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Keywords:

  • immunisation;
  • unvaccinated;
  • LMIC;
  • Demographic and Health Surveys
  • vaccination;
  • non vaccinés;
  • PRITI;
  • EDS
  • Inmunización;
  • no vacunados;
  • países con ingresos medios y bajos;
  • encuestas demográficas y sanitarias

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix

Objective  While childhood immunisation coverage levels have increased since the 70s, inequities in coverage between and within countries have been widely reported. Unvaccinated children remain undetected by routine monitoring systems and strikingly unreported. The objective of this study was to provide evidence on the magnitude of the problem and to describe predictors associated with non-vaccination.

Methods  Two hundred and forty-one nationally representative household surveys in 96 countries were analysed. Proportions and changes in time of ‘unvaccinated’ (children having not received a single dose of vaccine), ‘partially vaccinated’ and ‘fully vaccinated’ children were estimated. Predictors of non-vaccination were explored.

Results  The percentage of unvaccinated children was 9.9% across all surveys. 66 countries had more than one survey: 38 showed statistically significant reductions in the proportion of unvaccinated children between the first and last survey, 10 countries showed increases and the rest showed no significant changes. However, while 18 of the 38 countries also improved in terms of partially and fully vaccinated, in the other 20 the proportion of fully vaccinated decreased. The predictors more strongly associated with being unvaccinated were education of the caregiver, education of caregiver’s partner, caregiver’s tetanus toxoid (TT) status, wealth index and type of family member participation in decision-making when the child is ill. Multivariable logistic regression identified the TT status of the caregiver as the strongest predictors of unvaccinated children. Country-specific summaries were produced and sent to countries.

Conclusion  The number of unvaccinated children is not negligible and their proportion and the predictors of non-vaccination have to be drawn from specific surveys. Specific vaccine indicators cannot properly describe the performance of immunisation programmes in certain situations. National immunisation programmes and national and international immunisation stakeholders should also consider monitoring the proportion of unvaccinated children (i.e. those who have received no vaccines at all) and draw specific plans on the determinants of non-vaccination.

Objectif:  Bien que les taux de couverture vaccinale de l’enfance ont augmenté depuis les années 70, les inégalités dans la couverture entre et au sein des pays ont été largement rapportées. Des enfants non vaccinés demeurent non détectés par les systèmes de surveillance de routine et sont, de façon saisissante, non déclarés. L’objectif de cette étude était de fournir des preuves sur l’ampleur du problème et de décrire les facteurs prédictifs associés à la non vaccination.

Méthodes:  241 enquêtes nationales représentatives auprès des ménages dans 96 pays ont été analysées. Les proportions et les changements dans le temps des enfants «non vaccinés» (enfants n’ayant reçu aucune dose de vaccin), «partiellement vaccinés» et «complètement vaccinés” ont été estimés. Les facteurs prédictifs de la non vaccination ont été explorés ainsi que des méthodes de régression logistique.

Résultats:  Le pourcentage d’enfants non vaccinés était de 9,9% dans toutes les enquêtes. 66 pays disposaient de plus d’une enquête: 38 ont révélé des réductions statistiquement significatives dans la proportion d’enfants non vaccinés entre la première enquête et la dernière, 10 pays ont affiché des hausses et les autres n’ont montré aucun changement significatif. Cependant, alors que 18 des 38 pays ont enregistré une amélioration pour ce qui est des enfants «partiellement» et «totalement» vaccinés, dans les 20 autres pays, la proportion des enfants «complètement vaccinés» a diminué. Les facteurs prédictifs les plus fortement associés au fait d’être vaccinés étaient les suivants: l’éducation du gardien de l’enfant, l’éducation du compagnon/compagne du gardien, le statut anatoxine tétanique de la mère (AT), l’indice de richesse et le mode de participation des membres de la famille dans la prise de décision lorsque l’enfant est malade. La régression logistique multivariée a identifié le statut AT de la mère comme le facteur prédictif le plus puissant pour la non vaccination des enfants. Des résumés spécifiques aux pays ont étéétablis et envoyés à chaque pays.

Conclusion:  Le nombre d’enfants non vaccinés n’est pas négligeable et leur proportion et les facteurs prédictifs de l’absence de vaccination doivent être tirés d’enquêtes spécifiques. Les indicateurs spécifiques de vaccins ne peuvent pas décrire correctement la performance des programmes de vaccination dans certains contextes. Les programmes nationaux de vaccination et les parties prenantes dans la vaccination nationale et internationale devraient également envisager de surveiller la proportion des enfants non vaccinés (c’est-à-dire, ceux qui n’ont reçu aucun vaccin) et élaborer des plans spécifiques sur les déterminants de la non vaccination.

Objetivo:  Mientras que los niveles de cobertura vacunal infantil han aumentado desde los años 70, la inequidad en la cobertura entre y dentro de los países ha sido ampliamente reportada. Los niños sin vacunar continúan sin ser detectados por los sistemas rutinarios de monitorización, y sorprendentemente no son reportados. El objetivo de este estudio era proveer evidencia acerca de la magnitud del problema, y describir vaticinadores asociados a la no vacunación.

Métodos:  Se analizaron 241 censos nacionales realizado en hogares de 96 países. Se calcularon las proporciones y los cambios en el tiempo de niños “no vacunados” (niños que no recibieron ni una sola dosis de vacuna), “parcialmente vacunados” y “ completamente vacunados”. Se exploraron los vaticinadores del ser “no vacunado” y se utilizaron métodos de regresión logística.

