|
 |
|
|||||||
|
|||||||||
© 2004 American Public Health Association
RESEARCH AND PRACTICE |
Kevin Dombkowski and Gary Freed are with the Child Health Evaluation and Research Unit, Division of General Pediatrics, University of Michigan, Ann Arbor. Gary Freed and Paula Lantz are with the Department of Health Management and Policy, School of Public Health, University of Michigan.
Correspondence: Requests for reprints should be sent to Kevin Dombkowski, DrPH, University of Michigan Division of General Pediatrics, 300 N. Ingalls, Ann Arbor, MI 48109-0456 (e-mail: kjd{at}med.umich.edu).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Objectives. We examined the associations of having health insurance and having a usual source of medical care with age-appropriate childhood vaccination.
Methods. Simulations were conducted with multivariate logistic regression models and a nationally representative sample of children to assess the likelihood of age-appropriate vaccination.
Results. Simulated provision of health insurance and a usual source of medical care produced substantial increases in the likelihood of doses being received age-appropriately. Increases in the likelihood of a child’s being up to date were also observed, but these increases typically were smaller than for age-appropriate vaccination.
Conclusions. Changes in childhood vaccination status should be assessed in age-appropriate terms, because measures of "up to date" status may not capture the effects of immunization interventions.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Healthy People 20101 established the objective of full immunization of 80% of the nation’s children aged 2 years and younger. Current vaccination surveillance data demonstrate that additional progress toward this goal is necessary, as only 74% of children aged 19–35 months have received their fourth diphtheria–tetanus–pertussis vaccine (DTP4), third poliovirus vaccine (Polio3), first measles–mumps–rubella (MMR1), third Haemophilus influenzae type b, and third hepatitis B vaccine doses (i.e., the 4:3:1:3:3 vaccination series).2 Other findings indicate that although most children ultimately complete the 4:3:1 vaccination series, remarkably few children do so by the recommended ages.3,4
The barriers to childhood vaccinations are complex and persistent. Findings from previous research indicate that vaccination status can be influenced by a multitude of factors, including population characteristics5–13 as well as numerous provider-related factors.14–18 Some of the most fundamental barriers to childhood vaccinations collectively fall under inadequate access to health services. Poor access to care is thought to be a strong deterrent to childhood vaccinations; therefore, having health insurance as well as a regular source of care are both of critical importance.15,16,18 There is ample evidence that reducing out-of-pocket costs to families—such as through insurance coverage—is an effective mechanism to increase vaccination rates.19
However, other research findings indicate that some children may have adequate health insurance yet still have incomplete vaccinations.13,20 This may be, in part, a result of the fact that having health insurance does not necessarily guarantee having a regular source of care—and having both is believed to be important. There is evidence indicating that persons not having health insurance and a usual source of medical care are far less likely to have had a physician visit in the previous year, compared with their insured counterparts who have a usual source of care.21 Having health insurance and a usual source of care has been demonstrated to influence the likelihood of children completing the 4:3:1 vaccination series22; however, the role these factors play in age-appropriate vaccination is not well understood.
We hypothesized that having health insurance and a regular source of care each increase the likelihood of age-appropriate vaccination, and having both provides the highest likelihood of a child receiving vaccinations according to the recommended schedule. We evaluated this hypothesis through a simulation in which the likelihood of age-appropriate vaccination was assessed for those with and without health insurance and for those with and without a usual source of medical care. Our analysis is based on a nationally representative sample of children, extending the generalizability of previous research conducted on populations of more limited scope. The importance of assessing childhood vaccination status in age-appropriate terms has been previously demonstrated,3,4 and therefore we consider the likelihood of vaccination from 2 perspectives: whether a child is likely to ever receive a vaccine dose (i.e., be up to date) and whether the dose is likely to be received age-appropriately.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Study Population
Data were obtained from the National Health Interview Survey (NHIS) Immunization Supplement and the NHIS Health Insurance and Access to Care Supplements. The NHIS is a cross-sectional survey conducted annually that is representative of the civilian, noninstitutionalized population of the United States. The NHIS Immunization Supplement provides in-depth immunization history information as well as detailed information on household economic indicators and demographic characteristics of the respondents (i.e., the proxy respondents for the children in the sample).23 Before 1994, the NHIS Immunization Supplement provided detailed vaccination information for 1 randomly selected child under 6 years of age within each sampled household; from 1994 onward, all children aged 19–35 months in sampled households were also included in the sample. NHIS data were obtained from public use files for the second half of 1993 through 1996. Note that during 1993, the NHIS Health Insurance and Access to Care supplements were only collected during the second half of the year.24,25 Responses from each NHIS supplement were merged by person identifier within each sampling year for a total of 23 823 responses.
