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HEALTH POLICY AND ETHICS |
David B. Rein, Amanda A. Honeycutt, Lucia Rojas-Smith, and James C. Hersey are with the Health, Social Science, and Economic Research unit of RTI International, Atlanta, Ga.
Correspondence: Requests for reprints should be sent to David B. Rein, PhD, RTI International, 2951 Flowers Rd, Ste 119, Atlanta, GA 30341 (e-mail: drein{at}rti.org).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSReferences |
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The Centers for Disease Control and Prevention’s Section 317 Grants Program is the main source of funding for state and jurisdictional immunization programs, yet no study has evaluated its direct impact on vaccination coverage rates. Therefore, we used a fixed-effects model and data collected from 56 US jurisdictions to estimate the impact of Section 317 financial assistance immunization grants on childhood vaccination coverage rates from 1997 to 2003.
Our results showed that increases in Section 317 funding were significantly and meaningfully associated with higher rates of vaccination coverage; a $10 increase in per capita funding corresponded with a 1.6-percentage-point increase in vaccination coverage. Policymakers charged with funding public health programs should consider this study’s findings, which indicate that money allocated to vaccine activities translates directly into higher vaccine coverage rates.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSReferences |
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In 1993, the newly created Vaccines for Children entitlement program supplanted the Section 317 program as the main source of federal vaccine purchase funding. However, Section 317 financial assistance funding remains the primary source of funding for most jurisdictional vaccine program operations. In whole or in part, Section 317 funding supports activities that (1) direct public vaccine provision; (2) oversee provider quality by conducting assessments, training programs, and compliance monitoring; (3) develop immunization registries; (4) support school-based and community-based service delivery programs; (5) create and deliver consumer information; (6) conduct vaccine-preventable disease surveillance; and (7) conduct population needs assessments.3
The US vaccination system for children comprises a set of vaccine programs that are managed at the state and jurisdictional levels and are loosely coordinated by federal program managers at the CDC. The financing and provision of vaccines for children is shared by federal, state, and private sources. This decentralized system is similar to that of other industrialized countries, such as Germany and France. In contrast, Great Britain and Finland use fully public and centralized systems in which all vaccines are purchased and distributed by the government and the government is fully responsible for all vaccination program operations. In general, centralized systems are thought to result in higher coverage rates and to be more costly to operate; however, empirical evidence to support this notion is scarce.4
Several qualitative studies in the United States have evaluated the importance of Section 317 program activities in relation to the mix of other federal, state, and private efforts to ensure adequate immunization coverage.5–7 These studies concluded that the activities and operations supported by Section 317 program funds are in fact vital to holding together the decentralized immunization system. However, no quantitative study has evaluated the independent and direct impacts of Section 317 funding on vaccination coverage outcomes.
In our study, we evaluated whether the Section 317 program can be empirically and independently associated with improved coverage outcomes. This was difficult, because Section 317 funding represents only a small portion of a large and interdependent decentralized vaccination system, and because the characteristics of each funded jurisdiction may confound statistical attempts to identify an independent effect. To overcome this difficulty, we used a fixed-effects model that controlled for the jurisdictional characteristics that might otherwise confound or obscure this association.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSReferences |
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Immunization was calculated as
 | (1) |
where i indexes the jurisdiction and t indexes the year; X denotes jurisdiction-variant and time-variant confounding variables; jurisdiction represents a matrix of jurisdiction-specific dummy variables and time trends; 
represents the residual; and ß0, ß1,, and 
represent coefficients estimated through regression.
Dependent Variable: Immunization Outcome
The 2 primary measures of program success used by the Section 317 program are outbreaks of vaccine-preventable disease and vaccination coverage rates. We only used vaccination coverage rates in our model, because outbreaks of vaccine-preventable diseases are rare and are most often caused by random factors that are beyond the control of the Section 317 program, such as disease introduction from a foreign source.9 The dependent variable was a logit transformation of the proportion of children aged 19 to 35 months who had complete 4:3:1:3:3 vaccination coverage in jurisdiction i during year t.