Resultados:  El porcentaje de niños “no vacunados” era del 9.9% en todas los censos. 66 países tenían más de un censo: 38 mostraron una reducción estadísticamente significativa en la proporción de niños no vacunados entre el primer y el último censo; 10 países mostraron un aumento; y el resto no mostró un cambio significativo. Sin embargo, mientras que 18 de los 38 países también mejoraron en términos del número de los parcialmente y completamente vacunados, en otros 20 la proporción de los completamente vacunados disminuyó. Los vaticinadores más fuertemente asociados a no estar vacunados eran: la educación del cuidador, la educación de la pareja del cuidador, el estatus de la madre de tetanus toxoide (TT), el índice de riqueza, y el tipo de participación del miembro familiar en la toma de decisiones cuando el niño estaba enfermo. La regresión logística multivariable identificó el estatus de TT de la madre como el vaticinador más importante para los niños no vacunados. Se realizó y se envió a cada país un resumen específico de sus resultados.

Conclusión:  El número de niños no vacunados no es pequeño y su proporción y los vaticinadores de no vacunación han de sacarse de encuestas específicas. Los indicadores específicos de vacunas no pueden describir correctamente el desempeño de los programas de inmunización en ciertas situaciones. Los programas nacionales de inmunización y todas las partes interesadas en la inmunización, tanto a nivel nacional como internacional, deberían también tomar en consideración el monitorizar la proporción de niños no vacunados (es decir aquellos que no han recibido ninguna vacuna) y trazar planes específicos para los determinantes de la no vacunación.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix

Systematic international efforts to provide immunisation against major childhood diseases to all infants began in the late 1970s and early 1980s(Bland & Clements 1998). After rapid increases in coverage during the 1980s, global immunisation coverage remained stable between 1990 and 2000 at rates close to 80%. Since 2000, higher commitment to immunisation at both national and international levels led to a gradual rise in both the availability of new vaccines and in the proportion of children vaccinated (WHO, 2009).

Global achievements, however, mask substantial inter- and intra-country differences (Delamonica et al. 2005; Jones et al. 2009). In 2009, 23.3 million children under 1 year of age did not receive the third dose of Diphtheria-Tetanus-Pertussis vaccine (DTP3); 70% of those in 10 countries: Chad, China, Democratic Republic of the Congo, Ethiopia, India, Indonesia, Kenya, Nigeria, Pakistan and Uganda (WHO, 2012).

Routine vaccination monitoring and research on vaccination uptake tend to report on antigen and dose-specific vaccination rates (i.e. the proportion of children in the target population that have been vaccinated with a specific vaccine) either in terms of coverage (UNICEF, 2005) or timeliness of vaccination (Clark & Sanderson 2009). DTP3 is commonly used because it is delivered only in routine vaccination activities and it reflects the capacity of the system to engage infants in three consecutive vaccination events. Coverage expresses the proportion of targeted children who have received vaccines but does not indicate, for example, the ability of the system to deliver multiple-dose vaccines (Bos & Batson 2000); this is described by measuring the coverage of two doses of the same vaccine (e.g. DTP 1 and 3) and better described by dropout rates (i.e. the proportion of infants who received a dose of a certain vaccine but not a vaccine scheduled for an ulterior age).

One group of children has been strikingly less studied: those who received no doses of any vaccine (‘unvaccinated’)(Smith et al. 2004). This is because the proportion of unvaccinated children cannot be captured in the routine reporting system and it can only be assessed in household surveys (these are children who have never been in contact with the health system, where routine data are generated). In 2007, the WHO Strategic Advisory Group of Experts on Immunization (WHO/SAGE) requested that the WHO’s Department of Immunization, Vaccines and Biologicals undertake a ‘more detailed analysis of children who have not been reached by immunisation services’(WHO, 2008). The objective of this study was to contribute to the understanding of the factors associated with unvaccinated children as defined above by providing countries with a digested information pack on the matter.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix

The Demographic and Health Surveys (DHS) and the United Nations’ Children’s Fund (UNICEF) Multiple Indicator Cluster Survey (MICS) are nationally representative, multiple indicator household surveys. In both, probability-based, multi-stage sampling is used to select enumeration areas and households. Caregivers of children younger than 5 years are interviewed to determine children’s immunisation status (DHS Phase III, 1996; UNICEF – Childinfo, 2008).

A total of 263 DHS and MICS surveys with individual subjects’ responses were accessed. Of the 183 DHS (MEASURE-DHS) surveys, 17 were excluded: three had no relevant data for this study, six had restricted access at the time of the analysis, three were sub-national and five had no variables related to vaccination status. Of the 80 MICS surveys [44 MICS2(UNICEF – Child info, 2008) and 36 MICS3(UNICEF – Child info, 2012) datasets], five were excluded: four MICS2 and one MICS3 did not contain vaccination data. MICS1 surveys were not used because datasets were not available. A total of 241 surveys (166 DHS and 75 MICS) were included in the analyses. A list of included and excluded surveys is shown in Table 1 and countries are shown in Figure 1.