Several inclusion and exclusion criteria were applied to derive the sample used in our analysis. We were particularly interested in lengthy delays potentially experienced by children, and therefore we excluded those who were not old enough to experience delays with a duration of longer than 6 months (see outcome measure description, below). As a consequence, we based our analysis of age-appropriate vaccination on children aged 25 months or older, excluding those aged 24 months or younger (n = 7858). Children with responses based on parental recall of immunization history were necessarily excluded (n = 9109) because the NHIS does not collect the date of vaccination for those responses; the precise date of vaccination for a dose is only recorded in the NHIS for responses that are based on written records. Evaluation of our hypothesis required our analysis to assess the influence of having health insurance and a usual source of medical care, and therefore, children with responses missing this information from the NHIS were not included in the statistical models (n = 340). Chi-square tests of association showed that responses with missing insurance or usual source of medical care information were significantly associated with NHIS survey year, age of respondent, and urban location (P 
.05). Responses with missing information were more likely to be from the NHIS surveys from before 1995, for children more than 72 months of age, and for those living in urban households. Missing usual source of medical care was associated with health insurance status (P < .0001); 64% of the excluded cases were missing both health insurance and usual source of medical care information. Additional tests showed that responses missing health insurance and usual source of medical care were not significantly associated with our outcome measures for vaccination delay (P 
.05). These selection criteria resulted in an unweighted sample size of 6516 children, or 27.4% of the original survey.
Outcome Measures
An assessment of age-appropriate DTP4, Polio3, and MMR1 vaccination was performed for children aged 25–72 months with the immunization schedule in effect during the data collection period. DTP4 and Polio 3 are recommended by 18 months and MMR1 by 15 months. The age at time of vaccination (in months) was determined for each vaccine dose; DTP4 and Polio3 doses received at age 19 months or later were considered to be delayed (by 1 month) whereas MMR1 doses received at age 16 months or later were considered delayed. The outcome measure of delay in months was classified into 4 categories: no delay, delay of 1–6 months, greater than 6 months’ delay, or vaccine dose not received. Vaccination status was also assessed in terms of "up to date" status; that is, a dichotomous measure as to whether a child had received the respective vaccine doses (regardless of age at time of vaccination).26 In both methods of categorizing vaccination status, absence of a date of vaccination for the DTP4, Polio3, or MMR1 doses on a written record was coded as the respective dose not being received.
Characteristics Influencing Vaccination Status
Our conceptual framework for the characteristics that influence vaccination status was based on the expanded behavioral model of health care27 and on a review of the literature on the risk factors for inadequate childhood vaccination, with a focus on characteristics that could be ascertained from the NHIS. There is an abundance of literature regarding the predisposing, enabling, and need characteristics that affect whether or not children receive recommended vaccinations5–18,28 and regarding the most effective interventions to improve vaccination rates.19,29 Accordingly, our predictor variables included factors related to child’s gender, race, ethnicity, parental education level, family size, number of parents in the household (e.g., 2-parent household), urban/rural location, poverty status, Medicaid status, and telephone ownership (i.e., owning or not owning a telephone). In addition, 2 predictors were included: 1 to indicate the presence or absence of health insurance, and another to indicate whether the child had a usual source of medical care.
