenza Strain
FFS Long-Stay Mortality
Average Hospitalization Ratea with P&I
Estimated Effects on Mortality and Hospitalization Outcomes for a 1-Percentage-Point Improvement in the Match Rate Between the Circulating Influenza and Vaccine Strains
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Author Manuscript 0.0005
−0.0018
Nonsummer, n = 713,819
Nonsummer, n = 713,819 −0.0055b
All Seasons, n = 1,034,589 −0.00358c 0.0009
All Seasons, n = 1,034,589 0.0008
Nonsummer, n = 713,819
Average Hospitalization Ratea without P&I
−0.0031d
All Seasons, n = 50,838 −0.0049b
Nonsummer, n = 34,954
.10.
.05,
d
c
P < b.01,
Number of long-stay (>100 days) fee-for-service (FFS) residents hospitalized from a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
a
Model controlled for linear trends for calendar years, nursing home, and city and was weighted by the number of nursing home residents at risk for hospitalizations and deaths for each week.
P&I = pneumonia and influenza.
Vaccination rate
All Seasons, n = 1,034,589
FFS Long-Stay Mortality
Average Hospitalization Ratea with P&I
Centers for Disease Control and Prevention Mortality with P&I
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Estimated Effect of Vaccination Rate on Mortality and Hospitalization Outcomes
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Table 3 Pop-Vicas et al. Page 15
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J Am Geriatr Soc. Author manuscript; available in PMC 2016 February 22. Published in final edited form as: J Am Geriatr Soc. 2015 September ; 63(9): 1798–1804. doi:10.1111/jgs.13617.
Estimating the Effect of Influenza Vaccination on Nursing Home Residents’ Morbidity and Mortality Aurora Pop-Vicas, MD*,†, Momotazur Rahman, PhD‡, Pedro L. Gozalo, PhD‡, Stefan Gravenstein, MD, MPH*,§,‖, and Vincent Mor, PhD‡,#
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*Warren
Alpert Medical School, Brown University, Providence, Rhode Island †Memorial Hospital of Rhode Island, Pawtucket, Rhode Island ‡Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, Rhode Island §University Hospitals—Case Medical Center, Cleveland, Ohio ‖Medical School, Case Western Reserve University, Cleveland, Ohio #Providence Veteran’s Administration Medical Center, Providence, Rhode Island
Abstract OBJECTIVES—To estimate the effect of influenza vaccination on hospitalization and mortality in nursing home (NH) residents. DESIGN—Retrospective cohort study.
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SETTING—Medicare claims data linked to NH Minimum Data Set assessments and Centers for Disease Control and Prevention (CDC) surveillance data from 122 U.S. cities. PARTICIPANTS—More than 1 million Medicare fee-for-service, long-stay NH residents between 2000 and 2009. MEASUREMENTS—Weekly facility outcome aggregates of NH resident pneumonia and influenza (P&I) hospitalizations and all-cause mortality and city-level P&I mortality as reported by the CDC were created. The seasonal vaccine match rate for influenza A/H1N1, A/H3N2, and B strains was calculated, and each outcome was compared in seasons of high and low match rates using facility fixed-effects regression models separately for full-year and nonsummer months.
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Address correspondence to Vincent Mor, Brown University, 121 South Main Street, Box G-S121, Providence, RI 02912. [email protected]. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Vincent Mor, Pedro Gozalo, and Momotazur Rahman 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: Pop-Vicas, Gravenstein, Gozalo, Mor. Data acquisition: Pop-Vicas, Mor. Data analysis and interpretation: all authors. Drafting of manuscript: Pop-Vicas, Rahman. Critical revision of manuscript for important intellectual content: all authors. Statistical analysis: Gozalo, Rahman. Obtaining funding: Mor. Study supervision: Mor. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Figure S1. Average weekly hospitalization rates and mortality in nursing home (NH) residents during influenza seasons 2000 to 2009. Figure S2. Prevalence of influenza strains positive for A/H3N2 according to match rate during influenza seasons 2000 to 2009. Table S1. Nursing Home Resident Characteristics According to Influenza A/H3N2 Vaccine Match Rate: 2000 to 2009 Please note: Wiley-Blackwell is not responsible for the content, accuracy, errors, or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.