Table 1.   Predictors and their values used in these analyses
Variable descriptionPredictor valueReference value
Sex of the childFemaleMale
Level of education of the caregiverLeast educatedNot least educated
Marital status of the caregiverAloneIn couple
Tetanus toxoid (TT) vaccination status of the caregiver<2 TT doses2 or more TT doses
Caregiver’s decision when child illCaregiver does not decide aloneCaregiver decides alone
Sex of the head of the householdFemaleMale
Partner’s educationLeast educatedNot least educated
Household membersAbove medianBelow median
Number of offspring in the householdAbove medianBelow median
Number of offspring deadAbove medianBelow median
Birth order of the childFirst birthYounger
 First birth2nd born
Area of residenceRuralUrban
Radio ownershipNo radio in the householdRadio in the household
Television ownershipNo TV in the householdTV in the household
ReligionMinority groupsMajority group
Ethnic groupMinority groupsMajority group
Wealth indexPoorest quintile2nd quintile
 Poorest quintile3rd quintile
 Poorest quintile4th quintile
 Poorest quintile5th quintile
image

Figure 1.  Map showing the countries where at least one DHS or one MICS has been conducted.

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Children 12–59 months of age were included in the analyses. Twelve months of age was the lower limit because children of that age would have had the opportunity to receive all routine infant vaccines. The upper limit of 59 months was chosen to ensure a sufficiently large sample to make analyses meaningful.

Vaccines considered for the outcome variables were bacille Calmette-Guérin (BCG), any vaccine containing DTP, oral polio vaccine (OPV) and any vaccine containing measles antigen (MCV). The outcome variable was vaccination status dichotomised as children not having received any vaccination (‘unvaccinated’) vs. children having received at least one dose of any vaccine. A child was labelled as having missing vaccination status if none of the vaccines were documented as either given or not given and excluded from the analyses; as ‘unvaccinated’ when all documented vaccines were recorded as not given; and as having at least one dose, the remainder. The proportion of unvaccinated children was calculated by dividing the number of unvaccinated children by the total number of children with known vaccination status.

A second variable, ‘at least one dose’, was dichotomised as children having received at least one dose of vaccine but not being fully immunised vs. children having received all vaccines. Missing vaccination status was defined and handled as described above. A child was labelled as having had ‘at least one vaccine’ if it had at least one vaccine documented as given but not being fully vaccinated; and as ‘fully vaccinated’ if all eight vaccine doses (1 BCG, 3 DTP, 3 OPV and 1 MCV) were documented as given. Unvaccinated children were excluded. This variable provides and indication of the number and proportion of those children who having had the opportunity to have at least one contact with the vaccination programme could not be fully vaccinated (i.e. a dropout-like indicator).

In DHS and MICS, vaccination status is ascertained either by the date of vaccination recorded in the child health card, by having a mark on the card (a certain code is recorded in the dataset) or by the caregiver’s recall when the child health card was not available or incomplete. We took into account all vaccinations recorded in cards, regardless of the age at vaccination because the focus of these analyses was the access of children to (vaccination) services rather than correctness of vaccination. Compared to vaccinations recorded in cards, caregivers may forget to report a vaccination that was actually administered and documented (Valadez & Weld 1992; Langsten & Hill 1998) or conversely report that a vaccination was given when it was not actually given and not recorded in the card (George et al. 1990). Recall bias may come into play and cause differences in vaccination rates with those children whose caregivers retained the card (Suarez et al. 1997). In this study, a vaccination was considered as given if it was documented by either card or caregiver recall.

The findings of a systematic literature review were used to obtain an initial list of potential predictors. Research articles reporting on routine childhood immunisation were searched in MEDLINE (from 1966), EMBASE (from 1980), The Cochrane Library (last issue), LILACS (Latin American and Caribbean Centre on Health Science Information; 1982), RHINO literature database and the following websites: WHO (comprising WHOLIS; WHO AFRO Vaccine-Preventable Diseases; WHO/AFRO, -PAHO, -SEAR, -Europe, -EMRO, -WPRO Immunization), UNICEF, The GAVI Alliance, MEASURE DHS, The World Bank and Children’s Vaccine’s programme at PATH; and the sites of immunisation programmes of India, China, USA, Nigeria, Indonesia, Brazil, Bangladesh, Pakistan, Ethiopia and RDC. The inclusion criteria were studies on routine vaccinations in children, reporting quantitative coverage data of at least one vaccine. Of the 7784 studies retrieved, 254 studies were included. Reasons for exclusion were duplicate reports, newsletters or editorials, or not focusing on low- and middle-income countries (LMIC). The initial list of potential predictors included age and sex of the child, physical housing characteristics, ethnicity, religion, socio-economic status, place of residence, wealth, area of residence and access indicators, such as distance to health facilities. These were discussed in meetings with WHO and UNICEF staff to obtain a final list for the analyses.

For these analyses, potential predictor variables were dichotomised (values of the predictors in parentheses; the first term in the parentheses represents the value of the potential predictor for the logistic regression analyses): sex of the child (female vs. male), birth order of the child (first birth vs. subsequent births; first birth vs. the second), level of education of the caregiver (lowest level of education vs. all other education levels combined), marital status of caregiver (alone vs. in couple), tetanus toxoid (TT) vaccination status of the caregiver (<2 TT doses vs. two or more TT doses in any pregnancy), in case of child’s illness, decision-making for seeking care or treatment (caregiver does not decide or depends on other partner vs. caregiver decides, in conjunction with the partner or alone), sex of the head of the household (female vs. male), level of education of the caregiver’s partner (lowest level of education vs. all other education levels combined), ethnic and religious group (least common group vs. rest of the groups), number of household members (above the median vs. below the median), number of offspring in the household (above the median vs. below the median), offspring dead (above the median vs. below the median), area of residence (rural vs. urban), radio and television ownership (none vs. yes or more than one), wealth index (poorest vs. each one of the other four quintiles). Table 2 shows the potential predictors of the child being unvaccinated included in this study.

Table 2.   Proportion of unvaccinated children (over all children with known vaccination status) and of partially vaccinated (over all children with at least one dose of vaccine) and annual changes from the oldest to the most recent surveys for countries with at least two surveys
Country nameaOldest and most recentUnvaccinated (%)Annual change (%)Partially vaccinated (%)Annual change (%)
Year 1Year 2Year 1Year 2Year 1Year 2
  1. †Trinidad and Tobago excluded due to errors in the original dataset. ns: confidence intervals overlap; s: confidence intervals do not overlap. Confidence intervals not shown.

  2. ‡Corresponds to year 2005–2006.

Albania2000200515.50.0−3.1ns70.768.8−0.4ns
Armenia200020056.81.9−1.0s12.561.99.9s
Azerbaijan2000200610.212.40.4ns81.559.4−3.7ns
Bosnia and Herzegovina200020064.81.2−0.6s19.838.83.2s
Bangladesh1994200713.12.6−0.8s29.114.8−1.1s
Burkina Faso1993200618.10.6−1.3s49.542.1−0.6s
Burundi198720050.30.40.0ns43.763.51.1ns
Benin1996200614.58.1−0.6s36.850.41.4s
Bolivia1989200310.83.2−0.5s64.035.4−2.0s
Brazil198619965.42.0−0.3s37.220.7−1.6s
Congo DR2001200777.316.6−10.1s67.962.9−0.8s
Central African Republic1994200016.217.90.3ns55.267.52.1ns
Côte d’Ivoire1994200617.51.2−1.4s54.745.5−0.8s
Cameroon1991200623.04.6−1.2s52.859.60.4s
Colombia198620050.01.20.1s24.837.50.7s
Dominican Republic198620070.84.70.2s93.638.7−2.6s
Egypt1988200514.20.2−0.8s35.114.9−1.2s
Ethiopia1992199716.728.52.3s80.378.3−0.4s
Ghana198820061.80.3−0.1s54.136.6−1.0s
Gambia200020064.40.3−0.7s26.830.70.6s
Guinea1999200524.215.2−1.5s63.156.6−1.1s
Guatemala1987199912.45.1−0.6s55.835.1−1.7s
Guinea-Bissau200020068.81.5−1.2ns40.252.32.0ns
Guyana200020061.90.6−0.2s13.555.37.0s
Haiti1994200614.910.3−0.4ns56.851.4−0.4ns
Indonesia1991200732.09.6−1.4s36.336.50.0s
India1993200636.56.7−2.3s47.952.80.4s
Iraq200020062.11.6−0.1ns32.867.65.8ns
Jordan199020074.40.6−0.2s82.518.5−3.8s
Kenya198920030.46.10.4s27.443.21.1s
Kyrgyzstan199720050.31.00.1ns30.699.78.6ns
Comoros199620006.428.25.4s37.823.6−3.5s
Kazakhstan199520060.00.10.0s67.618.4−4.5s
Liberia198620073.512.80.4s77.165.0−0.6s
Lesotho200020048.94.3−1.2s19.731.42.9s
Morocco1987200515.40.1−0.9s35.938.50.1s
Madagascar1992200420.119.90.0ns41.632.3−0.8ns
Mali198720060.715.70.8s83.950.6−1.8s
Mongolia200020054.60.1−0.9s12.631.63.8s
Malawi199220068.80.5−0.6s21.745.51.7s
Mozambique1997200323.613.2−1.7ns39.434.6−0.8ns
Namibia199220079.23.2−0.4s40.737.2−0.2s
Niger1992200659.119.9−2.8s60.469.60.7s
Nigeria1990200343.422.6−1.6s49.782.92.6s
Nicaragua199820012.02.70.2ns19.328.53.1ns
Nepal2052206319.82.2−1.6s44.115.8−2.6s
Peru198620040.30.60.0ns56.840.1−0.9ns
Philippines1993200310.88.2−0.3s23.922.7−0.1s
Pakistan1991200631.86.0−1.7s50.043.4−0.4s
Rwanda199220057.12.8−0.3s15.323.00.6s
Sierra Leone2000200512.01.4−2.1s59.958.8−0.2s
Senegal198620053.45.20.1ns71.341.0−1.6ns
Swaziland200020062.33.30.2ns27.722.7−0.8ns
Chad1997200446.617.0−4.2s76.685.51.3s
Togo1998200613.63.5−1.3s61.855.5−0.8s
Thailand19872549‡0.00.10.0ns55.917.6−0.1ns
Tajikistan200020055.20.9−0.9s18.397.515.8s
Turkey199320046.52.1−0.4s28.944.11.4s
Tanzania199120048.64.4−0.3s26.924.7−0.2s
Uganda198820060.25.30.3s48.654.00.3s
Uzbekistan199620060.00.00.0ns19.499.78.0ns
Viet Nam199720062.91.0−0.2s43.374.53.5s
Yemen1991200630.610.9−1.3s36.181.03.0s
Zambia199220078.46.3−0.1ns30.531.90.1ns
Zimbabwe198820050.927.21.5s12.832.31.1s

Vaccination and predictor variables were thoroughly searched in all surveys, which had different names and code for the same variables, using an algorithm described elsewhere (Bosch-Capblanch 2011).

Statistical analyses were conducted using STATA/IC 10.0 for Windows (StataCorp, 2007). Coverage estimates with 95% confidence intervals (CI) were produced using the ‘svy’ STATA command to account for the complex survey designs. Odds ratios (OR) representing the likelihood of being unvaccinated for each potential predictor were obtained by simple and multivariable logistic regression analyses. Logistic regression analyses were conducted in the unique or most recent survey for each country.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix

Numbers and proportions of unvaccinated children

Two hundred and forty-one DHS and MICS surveys were conducted in 96 countries between 1986 and 2007. The total number of children between 12 and 59 months of age in all surveys with known vaccination status was 1 125 574. The overall number of unvaccinated children across all surveys and years was 111 118 (9.9%), and the median proportion of unvaccinated children was 5.3% (inter-quartile range (IQR) 1.9% to 12.4%). Figure 2 shows the distribution of the number of countries by the proportion of unvaccinated children. In the majority of the surveys (56), fewer than 5% of children were unvaccinated; in the remaining countries, the proportion of unvaccinated children ranged from 5.0% to 28.5%.

image

Figure 2.  Number of surveys by the proportion of unvaccinated children. Unique or most recent surveys. (Albania and Moldova 2000 excluded from the graphic, having no unvaccinated children).

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The proportions of unvaccinated children by country (unique or most recent survey) with 95% confidence intervals are depicted in Figure 3, with countries sorted by the magnitude of the proportion (note that the scales of the x-axes are different in the three bar charts). The 10 countries with the highest proportion of unvaccinated children were Ethiopia (in 2005, 28.5%), Comoros (in 2000, 28.2%), Zimbabwe (in 2005, 27.2%), Lao Peoples’ Democratic Republic (in 2000, 26.6%), Southern Sudan (in 2000, 26.3%), Nigeria (in 2003, 22.6%), Niger (in 2006, 19.9%), Madagascar (in 2004, 19.9%), Central African Republic (in 2000, 17.9%) and Chad (in 2004, 16.7%).

image

Figure 3.  Proportion of unvaccinated children 12–59 months of age by survey (sorted by proportion). Data from the unique or most recent survey in each country. Albania 2005 and Moldova 2000 were excluded from the graphs (no unvaccinated children).

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For those countries with more than one survey, we estimated changes in the proportion of unvaccinated children and of children with at least one dose of vaccine (Table 3) comparing the earliest and most recent surveys in each country. 48 countries experienced significant changes: 10 countries reduced the proportion of unvaccinated children with a median annual change of -0.9% (IQR: −1.4% to −0.4%); and in 38 countries, the proportion of unvaccinated children increased with a median change of 0.4% (IQR: 0.2% to 1.4%). 24 countries reduced the proportion of children with at least one dose, in favour of being fully vaccinated. The median annual change was −1% (IQR −1.8% to −0.5%); 24 others increased that proportion (i.e. less fully vaccinated), with a median change of 1.3% (IQR 0.6% to 3%) and 17 others had no significant changes.

Table 3.   Number of countries with significant changes in the proportion of unvaccinated and partially vaccinated children
UnvaccinatedPartially vaccinated*
BetterWorseTotals
  1. *Letters in parenthesis are related to Figure 4.

Better18 (a)20 (b)38
Worse6 (c)4 (d)10
Totals242448

The proportion of ‘unvaccinated’, ‘partially vaccinated’ and ‘fully vaccinated’ children can relate to each other in different ways as exemplified using dummy data in Figure 4, where the inner pie represents the baseline proportions arbitrarily set at 33% each, for illustration, and the outer doughnut represents the proportion some time later. In (b), for example, the proportion of unvaccinated children decreases while the proportion of partially vaccinated increases resulting in a smaller proportion of fully vaccinated children (i.e. the improve in non-vaccination leads to a worsening of fully vaccination). In the 48 surveys experiencing significant changes over time in the proportion of unvaccinated and partially vaccinated children, 18 improved in both indicators, 20 in only the proportion of unvaccinated, six in only the proportion of partially vaccinated (Dominican Republic from 1986 to 2007, Ethiopia from 1992 to 1997, Comoros from 1996 to 2000, Kazakhstan from 1995 to 2006, Liberia from 1986 to 2007 and Mali from 1987 to 2006) and 4 worsened in both (Colombia from 1986 to 2005, Kenya from 1989 to 2003, Uganda from 1988 to 2006 and Zimbabwe from 1988 to 2005) (Table 4).

image

Figure 4.  Four scenarios of change in the proportion of unvaccinated, partially vaccinated and fully vaccinated children. Inner pie: baseline proportions of unvaccinated, partially vaccinated and fully vaccinated children, arbitrarily set at 33% each; in the outer doughnut, the hypothetical situations some time later on.

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Table 4.   Number of surveys according to the odds ratio values (below 1, not significant around one and above one) by predictor
Predictor (reference value)Simple regressionTotal number surveys
<1=1>1
N%N%N%
  1. <1 and >1: indicates odds ratios below and above 1, respectively, with confidence intervals not containing the value 1; =1: indicates odds ratios with confidence intervals containing the value 1. The last column has the total number of surveys with data available for each predictor suitable for logistic regression analyses.