Statistical Analysis
NHIS Immunization Supplement sampling weights and the corresponding sampling strata identifiers were used in all computations of standard errors and confidence intervals with Statistical Analysis System (SAS) version 8.1 (SAS Institute, Cary, NC) in conjunction with SAS-callable SUDAAN (version 8.0).30 Pearson 
2 tests of association were conducted using the SUDAAN CROSSTAB procedure to assess the degree of association between each demographic characteristic and having health insurance, as well as having a usual source of medical care (
= 0.05). Multivariate logistic regression models were estimated using the MULTILOG procedure in SUDAAN. Models were estimated for each of the 3 vaccine doses, with a multinomial outcome variable indicating the categories of vaccination delay noted above. A set of parameters were estimated for delays of 1–6 months, delays greater than 6 months, or not receiving the respective dose, using the "no delay" category as the reference. Additional details regarding the development of these multivariate models are presented elsewhere.31
The probability of vaccination delay was estimated for each of the 3 doses, using the results of the multivariate regression models. By specifying a set of demographic characteristics for a hypothetical child, the probability of vaccination delay was estimated for delays of 1–6 months, delays greater than 6 months, and not receiving the respective doses. Initially, model inputs were set to indicate a child with no health insurance or usual source of medical care; this was considered our baseline scenario. In each of 3 additional scenarios, model inputs were changed for the respective factor or factors being assessed; all other model inputs related to demographic characteristics were held constant. The 3 simulation scenarios were having health insurance, in which the health insurance parameter was set to indicate the presence of health insurance; having a usual source of medical care, in which the baseline characteristics were changed to indicate that a usual source of medical care was present; and having health insurance and a usual source of medical care, in which baseline model inputs were set to indicate that both health insurance and a usual source of care were present.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Description of the Sample
Table 1
summarizes the demographic characteristics of 6516 NHIS Immunization Supplement responses for the sample of children 25–72 months of age during the 1993–1996 study period. Health insurance status was found to be significantly associated with ethnicity, poverty status, and a usual source of medical care. Children without health insurance were typically Hispanic, lived in a household below the federal poverty threshold, and without a usual source of medical care. Similarly, usual source of medical care was significantly associated with ethnicity and poverty status, as well as health insurance status. In addition, indicators for parental education level, family size, 2-parent household, Medicaid status, and telephone ownership were each significantly associated with health insurance status; usual source of medical care was significantly associated with parental education level and telephone ownership (P .05, results not shown).
|
Simulation Exercises
Simulation results from multivariate regression models are summarized in Table 2
, leading with the baseline scenario for each vaccine dose. For each scenario, the table illustrates the rates for children not up to date and up to date; the "up to date" cases (i.e., children who have received the dose at some point) are further distinguished as being up to date with no delay, with a 1–6 month delay, or with a delay of greater than 6 months. The results illustrate that only 28% of children with the baseline set of characteristics are likely to receive the DTP4 dose age-appropriately, and over a third of this population of children is expected to have lengthy delays exceeding 6 months. Similarly, the Polio3 dose is expected to be received age-appropriately for 54% of these children, and 51% are expected to receive their MMR1 dose age-appropriately.
|
The first simulated intervention was the provision of health insurance. In this scenario, the health insurance variable in each of the predictive models was changed to indicate the presence of health insurance; all other model inputs were constant. Table 2
illustrates that the predicted effect of having health insurance has differential influences across the 3 vaccine doses. The greatest increase was observed for the MMR1 dose, with the likelihood of ageappropriate vaccination increasing from 51% to 63%. The greatest decrease in the likelihood of a delay of greater than 6 months was for the DTP4 dose, with a drop from 39% to 26%.
The second simulation evaluated the provision of a usual source of medical care. In this scenario, the model inputs were changed to indicate the presence of a usual source of medical care; all other model inputs remained constant. Table 2 illustrates the effects of this intervention, with the greatest increase in the likelihood of age-appropriate vaccination being observed for the DTP4 dose, with an expected increase from 28% in the baseline scenario to 35%. The likelihood of age-appropriate vaccination for the Polio3 dose is identical to that observed in the simulation of providing health insurance, whereas a somewhat larger increase in the likelihood of an age-appropriate MMR1 was observed. Limited reductions in the likelihood of lengthy vaccination delays of greater than 6 months were observed, compared with the provision of health insurance.
The third simulated intervention considered the combined effects of the provision of both health insurance and a usual source of medical care. In this scenario, both indicator variables were changed to indicate the presence of health insurance and a usual source of medical care; all other variable values remained as they were in the baseline scenario. Table 2 shows that this intervention has a more substantial effect on the increasing of the likelihood of age-appropriate vaccination, with the largest increase observed for each of the 3 doses. The greatest increase in the likelihood of age-appropriate vaccination was observed for the MMR1 dose, with an increase from 51% to 67%. Similarly, the probability of age-appropriate DTP4 vaccination increased from 28% to 44% and from 54% to 64% for the Polio3 dose. The greatest reduction in the likelihood of lengthy vaccination delay was observed for the DTP4 dose, with a decrease from 39% to 22%.