The 4:3:1:3:3 vaccination series (vaccination coverage) comprises 4 or more doses of any diphtheria and tetanus toxoids and pertussis vaccines, 3 or more doses of any poliovirus vaccine, 1 or more doses of measles-mumps-rubella vaccine or other measles-containing vaccine, 3 or more doses of Haemophilus influenzae type b vaccine, and 3 or more doses of hepatitis B vaccine. Vaccination coverage is a composite measure of all childhood vaccines that were recommended by the Advisory Committee on Immunization Practices (ACIP) between 1997 and 2003 for children aged 19 to 35 months, and it represents the routine vaccination schedule of most infants in the United States.1 We used the 4:3:1:3:3 series as the outcome measure rather than the more recent 4:3:1:3:3:1 series, because all vaccines in the first series were recommended by the ACIP across the entire period of observation and because the 2 series differ only by a single dose of the varicella vaccine.
Key Explanatory Variable: Section 317 Financial Assistance Funding
To ascertain the independent impact of Section 317 program funding on vaccination coverage rates, we used a measure of Section 317 financial assistance funding as the key explanatory variable. The financial assistance component of the Section 317 program accounts for approximately 80% of federal funding for vaccine program operations and activities. In contrast, Section 317 direct assistance funding, which is used primarily for vaccine purchases, accounts for only 20% of federal vaccine purchase funding, with a great deal of additional funding coming from state governments and private insurers. In short, regression models can reasonably be expected to detect an independent impact of financial assistance funding but not direct assistance funding.
We measured Section 317 financial assistance funding as financial assistance allocations in year t–1 plus unspent financial assistance funds in year t–2 divided by the number of children aged 35 months or younger. We used funding in year t–1 because activities during the previous year (t–1) were likely to affect the survey measure of coverage rates during the current year (t ). We added unspent funds from the end of year t–2 because programs were entitled to spend these monies during year t–1. We then divided the sum of total available funds by the total number of children aged 35 months or younger in year t–1 to reflect the availability of funding in each jurisdiction per child of eligible age to receive vaccinations.
We converted funding amounts in all years to 2003 dollars with the consumer price index for all urban consumers, and we rescaled the funding variable so that a 1-unit increase in the funding variable indicated a $10 increase in per capita funding. Therefore, the coefficient on funding was equal to the proportional change in coverage associated with a $10 increase in funding.
Other Independent Control Variables
We used the following time-varying independent control variables on the basis of previous research10,11: percentage of the population who had incomes at or below the federal poverty line, the percentage who had incomes at least 5 times higher than the federal poverty line (5xP), the percentage seeking employment who received some form of unemployment compensation, and the percentage of children aged 15 years or younger who had no health insurance (NOHI). We estimated coverage during the current year (t ) as a function of the value during the previous year (t–1) for each control variable. Because previous coverage rates were highly correlated with current coverage rates, we also controlled for the previous year’s vaccination coverage rate. We used a set of dummy variables to control for jurisdictional fixed effects. To allow for the possibility of different coverage trends across jurisdictions, we included the following control variables: jurisdiction multiplied by time and jurisdiction multiplied by time-squared, where time represented the number of years since 1997.
Model Estimation
We performed a logit transformation (ln[p/1–p]) on the dependent variable to avoid predicting coverage rates less than 0% or greater than 100%, and we weighted each observation to adjust for potential grouped-data bias. Weights were set to nit
it(1–it ) in accordance with the minimum logit 
2 method, where nit is the number of children aged 18 to 35 months in each jurisdiction and i is the proportion of those children who were vaccinated.12 This weighting method weighted observations by the inverse of their contribution to the variance of the error term, such that areas with many immunized children were weighted more heavily. Logit models behave similarly to linear models with respect to fixed-effects adjusters; thus, the coefficients of the variables other than the jurisdictional controls were unaffected by the jurisdictional fixed effects and therefore could be treated as consistent.13
In Table 2
, we present the logit coefficients and the average marginal effects on all values in the data. The logit coefficients are difficult to interpret because they represented the change in the log-odds of coverage associated with a 1-unit change in 1 of the independent variables. The marginal coefficients are easier to interpret because they indicated the expected change in the proportion of children who had complete coverage associated with a 1-unit change in 1 of the independent variables.