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RESULTS—Average weekly all-cause mortality varied across seasons from 3.74 to 4.13 per 1,000 NH residents per week, and hospitalization for P&I varied from 2.05 to 2.43. Vaccine match rates were invariably high for H1N1 but variable across seasons for the other two types. The association between vaccine match and reduction in overall mortality and P&I hospitalizations was strongest for A/H3N2, the influenza strain typically responsible for the most-severe influenza cases. Given the approximately 130,000 deaths and 77,000 P&I hospitalizations of long-stay NH residents during the 32 nonsummer weeks, the model estimated that a 50-percentage-point increase in the A/H3N2 match rate (from 75%) reduced long-stay NH resident deaths by 2.0% and P&I hospitalizations by 4.2%. CONCLUSION—Well-matched influenza vaccine prevents P&I hospitalizations and mortality in NH residents.
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Keywords influenza vaccination; nursing home; hospitalization; vaccine match rate
INTRODUCTION Influenza causes significant morbidity, mortality, and healthcare costs,1–5 most of which elderly adults incur.4 Nursing home (NH) residents are especially vulnerable given their immune senescence, multimorbidity, and institutional quarters.6
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Vaccination remains the mainstay in prevention,7 recommended in the United States for all individuals aged 6 months and older. Nonetheless, some question the vaccine’s effectiveness in elderly adults.8,9 A meta-analysis that applied strict study inclusion criteria found no randomized placebo-controlled trials of influenza vaccine of individuals aged 65 and older and concluded that evidence is lacking in elderly adults of protection against laboratoryconfirmed influenza.10 Because randomized trials of influenza vaccine are no longer considered ethical,11 welldesigned observational studies are needed to inform the debate about the effectiveness of influenza vaccination policies directed toward protecting elderly adults. Numerous cohort and case–control studies have documented vaccine-associated reductions in influenzarelated hospitalizations and deaths in community-dwelling elderly adults12,13 and NH residents,14 although it is likely that these studies overestimate vaccine benefits because of frailty selection bias,15,16 or other unmeasured confounders.17 Studies that adjust for the effect of such unmeasured confounders report only small effects on hospitalizations and little or no mortality benefit in elderly adults.18
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These conflicting results may stem partially from the complexities of circulating seasonal influenza. Vaccine effectiveness depends on annual variations in influenza incidence, circulating strain virulence, and the quality of the vaccine-to–circulating strain match.19 In mild seasons, or when the quality of the vaccine match to circulating strain is good, too-few influenza cases may occur to permit detection of significant differences in vaccineassociated outcomes using an ecological model. To observe the vaccine-derived benefits that should be higher in seasons with a good vaccine match,20 it is necessary to study multiple
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influenza seasons. Furthermore, to overcome potential selection inherent in frailty bias, it is necessary to study populations with high rates of influenza vaccination rather than comparing vaccinated and unvaccinated individuals. NH residents, among the frailest of adult populations, with universal access to vaccine were studied. Influenza vaccination is a reportable quality indicator, ensuring higher vaccination coverage in NHs than in other community settings. Data on resident mortality and morbidity throughout the year and excluding the summer months, when influenza circulation is lowest, were used. National trends in mortality and influenza-related hospitalizations of NH residents were explored using a regression model that accounts for annual variation in prevailing strain distribution and vaccine match over 9 years.
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Study Design and Participants This was a retrospective cohort study of long-stay NH residents in the 122 U.S. cities under influenza surveillance by the Centers for Disease Control and Prevention (CDC). Sample and Data
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Using the Medicare enrollment file, quarterly mandatory NH resident Minimum Data Set (MDS) assessments, and Medicare inpatient hospital claims, weekly measures were created for each facility between October 2000 and March 2009. The Residential History File algorithm21 that concatenates MDS assessments and Medicare inpatient claims along with dates of death and Medicare Advantage health insurance coverage was used to identify all fee-for-service (FFS) long-stay residents (≥100 consecutive days in NH with only 10 intervening days in hospital) and aggregated counts of the number of deaths and of hospitalizations for pneumonia and influenza (P&I) per week per NH facility that long-stay NH residents experienced. Weekly data on the numbers of P&I city-level deaths that the CDC reported and published online were also abstracted as a point of comparative reference.22 Outcome Variables
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Three different outcome variables were calculated per facility week: all cause mortality (deaths per 1,000 long-stay FFS residents per week), hospitalization rate (hospitalizations due to P&I per 1,000 long-stay FFS NH residents), and city-level P&I mortality (CDCreported influenza-attributable deaths in a given week per 100,000 population in CDC surveillance cities).23 Hospitalizations were considered due to P&I if the primary discharge diagnosis was for any acute or chronic respiratory disease (International Classification of Diseases, Ninth Revision codes 460–466, 480–488, 490–496, 500–518).24 The rate of hospitalization for causes other than P&I was also calculated as a control measure; these were not expected to be responsive to vaccination. Main Explanatory Variable The main explanatory variable was the match rate between the prevailing seasonal influenza strains in circulation and the influenza vaccine used in a given season. This approach was
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recently used to compare one poorly matched year with several others in estimating the effect of vaccination in a population undergoing hemodialysis.25 A match rate variable was generated for each strain included in the yearly influenza vaccine (A/H1N1, A/H3N2, B). These match rates were calculated based on the CDC’s annual antigenic characterization summary as the percentage of circulating strains found to be genetically similar to the vaccine-included strains for that season.22
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Because all long-stay NH residents are frail and at risk of succumbing to seasonal influenza, it was decided not to standardize for case mix. The population characteristics of long-stay FFS residents in study facilities in good versus moderate versus poor match years were contrasted. The vaccination rate, which varies according to city and influenza season, was calculated from Online Survey and Certification Reporting System (OSCAR) data, which are reported annually, concurrent with NH inspections. Because the OSCAR vaccination rate varies according to the month of survey (more-complete and -accurate for surveys done in winter than summer), the vaccination rates reported in surveys between October and February were used to calculate the average yearly NH vaccination rate in each of the 122 CDC cities. The prevalence rate per strain was calculated as the percentage of samples that tested positive per year for specific strains in a given CDC division of the country as identified in the National Respiratory and Enteric Virus Surveillance System laboratories, which report the number of respiratory specimens tested and the number found positive for influenza types A and B each week. The CDC performs further antigenic characterization of a sample subset, allowing yearly match rate calculations for each strain.26 Statistical Analysis
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Outcomes are determined according to the interaction of three variables: the match rate of the influenza vaccination with circulating strains in the corresponding influenza season and city, the magnitude of the prevalence of influenza, and the vaccination rate. Ideally, the effect of all these three variables on outcomes should be examined, but it was found in the a priori analysis that influenza prevalence and match rate were correlated and endogenous—as vaccine match rate increased, the prevalence of influenza decreased (Figure S2, Appendix). Therefore, because the match rate can affect prevalence rates, it was decided to include only match rates and vaccination rates in the regression analyses. Influenza vaccination was incorporated as a government-promulgated measure of NH quality in 2005 and was observed to increase over time. Such changes are likely to be associated with market characteristics and NH quality, which may affect the outcome directly, but vaccination rates are not likely to be related to match rates and will not necessarily bias that estimate of effect if included in the analytical model. With these factors in mind, the main statistical model can be described as:
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Here Outcomeitw is the outcome variable for facility i in week w of influenza season t. The betas are coefficients of primary interest. MR_AH1tw, MR_AH3tw and MR_Btw represent the match rate of A/H1N1, A/H3N2, and B virus, respectively. VACCit represents the city-level vaccination rate of facility i in influenza season t. Because match rates vary only by t, influenza season fixed effects cannot be included, although a linear trend was included, represented by gamma. Thetas represent facility fixed effects. In the case of analyses performed using the CDC-supplied counts of deaths per week outcome, city fixed effects replaced facility fixed effects. Because influenza prevalence is low to nonexistent in the summer, all models were estimated separately with and without summer months (weeks 21– 39), consistent with a previously developed approach.27
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A series of sensitivity analyses were performed to determine how the model behaved under alternative specifications. First, rather than using a linear time trend, a more-restrictive yearfixed-effect term was used that capitalizes on the fact that influenza seasons overlap calendar years, making it possible to differentiate between influenza seasonal match and year. Based upon the estimates from the main statistical model, the number of long-stay NH residents’ lives saved and P&I hospitalizations avoided in an influenza season when the match increases by 50% (from s typical poor match rate of 25% to s good match rate of 75%) assuming that there are approximately 1 million long-stay NH residents in the United States on any given day was predicted. All analyses were performed using Stata version 12.0 (Stata Corp LP, College Station, TX). Ethics Statement
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The Brown University institutional review board approved this study, which relied upon Data Use Agreement 20296 with the Centers for Medicare and Medicaid Services to access the identifiable health data.
RESULTS
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Table 1 presents the match rate between the vaccine and the most prevalent circulating influenza strains per season along with the average annual vaccination rate and the outcome rates for the three outcome variables plus the control non-influenza-like-illness hospitalization rate. The match rate for A/H1N1 was largely invariant and quite high except for the 2007–08 season, when it was moderately good (66%). The match rate for B was generally low, although in one season, it was excellent and in another moderately good. The match rate for A/H3N2 was highly variable over the nine seasons, with three seasons being excellent, two moderately good and, four poor. The relationship between strain prevalence and match rate over the nine seasons was examined using standardized prevalence rates to plot them on the same vertical axis. A relationship could not be established for influenza B and H1N1, whereas A/H3N2 influenza prevalence was negatively correlated with the match rate, and the match rate explained approximately 42% of the variation in H3N2 prevalence rate (Figure S2, Appendix), substantiating the decision to exclude prevalence rates from the main statistical model.
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Because the match rate was consistently high for H1N1, and the 1 year of only moderate match could introduce idiosyncratic noise to the analyses, it was decided not to include the match rate for H1N1 in the main statistical model. Average weekly mortality varied from 3.74 to 4.13 per 1,000 long-stay NH residents per week; and the P&I hospitalization rate per 1,000 long-stay NH residents ranged from 2.05 to 2.43 (Table 1). CDC-reported P&I mortality fluctuated between 1.32/100,000 to 1.60/100,000 individuals. Overall mortality and P&I hospitalization rates per 1,000 longstay NH residents mirrored seasonal P&I mortality that the CDC reported in the 122 sentinel monitoring cities (Figure S1, Appendix).
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The mix of individuals whose mortality and hospital use were tracked did not meaningfully vary as a function of the annual match rate of the most-variable strain: H3N2 (Table S1, Appendix). The average age of the cohort was 77.2; 33.0% were male and 20.4% black. Diabetes mellitus, dementia from any cause, stroke, congestive heart failure, and emphysema or chronic obstructive pulmonary disorder were the most-common comorbid conditions, present in 29.7%, 14.0%, 17.0%, 13.2%, and 12.1% of the residents, respectively. The average ADL function score was 14.7 (range 0–28, with higher scores reflecting greater dependence on NH staff to perform daily duties).
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Table 2 presents the results of the regression model estimating the effect of the match between the vaccine and the two influenza strains on the outcomes. Results are presented separately for the full year and for the nonsummer months, and all models were controlled for the linear trend of calendar year, using NH and city fixed effects. For every percentage point improvement in the A/H3N2 match rate, mortality and P&I hospitalization rates during nonsummer months declined by approximately 0.002 deaths per 1,000 NH residents. A smaller effect can be seen in CDC-reported P&I city-wide mortality, which dropped by 0.0007 per 100,000 population for every 1% improvement in the match rate between the vaccine produced for use in any given year and A/H3N2 prevailing strain. Hospitalization of long-stay NH residents for primary diagnoses that did not include P&I revealed no statistically significant effect of the match rate for A/H3N2. The match rate for influenza strain B was unrelated to long-stay NH resident mortality or nonsummer P&I hospitalization rate. Further exploring the relationship between influenza prevalence and P&I hospitalization rate, Figure 1 reveals that, in years with a poor match, nonsummer weeks during which there was a high influenza A/H3N2 prevalence, P&I hospitalization rates were higher, but this relationship was largely absent in good match years.
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Table 3 presents the effect of vaccination on the outcomes studied, after controlling for the effect of match rate. As expected, increasing vaccination rates are associated with lower mortality and P&I hospitalizations, whereas the non-P&I hospitalizations are unaffected. A sensitivity analyses was performed, altering the model specification. Rather than using a linear calendar year time trend, the more-conservative year fixed-effect approach was used, which would necessarily absorb the effect of the match rate except for the fact that influenza
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seasons span 2 calendar years. The main results pertaining to the match rate of A/H3N2 for the nonsummer season remained the same.
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The model predicts that, for the estimated 1 million long-stay NH residents in U.S. nursing homes on any given day, a 1-percentage-point increase in vaccine match results in 1.6 fewer deaths and 2 fewer hospitalizations in an average nonsummer week. When the A/H3N2 match rate switches from a bad match rate of 25% to a good match rate of 75%, the model predicts approximately 80 fewer deaths and 100 fewer P&I hospitalizations per nonsummer week, which amounts to approximately 2,560 fewer deaths and 3,200 fewer P&I hospitalizations cumulatively over the 32 nonsummer weeks in a season. Given the approximately 130,000 deaths and 77,000 P&I hospitalizations of long-stay NH residents during the average influenza season, the model estimates that a 50-percentagepoint increase in the A/H3N2 match rate reduces long-stay NH resident deaths by 2.0% and P&I hospitalizations by 4.2%.
CONCLUSION
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This study took advantage of the random variation over multiple influenza seasons in the match rate between circulating and vaccine influenza strains to analyze the effect of vaccination on mortality and P&I hospitalization outcomes in the long-stay NH population. P&I hospitalizations and all-cause deaths in long-stay NH residents varied with the match between the vaccine and prevalent circulating strains. When the A/H3N2 match was excellent, P&I hospitalizations and deaths were significantly lower. All-cause mortality was estimated to be 2.0% less and P&I hospitalizations 4.2% less during an A/H3N2 predominant influenza season with a good (75%) match than in a year with a poor (25%) match. This equates to approximately 2,560 lives saved and more than 3,200 hospitalizations prevented annually for the U.S. long-stay NH resident population. The strongest association was observed between vaccine match and reduction in P&I hospitalizations and mortality for A/H3N2, the influenza strain typically responsible for the highest level of morbidity and mortality due to influenza.3,28 There was insufficient variability in the match rate for the H1N1 influenza strain to be able to estimate its effect properly because, in the nine seasons studied, the match rate fell below 90% in only 1 year. As for influenza strain B, the match rate was generally poor, with only one excellent and two moderately matched years.
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These results support the contention that influenza vaccination is an important primary prevention strategy for NH residents despite the poorer vaccine responsiveness of elderly adults.7,29,30 These results are in contrast to the recent literature suggesting limited, or no, vaccine benefits for older adults.10,18,25,31 Critics who cast doubt on vaccine effectiveness in older adults do so for three main reasons. First, there are few randomized controlled trials confirming a reduction in P&I and mortality, and the NH population is underrepresented in the few that have been conducted. Second, most trials define influenza cases according to positive serology, which can introduce bias in case detection, especially for frail elderly adults because placebo-controlled studies with the more-sensitive polymerase chain reaction or culture-confirmed influenza are lacking.10 Finally, the effect of confounding due to selection bias is traditionally cited as a bias in observational studies of influenza vaccination.8,17
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The approach used in this study overcomes selection bias because the vaccine effect is being tested on the entire population, not just those vaccinated. Furthermore, vaccine access is not pertinent in the NH setting, where the documented rate of vaccination in long-stay residents improved over the course of the study and approached 90% in NHs by 2010.32 The vaccine distributed at the beginning of an influenza season is manufactured each year to match the anticipated rather than the known influenza strains of the upcoming season; the actual match to the upcoming season’s circulating strains varies. As such, variation in the match rate is akin to subjecting the entire population to a natural experiment, as previously suggested.25,33 The advantage of studying the effect of the vaccine match on the long-stay NH population is that this population has high vaccination rates, particularly since 2005–06, when NHs were required to offer influenza vaccine to NH residents. Although laboratory confirmation was not provided, because biologically plausible differences between P&I outcomes and nonP&I hospitalizations were demonstrated, unrelated seasonal viral activity is not as likely to confound the findings. A previous study, accounting for the effect of antigenic match between vaccines and circulating strains, found no differences in vaccine-derived clinical benefits for individuals undergoing hemodialysis when three well-matched seasons were compared with a mismatched one.25 The current study used data from nine influenza seasons with greater heterogeneity in match rate rather than four seasons with a binary match rate. Furthermore, the effect of vaccination was tested in a population with uniformly high vaccination rates rather than a smaller population with less than a 50% vaccination rate, although like the previous study,25 the frailty bias common in observational studies was overcome by comparing population outcomes, regardless of individuals’ vaccination status.
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These results are broadly generalizable because a similar pattern of P&I mortality reduction was reported for the 122 CDC sentinel cities. Because this is an entirely independent data source, it is encouraging that these results are consistent. Also, the current study demonstrated that the vaccine match rate did not affect hospitalization for illnesses not related to influenza, which provides a “control” indicating that this was not an idiosyncratic change in NH hospitalization rates that increased during this period.34 Finally, the estimate of a 2.0% vaccine-associated annual mortality reduction in NH residents falls under the 10% threshold of overall seasonal influenza-attributable mortality,35 giving greater “face validity” to the model.
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This study has several limitations. First, detailed data on which individuals in the NH population were vaccinated in U.S. NHs is not available until after October 2006, meaning that estimates of the proportion of long-stay residents vaccinated every year were necessarily crude. Additionally, the evidence in the hospital sector is compelling; vaccination of healthcare workers contributes to reductions in influenza and its healthcare consequences,36 but no such data on NH staff vaccination rates are broadly available. Although the overall vaccination rates for NH residents increased steadily over the study period, it is possible that some of the vaccine-associated clinical benefits were the result of increasing regional herd immunity and subsequent decreased influenza exposure rather than individually conferred vaccine protection. The primary outcome variable, hospitalization for P&I according to Medicare claims, is less specific than laboratory-confirmed influenza,
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meaning that influenza-associated morbidity may have been overestimated in this study, although a precise assessment of influenza morbidity based upon laboratory-confirmed influenza is not feasible in large observational studies. Highly accurate diagnostic modalities such as reverse transcription polymerase chain reaction and viral culture are not available for clinical use in most NH-affiliated laboratories. Furthermore, frail elderly adults can have more-subtle clinical manifestations during influenza infection than younger adults; these signs of disease are often underrecognized, and few NH residents are tested for influenza.37 Finally, the effect of being vaccinated cannot be compared with that of no vaccination, something only possible in a large-scale clinical trial, which most would agree is unethical at this juncture. Despite all these limitations, because good and poor match seasons occur randomly over time, it is likely that these limitations are similarly present during seasons with low and high influenza activity, providing validity to these findings based on match rate comparisons.
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In summary, well-matched influenza vaccines can significantly reduce morbidity and mortality in NH residents and remain an important disease prevention modality for this vulnerable NH population. If influenza vaccination is beneficial even in this frail population, the effectiveness of seasonal influenza vaccines in healthier older populations may be underestimated.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
Acknowledgments Author Manuscript
This study was supported in part by Agency for Health Care Research and Quality Grant R01HS018462. Sponsor’s Role: The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
REFERENCES
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Figure 1.
Relationship between influenza A/H3N2 weekly prevalence and weekly pneumonia and influenza (P&I) hospitalization rate for bad vaccine match versus good vaccine match years. Increasing weekly prevalence of influenza strain A/H3N2 affects the rate of P&I hospitalization during nonsummer weeks for well-matched versus poorly matched seasons. In years with a poor match (solid line), weeks with high influenza A/H3N2 prevalence increased P&I hospitalization, but this relationship was barely observable in good match years (dotted line). Match rate varies from 1% to 100%; low (bad) match rate (≥25%) influenza seasons: 2003–04, 2004–05, 2006–07, and 2007–08. LS = long-stay; NH = nursing home.
Author Manuscript J Am Geriatr Soc. Author manuscript; available in PMC 2016 February 22.