Figure 1 contrasts changes in the likelihood of age-appropriate vaccination versus changes in up-to-date vaccination status for each dose and simulation scenario. The figure shows that although the likelihood of being up to date increased modestly for each of the simulation scenarios, the likelihood of age-appropriate vaccination generally increased much more substantially. This is especially evident in the third simulation scenario, which showed the greatest increases in terms of "up to date" status, yet those increases were less than half the magnitude of the gains observed for age-appropriate vaccination.
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Our findings indicate that the absence of health insurance and not having a usual source of medical care are both considerable barriers to age-appropriate vaccination, and that having health insurance and a usual source of care can positively influence childhood vaccination rates. Although provision of health insurance and a usual source of care is likely to have only modest effects on up-to-date vaccination status, our findings indicate that the more substantial gains can be observed in terms of the likelihood of age-appropriate vaccination.
These findings strengthen similar evidence previously reported based on "up to date" vaccination status. Our results are nationally representative and extend previous findings that suggested absence of health insurance negatively affects vaccination status. Our findings indicate that having health insurance is associated with as high as a 13% increase in the likelihood of age-appropriate vaccination and a 2% increase for "up to date" status (for MMR1); previous findings indicate that having insurance increased the likelihood of 4:3:1 series completion by 8%.22 In another study of children living in poverty, Latino preschoolers without health insurance were found to be half as likely as their insured counterparts to be up-to-date for recommended vaccinations at 24 months of age.11 Similarly, a nationally representative study demonstrated that children who receive at least some of their vaccinations in their medical home had 1.15 times the odds of being up to date for the 4:3:1:3 series compared with their counterparts who do not.32 Other evidence indicates that continuity of care within the medical home also plays a role in "up to date" vaccination status; children with high continuity of care had 1.36 times the odds of being up to date for the MMR1 dose by 15 months compared with those with low continuity of care.33
Our results illustrate that the distinction between "up to date" and age-appropriate vaccination status is important in assessing changes in vaccination status following public health policy changes. We found that the overall "up to date" vaccination status did not change substantially with the provision of health insurance and a usual provider, yet the likelihood of age-appropriate vaccination increased considerably. Although potential increases to rates of being up to date are limited, because these rates already are relatively high, the contrast between improvements to rates of being up to date and rates of ageappropriate vaccination remains noteworthy. This finding underscores the importance of measuring vaccination status in age-appropriate terms, as the benefits of immunization interventions may be manifested in terms of children obtaining vaccination doses in a more timely manner rather than influencing whether or not children eventually receive the doses (i.e., whether or not they become up to date). Results from previous studies illustrate the importance of this nuance of vaccination status measurement in terms of health plan performance measurement and national surveillance of childhood vaccination status.3,4,26
Our findings were based on data from the period immediately before implementation of the Vaccines for Children (VFC) program, which became operational in fiscal year 1995. Although our results include data from the 1995 NHIS, the sampled children (aged older than 24 months in 1995) would have received all vaccinations before VFC. The VFC program was designed to provide free vaccines to uninsured children and to promote the provision of vaccines in a medical home.34 Similarly, other programs such as the State Children’s Health Insurance Program (SCHIP) and Medicaid expansion programs have been implemented since 1997 to expand coverage for primary care services to children. During the same period, Medicaid programs have continued to shift toward managed care enrollment, with a presumed emphasis on primary care services in a child’s medical home (a doctor/clinic where medical attention is usually sought). Although the effects of these programs cannot be directly assessed through our results, our findings indicate that the removal of insurance and medical home barriers as a consequence of programs such as VFC, SCHIP, and Medicaid managed care would be likely to positively influence age-appropriate vaccination rates.
There are several limitations to this study. The results presented here focus on the influence of having a regular source of medical care and having health insurance. There is some evidence indicating not only that a usual source of care is important but also that having a regular physician is important.21 Other evidence points to continuity of care by the same primary care physician as being an important factor in childhood immunization.33 However, the information available from the NHIS is limited to whether or not a child has a usual source of medical care and therefore does not support the assessment of the effect of having a regular physician. In addition, insurance coverage could only be ascertained as being present or absent; the NHIS data on which this study was conducted did not allow results to be adjusted based on the duration of insurance coverage or on whether interruptions to coverage were experienced.
