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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.
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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.
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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 description||Predictor value||Reference value|
|Sex of the child||Female||Male|
|Level of education of the caregiver||Least educated||Not least educated|
|Marital status of the caregiver||Alone||In couple|
|Tetanus toxoid (TT) vaccination status of the caregiver||<2 TT doses||2 or more TT doses|
|Caregiver’s decision when child ill||Caregiver does not decide alone||Caregiver decides alone|
|Sex of the head of the household||Female||Male|
|Partner’s education||Least educated||Not least educated|
|Household members||Above median||Below median|
|Number of offspring in the household||Above median||Below median|
|Number of offspring dead||Above median||Below median|
|Birth order of the child||First birth||Younger|
| ||First birth||2nd born|
|Area of residence||Rural||Urban|
|Radio ownership||No radio in the household||Radio in the household|
|Television ownership||No TV in the household||TV in the household|
|Religion||Minority groups||Majority group|
|Ethnic group||Minority groups||Majority group|
|Wealth index||Poorest quintile||2nd quintile|
| ||Poorest quintile||3rd quintile|
| ||Poorest quintile||4th quintile|
| ||Poorest quintile||5th quintile|
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 namea||Oldest and most recent||Unvaccinated (%)||Annual change (%)||Partially vaccinated (%)||Annual change (%)|
|Year 1||Year 2||Year 1||Year 2||Year 1||Year 2|
|Bosnia and Herzegovina||2000||2006||4.8||1.2||−0.6||s||19.8||38.8||3.2||s|
|Central African Republic||1994||2000||16.2||17.9||0.3||ns||55.2||67.5||2.1||ns|
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.
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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.