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Socioeconomic inequalities in non- coverage of full vaccination among children in Bangladesh: a comparative study of Demographic and Health Surveys, 2007 and 2017–18

Abstract

Background

Vaccination is considered as a powerful and cost-effective weapon against many communicable diseases. An increase in full vaccination among the most vulnerable populations in Bangladesh was observed in the last decade. This study aimed to capture the socioeconomic inequalities in non-coverage of full vaccination among children aged 12–23 months using the nationally representative data from the Bangladesh Demographic and Health Surveys (BDHS).

Methods

Data for this study have been drawn from the 2007 and 2017–18 BDHS, which covered 10,996 and 20,127 ever-married women aged 15–49 years in 2007 and 2017–18, respectively. Binary logistic regression analysis was performed to find the factors associated with children who did not receive full vaccination. Further, the concentration index was used to observe the socioeconomic inequality for the outcome variable.

Results

The proportion of children who did not get fully vaccinated decreased by more than 6 points (18.2 percent to 11.8 percent) between the years 2007 and 2017–18. In 2017–18, the odds of children who were not fully vaccinated were 58 percent and 53 percent less among mothers who had primary education in 2007 [adjusted odds ratio (AOR): 0.42; confidence interval (CI): 0.24–0.73] and 2017–18 [AOR: 0.47; CI: 0.23–0.94] respectively, compared to mothers with no education. The inequality for children who were not fully vaccinated had declined between two survey periods [concentration index (CCI) value of − 0.13 in 2007 and -0.08 in 2017–18]. The concentration of inequality in children with higher parity who did not receive full vaccination had increased from 5 percent in 2007 to 16.9 percent in 2017–18. There was a drastic increase in the socioeconomic inequality contributed by place of delivery from 2.9 percent (2007) to 60.5 percent (2017–18) among children who did not receive full vaccination.

Conclusions

The present study provide eminent evidence that non-coverage of full vaccination is more prevalent among children from poor households in Bangladesh, which is mainly associated with factors like mother’s education, father’s education and working status and household wealth index across the two rounds. These factors suggest multifaceted pro-poor interventions that will protect them from hardship and reduce their socioeconomic inequalities in coverage of full vaccination.

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Background

While inequality between countries has been declining, achieving Sustainable Development Goals (SDGs) is a major obstacle in poor resource settings due to within-country inequality in health [1]. Substantial progress has been made globally in the reduction of child mortality as a Millennium Development Goal [2]. Various evidence has shown that vaccination coverage at the recommended ages and intervals ensures that children are adequately protected from target diseases at all-times such as encephalitis [3]. Importantly, vaccination is considered as a powerful and cost-effective weapon against many of communicable diseases. Although the utmost priority was given to developing countries because of the higher prevalence of diseases and inadequate service delivery for immunisation, most of the unvaccinated children live in these countries [4,5,6].

A growing body of literature suggests that the socioeconomic inequalities still remain a major barrier to childhood vaccination coverage in low- and middle-income countries [7, 8]. Studies showed an absolute inequality in childhood vaccination coverage which is advantageous for male and urban residing children in the south and southeast regions [9,10,11]. Factors that hinder achieving complete childhood vaccination coverage include inadequacy of health services, lack of parental education, poverty, and traditional beliefs in society [12,13,14]. Evidence suggests that parental education, work status and household economic status are the most influential factors in reducing childhood vaccination inequality in poor societies [15,16,17]. Studies based on the Demographic and Health Surveys (DHS) also showed that full immunization was associated with the child’s age, sex and mother’s education and wealth [18,19,20]. Additionally, children of mothers with lower parity and higher birth interval of a preceding child in multiple studies were found to be more likely to be vaccinated [21, 22].

An encouraging increase in full vaccination among the most vulnerable populations in Bangladesh was observed in the last decade [23]. However, despite years of health and medical advancement, children still contact infectious diseases and inequalities in vaccination coverage and uptake persist, and deaths from vaccine-preventable diseases remain high in some parts of the country [24]. An earlier study revealed that almost half of the deaths occurring among children from poor socioeconomic groups could be prevented through immunisation alone [22]. The epidemiological studies conducted in this setting show an increased burden of infectious diseases observed in the impoverished parts like urban slums and poor regions of the country [25]. Also, in another study, full coverage of childhood vaccination was found to be positively associated with urban residence and media exposure [22].

Facility-based delivery has substantially increased in Bangladesh in the last decades, and a window of opportunity has been opened to raise the vaccination coverage [26, 27]. Since evidence shows that an age-appropriate vaccination raises the probability of children’s school enrolment [27], a better understanding of the determinants of inequalities in vaccination coverage is crucial for health intervention strategies. Although a couple of studies have reported findings on childhood vaccination, few of them have generated evidence on the inequalities in vaccination coverage in low and middle-income countries [7, 17, 28]. This study aimed to capture the socioeconomic inequalities in non-coverage of full vaccination status among children aged 12–23 months using the nationally representative DHS datasets.

Methods

Data

The study used data from the 2007 Bangladesh Demographic and Health Survey (BDHS) and 2017–18 BDHS, which are nationally representative surveys conducted by the National Institute for Population Research and Training (NIPORT) of the Ministry of Health and Family Welfare [29, 30]. The 2007 BDHS survey used the sampling frame provided by the list of census enumeration areas (EAs) with population and household information from the 2001 Population Census. Bangladesh is divided into six administrative divisions. In turn, each division is divided into zilas, and each zila into upazilas. Rural areas in an upazila are divided into union parishads (UPs), and UPs are further divided into mouzas. Urban areas in an upazila are divided into wards, and wards are subdivided into mahallas. EAs from the census was used as the Primary Sampling Units (PSUs). The survey is based on a two-stage stratified sample of households. At the first stage of sampling, 361 PSUs were selected. The selection of PSUs was done independently for each stratum and with probability proportional to PSU size in terms of a number of households. The urban areas of each division were further subdivided into three strata: statistical metropolitan areas (SMAs), municipality areas, and other urban areas. In all, the sample consisted of 22 strata because Barisal and Sylhet do not have SMAs. The 361 PSUs selected in the first stage of sampling included 227 rural PSUs and 134 urban PSUs. On average, 30 households were selected from each PSU, using an equal probability systematic sampling technique. A total of 10,996 ever-married women aged 15–49 from 10,400 households were interviewed with a response rate of 98.5% and 99.5%, respectively.