Our findings are based on written vaccination records because the NHIS does not collect dates of vaccination for responses based on parental recall. Previously, studies have shown that incomplete parental records can have a substantial effect on assessment of vaccination status.35 Information from the National Immunization Provider Record Check Study data can be used to make adjustments for incompleteness of parental records; however, those data were not available in public use files at the time of this study. The children with written vaccination records that were included in this study had higher proportions of white race, Hispanic ethnicity, rural residency, family with 2 parents, family not living in poverty, and insurance coverage. Each of these factors is associated with reduced risk of vaccination delay.5–9,12,20,36 We believe that by excluding those who lack written vaccination records our models tend to understate the effect of having health insurance with respect to reducing the likelihood of vaccination delay.
Finally, our simulation models attempted to control for the many of the known sociodemographic characteristics associated with vaccination status. We recognize that other characteristics not completely represented in our models could be associated with having health insurance and a usual source of medical care. As a consequence, our findings may overstate the effect that eliminating barriers to health insurance and a usual source of medical care may have with respect to improving rates of age-appropriate vaccination.
Despite these limitations, our findings provide an additional perspective on measuring the influence of programs aimed at improving vaccination status for children. The results of our simulation exercises indicate that programs to improve health insurance coverage (such as SCHIP) and having a usual source of medical care (such as Medicaid managed care) may positively influence age-appropriate vaccination status, but that these advances may not be readily evident based on changes in "up to date" status. As a result, assessing programs that influence childhood vaccinations in age-appropriate terms may reveal effects that might otherwise go unnoticed.
|
Acknowledgments |
|---|
This work was supported by a grant from the Blue Cross/Blue Shield Foundation of Michigan.
Human Participant Protection
This study was approved by the University of Michigan institutional review board.
|
Footnotes |
|---|
Contributors
K. Dombkowski implemented the study, conducted the data analysis, and wrote the article. P. Lantz advised on study implementation and provided editorial input on the article. G. Freed provided input on the interpretation of data and editorial input on the article.
Accepted for publication May 15, 2003.
|
References |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
1. Healthy People 2010: Understanding and Improving Health. Washington, DC: US Department of Health and Human Services; 2001. Also available at: http://web.health.gov/healthypeople/document/. Accessed March 17, 2004.
2. Centers for Disease Control and Prevention. National, state, and urban area vaccination coverage levels among children aged 19–35 months—United States, 2002. MMWR Morb Mortal Wkly Rep.2003;52(31):728–732.[Medline]
3. Dombkowski KJ, Lantz PM, Freed GL. The need for surveillance of delay in age-appropriate immunization. Am J Prev Med.2002;23(1):36–42.[Web of Science][Medline]
4. Luman E, McCauley M, Stokely S, Chu S, Pickering L. Timeliness of childhood immunizations. Pediatrics. 2002;110(5):935–939.
5. Guendelman S, English P, Chavez G. The effects of maternal health behaviors and other risk factors on immunization status among Mexican American infants. Pediatrics. 1995;95(6):823–828.
6. Bates A, Wolinsky F. Personal, financial, and structural barriers to immunization in socioeconomically disadvantaged urban children. Pediatrics. 1998;101(4):591–596.
7. Bobo J, Gale J, Purushottam B, Wassilak S. Risk factors for delayed immunization in a random sample of 1163 children from Washington and Oregon. Pediatrics. 1993;91(2):308–314.
8. Miller LA, Hoffman RE, Baron AE, Marine WM, Melinkovich P. Risk factors for delayed immunization against measles, mumps, and rubella in Colorado two-year-olds. Pediatrics. 1994;94(2 Pt 1):213–219.
9. Schaffer S, Szilagyi P. Immunization status and birth order. Arch Pediatr Adolesc Med.1995;149:792–797.
10. Zimmerman R, Ahwesh E, Mieczkowski TA, Block B, Janosky J, Barker D. Influence of family functioning and income on vaccination in inner-city health centers. Arch Pediat Adolesc Med.1996;150:1054–1061.
11. Wood D, Donald-Sherbourne C, Halfon N, et al. Factors related to immunization status among inner-city Latino and African American preschoolers. Pediatrics. 1995;96(2):295–301.
12. Strobino D, Keane V, Holt E, Hughart N, Guyer B. Parental attitudes do not explain underimmunization. Pediatrics. 1996;98(6):1076–1083.
13. Fielding J, Cumberland W, Pettitt L. Immunization status of children of employees of a large corporation. JAMA.1994;271(7):525–530.
14. Holt E, Guyer B, Hughart N, et al. The contribution of missed opportunities to childhood underimmunization in Baltimore. Pediatrics. 1996;97(4):474–480.