Birth order – 1st born (vs. 2nd born)003963233762
Birth order – 1st born (vs. youngest)232845325262
Education – Least educated002023667786
Education partner – Least educated12915518461
Ethnic – Minority groups10212042183848
Household members – More members684558273578
Marital status - Alone567079141689
Radio – No112130496971
Religion – Minority groups9162951193357
Sex – Female2285925592
Sex head household – Female1119417161058
Sons and daughters dead – More deaths232133416464
Sons and daughters in household – More335362303586
Television – No003139496180
Tetanus before birth – No001623537769
Wealth index – Poorest (vs. less poor)564553354185
Wealth index – Poorest (vs. moderately poor)673339465485
Wealth index – Poorest (vs. rich)332934546386
Wealth index – Poorest (vs. richest)342428586885
Child ill decide – No decides alone00413268730
Residence – Rural673743435086

Predictors of unvaccinated children

To ascertain the country-specific population characteristics of unvaccinated children and to identify possible entry points for interventions, we produced two types of summaries: (i) country-specific fact sheets containing the proportions of unvaccinated children for each value of the potential predictor variables and the OR describing the association between the potential predictors and the outcome (unvaccinated), one sheet per survey and (ii) for each predictor, OR for all countries were plotted together to illustrate achievements by country. These results are available from the SAGE/WHO website (WHO). The main findings are summarised below.

The distribution of OR (median and inter-quartile ranges) by predictor across surveys is depicted in Figure 5. The median OR (likelihood of being unvaccinated) was greater among the poorest households (as compared with the richest), children with less educated caregiver and caregiver’ partners, children of caregivers unvaccinated against TT and children of caregivers who decide alone regarding the child’s care when the child was ill. Predictors that showed no significant differences were the sex of the child, the sex of the head of the household and the number of household members.

image

Figure 5.  Distribution of OR by predictor, sorted by median OR. Data from the unique or most recent survey in each country. Mid-lines in boxes: median; lateral extremes in boxes: 20th and 75th percentiles; dots: individual surveys.

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No predictor was associated with being unvaccinated in all surveys. For example, wealth index was significantly associated with being unvaccinated in 58 surveys, 68% of those for which this variable was reported; caregiver’s education in 66 (77%) surveys, partners’ education in 51 (84%), TT vaccination status in 53 (77%) and caregiver deciding when a child is ill in 26 (87%) of surveys (note that not all surveys had data for all predictors). See Table 5 for the number of surveys according to the OR for each predictor.

Table 5.   Median odds ratios and inter-quartile ranges across surveys for each predictor (multivariable logistic regression) and both outcomes
 UnvaccinatedAt least one dose
MedianIQRMedianIQR
Education caregiver – least educated1.871.332.871.311.051.67
Education partner – least educated1.611.162.521.171.001.44
Tetanus before birth – No2.531.603.851.361.081.72
Child ill decision – decides alone2.191.493.131.331.161.61
Wealth – poorest (vs.‘less poor’)1.300.981.781.200.991.51
Wealth – poorest (vs.‘moderately poor’)1.791.002.731.341.001.77
Wealth – poorest (vs.‘rich’)1.821.003.091.581.091.95
Wealth – poorest (vs.‘richest’)2.301.045.321.731.122.66

Multivariable logistic regression was performed to account for confounding and effect modification. The independent variables were those having the strongest association with the likelihood of being unvaccinated defined as having the highest median OR in the simple logistic regression: education of the caregiver, education of caregiver’s partner, TT vaccination status of the caregiver, decision-making when child is ill and wealth index. Summary results of the multivariable logistic regression are shown in Table 6.

The TT vaccination status of the caregiver was the predictor with the highest association with being unvaccinated (OR 2.53, IQR 1.60 to 3.85). The OR of the wealth index, using the poorest quintile as reference, increased progressively with the other quintiles from the ‘less poor’ (OR 1.30, IQR 0.98 to 1.78) up to the ‘richest’ (OR 2.30, IQR 1.04 to 5.32).

The absolute magnitude of OR for the outcome ‘at least one dose’ was smaller than their equivalents in the ‘unvaccinated’ analysis. The highest OR was observed when comparing the poorest with the richest wealth quintile (OR 1.73, IQR 1.12 to 2.66).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix

Despite steady increases in vaccination coverage over the past decades (WHO, 2009), a significant number of children remain unreached by immunisation services. In responding to WHO/SAGE (WHO/SAGE), we have attempted to provide information on the characteristics of unvaccinated children in a format useful to country immunisation programme managers. Fact sheets were sent to countries as an aid for decision-making. To retain survey-specific information and to avoid giving the false impression that the described associations are global, we have avoided conducting meta-analyses.

It is striking that the study of children not having received a single dose of any vaccine has been relatively neglected by research. A number of countries have had more than 20% children receiving no vaccinations, two of them with large numbers of children under 5 years of age: Nigeria [25 776 000 children in 2010 (United Nations, 2009)] and Ethiopia [13 819 000 children in 2010 (United Nations, 2009)]. While the proportion of unvaccinated children is relatively small in the great majority of countries, there remain children who have had not a single contact with the health system resulting in a vaccination.

Reporting on a single indicator, while being a feasible and timely way to assess the performance of immunisation programmes, does not unveil serious events, such as non-vaccination, because improvements in the coverage of any subset of vaccines do not necessarily entail an increase in fully immunised children or a decrease in the proportion of unvaccinated; the proportion of unvaccinated children can improve while the proportion of fully vaccinated children can be reduced and vice versa. This has implications for performance-based funding schemes as well as programmatic planning, which are often based on a single indicator (GAVI Alliance, 2011). Common measures of immunisation system performance such as antigen-/dose-specific coverage, dropout, proportion of fully immunised and proportion of un-immunised (WHO, 1998; Vandelaer et al. 2008), while related, are actually independent measures. For example, in Ethiopia, DTP3 coverage increased between 2000 and 2005 from 56% to 69% while the proportion of unvaccinated children also increased from 16.7% to 28.5%.

Logistic regression analyses confirm that these children live in the poorest and least well-educated families. The analyses showed that predictors that were frequently and strongly associated with being unvaccinated were limited caregivers’ education, limited caregivers’ partners’ education, poor TT vaccination status of caregiver, poorest household and caregiver deciding alone about the care for the ill child. The association with TT could suggest that services are largely accessible to a sector of the population who is willing to use them, or that households may uptake health services as a whole without distinction of services or that TT immunisation has a positive effect in the subsequent uptake of childhood immunisations. However, household surveys have limited data on health services issues, such as range of activities, staff or other resources, to reach a conclusion.

Both simple and multivariable methods were used to determine the significance and magnitude of the association between potential predictors and the outcome variables. While multivariable analysis is more explanatory and provides a more precise estimates of the contribution of each individual factor associated with being unvaccinated by controlling for the contributions of other factors included in the model, simple logistic regression may be more useful in directing interventions by targeting population characteristics strongly associated with non-vaccination. The ‘diagnostic odds ratio’ has been suggested as a prevalence-independent diagnostic performance indicator (Glas et al. 2003), which allows for comparing tests (in our case, for identifying predictors) and for analysing using logistic regression models. Association with predictors was slightly different when considering unvaccinated children or children with at least one but not all doses of vaccine. Similar findings have been reported elsewhere, although the calculations of partially vaccination rates were not identical to those used here (Smith et al. 2004). Predictors were strongly associated with the fact of being unvaccinated suggesting that these children belong to more extreme situations.

Addressing some of the identified predictors require substantial resources and time; and the impact on vaccination outcomes may not be immediate (e.g. household wealth). However, we purposely included other predictors that could be useful in identifying potential interventions, such as ownership of radio or television (TV) in the household. The absence of radio or TV was strongly associated with an increase in the likelihood of being unvaccinated (in the simple and multivariable logistic regression models) and informs the use of mass media interventions to increase coverage (Grilli et al. 2002).

This analysis had several limitations. First, for some children, the vaccination status was ascertained by caregiver’s recall. A bias may be introduced overall if recall significantly differs between the different predictor groups. Furthermore, the inclusion of children who received vaccines beyond the correct vaccine schedule will have probably reduced the proportion of unvaccinated children. Therefore, our findings should be seen as a best case scenario. Secondly, data for all potential predictors were not available in all surveys. For example, the predictor ‘caregiver’s decision when child is ill’ appeared in only 30 surveys (MEASURE-DHS). Thirdly, DHS and MICS, in their different waves, were designed in slightly different ways. Although data were harmonised prior to the analyses, some inconsistencies may remain undetected. Forth, not all surveys were recent and findings may no longer be relevant in some rapidly changing countries. Finally, many potential predictors of a child receiving no vaccination are likely to be missed by multiple indicator surveys. More targeted surveys enhanced with qualitative methods are likely to provide a more complete picture of the characteristics and causes of a child being unvaccinated.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix

While routine vaccination coverage monitoring based on specific vaccines provides a feasible and timely way to ascertain the performance of immunisation programmes, serious events (such as being ‘unvaccinated’) and inequities may remain unveiled. Countries' immunisation programmes and national and international immunisation stakeholders should monitor the proportion of unvaccinated children in addition to coverage for specific vaccines. This should be performed periodically or where poor performance is suspected. Nationally representative household surveys provide evidence on those issues and can also be used to ascertain the specific factors that influence access to immunisation services. In our analyses, several factors emerged as important and the country-specific fact sheets made the findings accessible at country level to consider corrective actions.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix

We thank Bernard Brabin, Christian Schindler and Kaspar Wyss for comments on the manuscript, and Jos Vandelaer (UNICEF) for his contributions during the conceptualisation phase. Lise Beck produced Figure 1. K. Banerjee and A. Burton are staff members of the World Health Organization. The authors alone are responsible for the views expressed in this publication and they do not necessarily represent the decisions, policy or views of the World Health Organization.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix
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Appendix

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusion
  8. Acknowledgements
  9. References
  10. Appendix
Table Appendix1.   Data sets included and excluded in these analyses
 CountryYear
  1. Years are expressed according to countries' calendars which are specific in some countries (e.g. Thailand).

DHS – Included
  1Armenia2000
  2Armenia2005
  3Azerbaijan2006
  4Bangladesh1994
  5Bangladesh1996
  6Bangladesh2000
  7Bangladesh2004
  8Bangladesh2007
  9Benin1996
 10Benin2001
 11Benin2006
 12Bolivia1989
 13Bolivia1994
 14Bolivia1998
 15Bolivia2003
 16Brazil1986
 17Brazil1996
 18Burkina Faso1993
 19Burkina Faso1999
 20Burkina Faso2003
 21Burundi1987
 22Cameroon1991
 23Cameroon1998
 24Cameroon2004
 25Central African Republic1994
 26Chad1997
 27Chad2004
 28Colombia1986
 29Colombia1990
 30Colombia1995
 31Colombia2000
 32Colombia2005
 33Comoros1996
 34Congo2005
 35Congo DR2007
 36Côte D’Ivoire1994
 37Côte D’Ivoire1999
 38Dominican Republic1986
 39Dominican Republic1991
 40Dominican Republic1996
 41Dominican Republic1999
 42Dominican Republic2002
 43Dominican Republic2007
 44Egypt1988
 45Egypt1992
 46Egypt1995
 47Egypt2000
 48Egypt2003
 49Egypt2005
 50Ethiopia1992
 51Ethiopia1997
 52Gabon2000
 53Ghana1988
 54Ghana1993
  55Ghana1998
  56Ghana2003
  57Guatemala1987
  58Guatemala1995
  59Guatemala1999
  60Guinea1999
  61Guinea2005
  62Haiti1994
  63Haiti2000
  64Haiti2006
  65Honduras2006
  66India1993
  67India1999
  68India2006
  69Indonesia1991
  70Indonesia1994
  71Indonesia1997
  72Indonesia2002
  73Indonesia2007
  74Jordan1990
  75Jordan1997
  76Jordan2002
  77Jordan2007
  78Kazakhstan1995
  79Kazakhstan1999
  80Kenya1989
  81Kenya1993
  82Kenya1998
  83Kenya2003
  84Kyrgyzstan1997
  85Lesotho2004
  86Liberia1986
  87Liberia2007
  88Madagascar1992
  89Madagascar1997
  90Madagascar2004
  91Malawi1992
  92Malawi2000
  93Malawi2004
  94Mali1987
  95Mali1996
  96Mali2001
  97Mali2006
  98Mexico1987
  99Morocco1987
 100Morocco1992
 101Morocco2003
 102Morocco2005
 103Mozambique1997
 104Mozambique2003
 105Namibia1992
 106Namibia2000
 107Namibia2007
 108Nepal2052
 109Nepal2057
 110Nepal2063
 111Nicaragua1998
 112Nicaragua2001
 113Niger1992
 114Niger1998
 115Niger2006
 116Nigeria1990
 117Nigeria1999
 118Nigeria2003
 119Pakistan1991
 120Pakistan2006
 121Paraguay1990
 122Peru1986
 123Peru1991
 124Peru1996
 125Peru2000
 126Peru2004
 127Philippines1993
 128Philippines1998
 129Philippines2003
 130Rwanda1992
 131Rwanda2000
 132Rwanda2005
 133Senegal1986
 134Senegal1993
 135Senegal2005
 136South Africa1998
 137Sri Lanka1987
 138Sudan1990
 139Swaziland2006
 140Tanzania1991
 141Tanzania1996
 142Tanzania1999
 143Tanzania2004
 144Thailand1987
 145Togo1998
 146Trinidad and Tobago1987
 147Tunisia1988
 148Turkey1993
 149Turkey1998
 150Turkey2004
 151Uganda1988
 152Uganda1995
 153Uganda2001
 154Uganda2006
 155Uzbekistan1996
 156Viet Nam1997
 157Viet Nam2002
 158Yemen1991
 159Zambia1992
 160Zambia1996
 161Zambia2002
 162Zambia2007
 163Zimbabwe1988
 164Zimbabwe1994
 165Zimbabwe1999
 166Zimbabwe2005
DHS – Excluded
 167Brazil1991
 168Dominican Republic (special DHS)2007
 169Ecuador1987
 170Indonesia1987
 171Nigeria (Ondo State)1986
 172Senegal1997
 173Togo1988
 174Ukraine2007
MICS 2 – Included
   1Albania2000
   2Angola2001
   3Azerbaijan2000
   4Bosnia and Herzegovina2000
   5Bolivia2000
   6Burundi2000
   7Cameroon2000
   8Chad2000
   9Côte D’Ivoire2000
  10Comoros2000
  11Congo DR2001
  12Dominican Republic2000
  13Equatorial Guinea2000
  14Gambia2000
  15Guinea-Bissau2000
  16Guyana2000
  17Iraq2000
  18Kenya2000
  19Lesotho2000
  20Lao PDR2000
  21Madagascar2000
  22Mongolia2000
  23Myanmar2000
  24Moldova2000
  25Niger2000
  26Central African Republic2000
  27Rwanda2000
  28Sierra Leone2000
  29Sudan North2000
  30Sudan South2000
  31Sao Tome and Principe2000
  32Suriname2000
  33Swaziland2000
  34Tajikistan2000
  35Togo2000
  36Trinidad and Tobago2000
  37Uzbekistan2000
  38Venezuela2000
  39Viet Nam2015
  40Zambia1999
MICS-2 Excluded
  41Indonesia2000
  42JamaicaUnknown
  43Philippines2000
  44Senegal2000
MICS-3 Included
  1Albania2005
  2Bangladesh2006
  3Belarus2005
  4Belize2006
  5Bosnia and Herzegovina2006
  6Burkina Faso2006
  7Burundi2005
  8Cameroon2006
  9Cuba2006
 10Gambia2006
 11Georgia2005
 12Ghana2006
 13Guinea-Bissau2006
 14Guyana2006
 15Iraq2006
 16Côte D’Ivoire2006
 17Jamaica2005
 18Kazakhstan2006
 19Kyrgyzstan2005
 20Macedonia2005
 21Malawi2006
 22Mauritania2007
 23Mongolia2005
 24Montenegro2005
 25Serbia2005
 26Sierra Leone2005
 27Somalia2006
 28Syrian Arab Republic2006
 29Tajikistan2005
 30Thailand2549
 31Togo2006
 32Trinidad and Tobago2006
 33Uzbekistan2006
 34Viet Nam2006
 35Yemen2006
MICS 3 – Excluded
 36Ukraine2005