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 | (2) |
with terms as defined in equation 1 but substituting ß2 – ß5 for and the named covariates for X and with 
and 
representing the coefficients on the jurisdictional time trends. This fixed-effects model provided an estimate of the average within-jurisdiction effect of Section 317 program funding on immunization coverage rates.
This estimate is a better measure of the causal impact of the program on immunization outcomes than a measure that compares the effect across jurisdictions, because the effect across jurisdictions may be confounded by unmeasured jurisdictional characteristics. For example, if jurisdictions were awarded funding to compensate for low past-coverage rates, estimates that failed to control for fixed effects may have indicated a negative association between funding and vaccination coverage. Fixed-effects models are better for assessing whether changes in funding cause changes in vaccination coverage, because they use only the natural variation within jurisdictions to estimate the impact of changes in funding on vaccination coverage.
Data
We used annual data collected from 1995 to 2003 for all 50 states and 6 cities: Chicago, Ill; Houston, Tex; Philadelphia, Pa; New York, NY: San Antonio, Tex; and Washington, DC (data were missing from 2 observations: New York City in 1995 and Vermont in 2002). We obtained jurisdictional vaccine coverage estimates from the National Immunization Survey,14 Section 317 program funding data from published sources1 and from CDC’s National Immunization Program, and population data from the Current Population Survey, March Supplement Annual Demographics Survey (1996–2003).15
Table 1 shows the means and the standard deviations for all nondummy variables during the years included in the model, which were weighted for the size of each jurisdiction during each year. We calculated the variance inflation factors for each explanatory variable in the model to screen for problematic levels of collinearity. None of the covariates exhibited a variance inflation factor greater than 3.1, which indicated that a regression could tolerate the inclusion of all these variables in the same model.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSReferences |
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). On average, a $10 increase in funding corresponded with a proportional 1.6% increase in vaccination coverage rates. Increases in unemployment were significantly associated with increases in vaccination coverage, whereas increases in the poverty rate, the uninsured rate, and the percentage of families who had incomes at least 5 times above the poverty line were not. The previous year’s vaccination coverage was negatively and significantly associated with current vaccination coverage (P < .01).
Model Consistency With Changes in Specification and Data
Our finding of a positive and statistically significant impact of Section 317 financial assistance funding on children’s vaccination outcomes was consistent with alternative jurisdictional controls, such as no jurisdictional control, a jurisdictional control with no jurisdiction-specific time effect, and a jurisdiction-specific time effect without a nonlinear component. The effect of Section 317 funding also was positive and significant in a series of models that each omitted 1 year of data, with the exception of the model that omitted 1997 (Table 3).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSReferences |
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).
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Our study has some data limitations. The funding data consisted of allocations—as opposed to expenditures—and included no information about how funding was allocated within a jurisdiction, the dependent variable did not record the impact of funding allocated to children older than 35 months, and there was only a short panel (7 years) of usable data. However, these data limitations increased the standard error of the regression estimates and thus, made it more difficult to identify a significant effect of financial assistance funding. The fact that the results show a significant association between funding and vaccination coverage rates despite the data limitations underscores the strength of that association.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSReferences |
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When this estimated effect is applied to the US child cohort, our model predicts that 240000 additional children would have achieved full vaccination coverage in 2003 if Section 317 financial assistance funding had remained at 1997 levels. Furthermore, this considerable program impact may be conservative, because the use of the Section 317 financial assistance funding also likely enhances the effectiveness of other vaccination funding, such as monies for financing vaccines, and the model only considered the impact of funding apart from other programs.