15. Freed G, Bordley W, DeFriese G. Childhood immunization programs: an analysis of policy issues. Milbank Q.1993;71(1):65–97.[Web of Science][Medline]
16. National Vaccine Advisory Committee. The measles epidemic: the problems, barriers, and recommendations. JAMA. 1991;266:1547–1552.
17. Kimmel S, Madlon-Kay D, Burns I, Admire B. Breaking the barriers to childhood immunization. Am Fam Physician. 1996;53:1648–1656.[Web of Science][Medline]
18. American Academy of Pediatrics. Implementation of the immunization policy. Pediatrics. 1995;96(2):360–361.
19. Briss P, Rodewald L, Hinman A, et al. Reviews of evidence regarding interventions to improve vaccination coverage in children, adolescents, and adults. Am J Prev Med. 2000;18(1S):108.
20. Lieu T, Black S, Ray P, Chellino M, Shinefield H, Alder N. Risk factors for delayed immunization among children in HMOs. Am J Public Health. 1994;84:1621–1625.
21. Sox CM, Swartz K, Burstin HR, Brennan TA. Insurance or a regular physician: which is the most powerful predictor of health care? Am J Public Health. 1998;88(3):364–370.
22. Mayer ML, Clark SJ, Konrad TR, Freeman VA, Slifkin RT. The role of state policies and programs in buffering the effects of poverty on children’s immunization receipt. Am J Public Health. 1999;89(2):164–170.
23. US Department of Health and Human Services. National Health Interview Survey, 1995: Immunization Supplement. Hyattsville, Md: National Center for Health Statistics; 1998.
24. National Center for Health Statistics. National Health Interview Survey, 1993: Health Insurance Supplement. Hyattsville, Md: US Department of Health and Human Services; 1995.
25. National Center for Health Statistics. National Health Interview Survey, 1993: Access to Care Supplement. Hyattsville, Md: US Department of Health and Human Services; 1995.
26. Rodewald L, Maes E, Stevenson J, Lyons B, Stokely S, Szilagyi P. Immunization performance measurement in a changing immunization environment. Pediatrics. April 1999;103(4):889–897.
27. Aday L, Andersen R, Fleming G. Health Care in the US: Equitable for Whom? Beverly Hills, Calif: Sage Publications; 1980.
28. Battaglia M. Methodology of the National Immunization Survey. Cambridge, Mass: Abt Associates; 1997.
29. Szilagyi P, Bordley C, Vann J, et al. Effect of patient reminder/recall interventions on immunization rates. JAMA.2000;284(14):1820–1827.
30. Research Triangle Institute. SUDAAN User’s Manual, Release 8.0. Research Triangle Park, NC: Research Triangle Institute; 2001.
31. Dombkowski KJ, Lantz PM, Freed GL. Risk factors for delay in age-appropriate vaccination. Public Health Rep. 2004;119(2):xx.
32. Santoli JM, Rodewald L, Maes E, Battaglia M, Coronado V. Vaccines for Children Program, United States, 1997. Pediatrics. 1999;104(2):e15.
33. Christakis DA, Mell L, Wright JA, Davis R, Connell FA. The association between greater continuity of care and timely measles-mumps-rubella vaccination. Am J Public Health. 2000;90(6):962–965.
34. Institute of Medicine. Calling the Shots. Washington, DC: National Academy Press; 2000.
35. Stokely S, Rodewald L, Maes E. The impact of record scattering on the measurement of immunization coverage. Pediatrics. 2001;107(1):91–96.
36. Centers for Disease Control and Prevention. Vaccination coverage by race/ethnicity and poverty level among children aged 19–35 months—United States, 1997. MMWR Morb Mortal Wkly Rep. 1998;47(44):956–959.[Medline]
This article has been cited by other articles:
|
|
 |
|
  S. S. Kim, J. A. Frimpong, P. A. Rivers, and J. J. Kronenfeld Effects of Maternal and Provider Characteristics on Up-to-Date Immunization Status of Children Aged 19 to 35 Months Am J Public Health, February 1, 2007; 97(2): 259 - 266. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. H. Schempf, C. S. Minkovitz, D. M. Strobino, and B. Guyer Parental Satisfaction With Early Pediatric Care and Immunization of Young Children: The Mediating Role of Age-Appropriate Well-Child Care Utilization Arch Pediatr Adolesc Med, January 1, 2007; 161(1): 50 - 56. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |