Discussion
We believe that this is the largest ever set of analyses on immunisation coverage according to ethnicity, covering 339 ethnic groups in 64 LMICs. Our results show that significant variability in no-DPT prevalence according to ethnicity was detected in more than half of the countries studied. We also showed that the largest ethnic group in each sample was not always the group with the lowest no-DPT prevalence among all ethnicities, but our pooled analyses showed that children belonging to the majority group tended to show higher coverage than all other children in the same country.
Studies from single LMICs have reported ethnic differences in immunisation coverage, as was the case in studies from China, Kenya, the Philippines and Pakistan.4–6 20 We were only able to identify one multicountry study on immunisation coverage—in this case, with three DPT doses—by ethnic group; the analyses included 16 countries from Latin America and the Caribbean and relied on data collected from 2004 to 2015.7 In three countries (Nicaragua, Panama and Paraguay), indigenous children had significantly lower coverage than the reference group composed of children of European or mixed ancestry. It should be noted, however, that immunisation coverage tends to be much higher in Latin America than in most LMICs.21 None of the studies identified in our literature search reported on no-DPT children according to ethnicity. Given the current emphasis on reaching zero-dose children, there is a clear need for such studies to guide policy.
The literature, mostly from high-income countries, suggests that while adjusting for sociodemographic variables when comparing health outcomes among ethnic groups often attenuates disparities, these still persist.22 In our own analyses, ethnic gaps did not change markedly in most countries after adjustment for maternal education, household wealth and urban–rural residence. The exceptions included Angola, Benin, Nigeria and the Philippines, where the results suggest that socioeconomic factors account for a substantial proportion of the gaps. In many countries where the gaps persisted after adjustment, ethnic-based discrimination affecting the deployment and population access to essential services may account for much of the observed disparities. These differences could also reflect subnational variations in access, as some ethnic groups are highly concentrated in specific areas. For example, in Kenya, the largest no-DPT prevalence was found among Somali children who live in the Northeast of the country, and in the Philippines, the Maranao children, who inhabit a well-delimited area of Mindanao island, show much higher no-DPT prevalence that in any other ethnic group in the country.
Our analyses have limitations, which include the use of self-reported ethnicity or proxy variables; this also applies to most studies of ethnic disparities in health.23 The way by which different ethnic groups were classified depended on the agencies that developed questionnaires for each country, which may not have used consistent approaches, as is suggested by the wide variability in the number of groups among countries. Also, many survey datasets include some groups labelled as ‘other ethnicities’; due to sample size limitations, we also included in this category additional ethnic groups with fewer than 50 children in the sample. A particular case is that of India, where the ethnicity variable included only three groups: (any) caste, no caste or tribe, and tribe, with 89.0%, 3.8% and 7.2% of all children, respectively. This classification showed that no-DPT prevalence range, from 10.2% among the former to 14.2% among the latter, but further breakdown showing the main castes would have been useful.
Our option for not reporting estimates for groups with small numbers of children has led to the omission of some potentially informative ethnic groups in some countries, for example, white ethnic groups in South Africa. In addition, there may be inconsistencies between successive surveys in some countries; for example, the Nigeria 2016 MICS recognised only four groups, whereas the 2018 DHS used in the present analyses identified 10 groups plus an other category (online supplemental table 1). One should also note that some ethnic groups, such as nomads or those living in conflict-afflicted areas, may be under-represented in the sample. An additional limitation refers to the fact that surveys included in the analyses took place over a 9-year period, although we gave preference to more recent surveys when more than one existed for the same country. For countries without recent surveys, our findings may fail to describe the current situation.
In as much as we would like to calculate summary measures of inequality in order to rank countries according to the overall magnitude of ethnic gaps, such measures tend to show higher values in countries with many ethnic groups than for countries with few groups. In our analyses, significant differences between the highest and lowest ethnic groups in terms of zero-dose prevalence (p<0.05) were observed in 45% of countries with two to three groups, 61% of those with four to eight groups, and in all but one country with nine or more groups (online supplemental table 6). This limitation affects all summary measures of inequality for unordered categories.24 25
Our analyses are limited to countries with recent surveys providing data both on ethnicity and DPT coverage. We examined surveys from over 100 countries to identify 64 that could be included in the present analyses. Whether or not our results may be generalised to other LMICs is debatable, but the fact that most countries showed significant ethnic gaps in no-DPT prevalence suggests that such inequalities may be present in countries that were not studied.
The purpose of our analyses was to present a broad picture of inequalities according to ethnic groups in access to immunisation based on recent national surveys. A detailed examination of the national contexts in which these inequalities exist is beyond the scope of the present analyses, but we hope that our results will motivate national researchers and other country actors to delve deeper into these disparities and their determinants. Further research may include an examination of the drivers of immunisation inequalities in different countries and comparisons between countries with contrasting patterns of ethnic group inequalities. Attention should also be given to investigate why some groups that cross national boundaries, such as the Fula or Baloch, often show wide differences in coverage among countries where they are present.
Ideally, equity-oriented health programming and research on health inequalities should rely on multiple stratification variables. Although wealth and educational inequalities are useful for advocacy purposes and for monitoring time trends, they are often insufficient for targeting interventions at specific groups, as the poor and uneducated may be spread throughout a country. Geographical inequalities are better suited for targeting, but within a given province or district, there may be important disparities, as is the case for large metropolitan areas. Stratification of health indicators according to ethnicity will likely contribute to existing analyses in terms of monitoring, targeting of interventions to easily defined population subgroups, and evaluating the equity impact of health services and programmes. Given that ethnicity appears to be a significant predictor of immunisation status in many LMICs, we advocate for greater attention to recording ethnicity in surveys and—in particular—in routine health information systems.
In summary, we find it astonishing that ethnicity has not been studied as an important driver of health inequalities in LMICs, particularly in terms of immunisation coverage. Ethnicity is a complex concept encompassing culture, language and ancestry, which acts as a determinant of health beliefs and behaviours.8 It also affects social cohesion and therefore the dissemination of health information. In many, if not most, countries, ethnicity drives unequal access to socioeconomic opportunities and use of public goods including health services. As such, we show that ethnicity is an important determinant of immunisation inequalities in many LMICs that should be considered in order to reach zero-dose children and the communities where they live, thus ensuring that no child is left behind.