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H3N2
H1N1 and B
H3N2
H3N2
H3N2
H1N1
H3N2
H1N1
2001–02
2002–03
2003–04
2004–05
2005–06
2006–07
2007–08
2008–09
100
66
90
97
100
100
100
100
95
A/ H1N1 B
17
1.5
23e 100
38.5
24e
61.8
22e 3.1
7.0
72.8
99.6
11.2e
4.8
11
85
100
100
A/ H3N2
66.9
67.2
62.8
59.7
55.2
58.7
58.1
52.7
50.9
Vaccination Rate, %
1.37
1.48
1.32
1.46
1.56
1.60
1.44
1.50
1.55
CDC Average Mortality with P&Ic
2.05
2.30
2.19
2.22
2.43
2.34
2.26
2.34
2.25
Average Hospitalization Rated with P&I
Outcomes
9.16
9.12
8.67
8.46
8.22
7.91
7.80
7.61
7.42
Average Hospitalization Rated without P&I
Number of long-stay FFS residents hospitalized from a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
Poor match year.
e
d
c Number of deaths with pneumonia and influenza (P&I) in a city in a given week per 100,000 city population.
Number of death in long-stay (>100 days) fee-for-service (FFS) residents in a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
b
3.74
3.98
3.88
3.87
4.12
4.07
4.05
4.04
4.13
Average FFS Long-Stay Mortalityb
Match rate between seasonal influenza prevailing strain and vaccine strain.
a
H1N1
Predominant Strain Overall
2000–01
Influenza Season
Match Rate, %a
Seasonal Influenza Prevailing Strain and Vaccine Strain Match Rates and Average Weekly Outcomes in Long-Stay Nursing Home Residents and Centers for Disease Control and Prevention (CDC) Surveillance Cities during Influenza Seasons 2000–2009
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Table 1 Pop-Vicas et al. Page 13
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Author Manuscript 0.0000
−0.0003
All Seasons, n = 1,034,589
0.0001
−0.0006c
−0.0016b 0.0004
All Seasons, n = 1,034,589
Nonsummer, n = 713,819
0.0002
−0.0020b
Nonsummer, n = 713,819
−0.0004
0.0008
All Seasons, n = 1,034,589
−0.0006
0.0004
Nonsummer, n = 713,819
Average Hospitalization Ratea without P&I
−0.0007c −0.0006
−0.0006d
Nonsummer, n = 34,954
−0.0001
All Seasons, n = 50,838
Centers for Disease Control and Prevention Mortality with P&I
.10.
d
.05,
c
P < b.001,
a Number of long-stay (>100 days) fee-for-service (FFS) residents hospitalized from a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
Model controlled for linear trends for calendar years, nursing home, and city, and is weighted by the number of nursing home residents at risk for hospitalizations and deaths for each week.
P&I = pneumonia and influenza.
B match rate
A/H3N2 match rate
Influenza Strain
FFS Long-Stay Mortality
Average Hospitalization Ratea with P&I
Estimated Effects on Mortality and Hospitalization Outcomes for a 1-Percentage-Point Improvement in the Match Rate Between the Circulating Influenza and Vaccine Strains
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Author Manuscript 0.0005
−0.0018
Nonsummer, n = 713,819
Nonsummer, n = 713,819 −0.0055b
All Seasons, n = 1,034,589 −0.00358c 0.0009
All Seasons, n = 1,034,589 0.0008
Nonsummer, n = 713,819
Average Hospitalization Ratea without P&I
−0.0031d
All Seasons, n = 50,838 −0.0049b
Nonsummer, n = 34,954
.10.
.05,
d
c
P < b.01,
Number of long-stay (>100 days) fee-for-service (FFS) residents hospitalized from a given facility in a given week divided by total number of long-stay FFS residents in that facility in corresponding week multiplied by 1,000.
a
Model controlled for linear trends for calendar years, nursing home, and city and was weighted by the number of nursing home residents at risk for hospitalizations and deaths for each week.
P&I = pneumonia and influenza.
Vaccination rate
All Seasons, n = 1,034,589
FFS Long-Stay Mortality
Average Hospitalization Ratea with P&I
Centers for Disease Control and Prevention Mortality with P&I
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Estimated Effect of Vaccination Rate on Mortality and Hospitalization Outcomes
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Table 3 Pop-Vicas et al. Page 15
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