Because of current US federal budget constraints and competing priorities for discretionary funding, funding for all public health programs, including the Section 317 program, are at risk. When considering future allocations to the Section 317 program, policymakers should also consider the strong empirical association between financial assistance funding and increased child immunization rates. Funding added to the Section 317 program clearly led to improvements in vaccination coverage rates during the time period we studied. Future reductions to this funding may hinder the capability of the United States to meet its long-term vaccination objectives.16
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Acknowledgments |
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We wish to gratefully acknowledge the cooperation and contributions of many people at the CDC’s National Immunization Program, in particular Nicole Smith, Mark Messonnier, and Bo-Hyun Cho. We thank Jeff Novey for his editorial assistance.
Human Participant Protection
No protocol approval was needed for this study.
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Footnotes |
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Contributors
D. B. Rein and A. A. Honeycutt designed the study’s analytic plan and jointly made most of the research decisions. D. B. Rein developed the data set, conducted the analytic work and wrote the article, with A. A. Honeycutt serving as the primary reviewer and editor. L. Rojas-Smith and J. C. Hersey provided feedback on drafts and obtained funding.
Accepted for publication February 25, 2006.
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONCONCLUSIONSReferences |
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2. Orenstein WA. Testimony on the immunization grant program of the PHS, 1997. Available at: http://www.hhs.gov/asl/testify/t970506a.html. Accessed July 1, 2004.
3. Brink E, Koops G, Brusuelas K, Hicks T. Immunization Program Operations Manual (IPOM). Atlanta, Ga: Centers for Disease Control and Prevention, National Immunization Program; 2000.
4. Schmitt HJ, Booy R, Weil-Olivier C, Van Damme P, Cohen R, Peltola H. Child vaccination policies in Europe: a report from the summits of independent European vaccination experts. Lancet Infect Dis. 2003;3:103–108.[CrossRef][Web of Science][Medline]
5. Hinman AR, Orenstein WA, Rodewald L. Financing immunizations in the United States. Clin Infect Dis. 2004;15:1440–1446.
6. Johnson KA, Sardell A, Richards B. Federal immunization policy and funding: a history of responding to crises. Am J Prev Med. 2000;19:99–112.[Web of Science][Medline]
7. Fairbrother G, Kuttner H, Miller W, et al. Findings from case studies of state and local immunization programs. Am J Prev Med. 2000;19:54–77.[CrossRef][Web of Science][Medline]
8. Office of Management and Budget. Performance and management assessments. Available at: http://www.whitehouse.gov/omb/budget/fy2004/pma.html. Accessed July 1, 2004.
9. Dayan G, Papania M, Redd S, et al. Epidemiology of measles, United States 2001 to 2003. MMWR Morb Mortal Wkly Rep. 2004;53:713–716.[Medline]
10. Hinman AR, Orenstein WA, Rodewald L. Childhood immunization: laws that work. J Law Med Ethics. 2002;30:122–127.[Web of Science][Medline]
11. Freed GL, Clark SJ, Pathman DE, Schectman R, Serling J. Impact of North Carolina’s universal vaccine purchase program by children’s insurance status. Arch Pediatr Adolesc Med. 1999; 153:748–754.
12. Maddala GS. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge, UK: Cambridge University Press; 1999.
13. Greene W. Fixed and random effects in nonlinear models. Department of Economics, NYU Stern School of Business. Available at: http://pages.stern.nyu.edu/~wgreene/. Accessed July 1, 2005.
14. Centers for Disease Control and Prevention, National Immunization Survey. Immunization coverage in the US: estimated vaccination coverage with individual vaccines and selected vaccination series among children 19–35 months of age by state and immunization action plan area. Available at: http://www.cdc.gov/nis/datafiles.htm. Accessed July 1, 2005.
15. Current Population Survey, Annual Demographics File (annual computer files from 1996 to 2003). Washington, DC: US Dept of Commerce, Bureau of the Census.
16. US Department of Health and Human Services. Healthy People 2010: Understanding and Improving Health. 2nd ed. Washington, DC: US Government Printing Office; 2000.
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