The 2017–18 survey used a sampling frame from the list of enumeration areas (EAs) of the 2011 Population and Housing Census of the People’s Republic of Bangladesh, provided by the Bangladesh Bureau of Statistics (BBS). The primary sampling unit (PSU) of the survey is an EA with an average of about 120 households. The survey is based on a two-stage stratified sample of households. In the first stage, 675 EAs (250 in urban areas and 425 in rural areas) were selected with probability proportional to EA size. In the second stage of sampling, a systematic sample of an average of 30 households per EA was selected to provide statistically reliable estimates of key demographic and health variables for the country as a whole, for urban and rural areas separately [31]. A total 20,127 ever-married women aged 15–49 years from 19,457 households were interviewed with a response rate of 98.8% and 99.4%, respectively.

The BDHS obtained detailed information on fertility levels, marriage, fertility preferences, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, and knowledge and attitudes regarding HIV/AIDS and other sexually transmitted infections. The detailed information on the survey is given elsewhere [30]. The effective sample size for the current analysis was 1157 (2007) and 1660 (2017–18) women aged 15–49 years who had given birth at home or a health facility during three years preceding the survey.

Variable description

Outcome variable

The outcome variable was whether the child received all basic vaccination or not. Full vaccination includes one dose of BCG against tuberculosis, three doses of DPT (diphtheria, whooping cough, and tetanus), three doses of oral polio vaccine and one dose of measles vaccine among 12–23 months children [32]. The variable was coded as 0 “received full vaccination” and 1 “did not receive full vaccination”. The DHS collected the full vaccination status of children from the two sources. Primarily immunisation record cards were provided by mothers, but if these were absent in the DHS the data collectors used mothers’ verbal reports of children’s immunisation status [33].

Equity stratifier

The wealth index was the equity stratifier in the present study. The wealth index has a natural ordering and known as an ordered stratifier used in several socioeconomic-related inequality studies and has a high predictive value in low and middle-income countries [34, 35]. It was coded as poorest, poorer, middle, richer, and richest [32, 34]. Households were given scores based on the number and kinds of consumer goods they own, ranging from a television to a bicycle or car, and housing characteristics such as the source of drinking water, toilet facilities, and flooring materials. These scores are derived using principal component analysis. National wealth quintiles are compiled by assigning the household score to each usual (de jure) household member, ranking each person in the household population by their score, and then dividing the distribution into five equal categories [32, 34].

Explanatory variable

The study added the explanatory variables based on the literature available [8, 23, 36]. The sex of the child was coded as male and female, the mothers’ age was coded as 15–19, 20–14, 25–29 and 30 + years, mothers’ and fathers’ educational status was coded as not educated, primary, secondary and higher. Mothers’ and Fathers’ working status was coded as not working and working. Media exposure which includes exposure to television, radio and newspaper, was coded as exposed to anyone and not exposed [37]. Parity was coded as 1, 2, 3 and 4 and above. Preceding childbirth interval was coded as less than 24 months, 24–36 months and more than 36 months [33, 38]. Antenatal care visits were coded as no visit, 1–3 visits and 4 or above visits [38]. Postnatal care within two days of delivery was coded as no and yes [33]. The place child delivered was coded as home and health facility.

Religion was coded as Islam and others, and residential status was coded as urban and rural. Administrative divisions were provided in the survey as Barisal, Chittagong, Dhaka, Khulna, Rajshahi and Sylhet. To be noted in Bangladesh Demographic and Health Survey 2017–18 had eight administrative divisions, namely Barisal, Chittagong, Dhaka, Khulna, Mymensingh Rajshahi, Rangpur and Sylhet. For analytical reasons, Mymensingh was merged in Dhaka and Rangpur was merged in Rajshahi as these were divided from these regions at certain points after 2004.

Statistical analysis

Descriptive (percentage) along with bivariate analysis was used for carving out preliminary results. The Chi-square test was used to resemble the significance level (p-values) during bivariate association. Along with that, binary logistic regression analysis [39] was performed to estimate the association between outcome and explanatory variables.

Concentration Index (CCI)

The concentration curve is obtained by plotting the cumulative proportion of outcome variables (vaccination status) on y-axis against the increasing percentage of the population ranked by the socioeconomic indicator (wealth index) on x-axis. The curves show that whether the socio-economic status related inequality in the outcome variable (on x-axis) prevails or not [32, 37]. If the curve is above the line of equality (45 degree line) that means the index value is negative; hence it shows that the outcome variable is disproportionally concentrated among the poor and vice-versa [32, 37]. Income-related inequality in the vaccination status was measured by the concentration index (CCI) and the concentration curve (CC), using the wealth score as the socioeconomic indicator and binary outcome as vaccination status [32, 37]. The concentration index is defined as twice the area between the concentration curve and the line of equality. The concentration index measures the inequality of one variable (vaccination status) over the distribution of another variable (wealth index) [40]. The index ranges from -1 to + 1, where the index value of 0 (zero) shows no socioeconomic inequality [40]. However, the positive value of the index shows pro-rich inequality and vice-versa. Additional on either scale higher the value, the higher the extent of socioeconomic inequality.

CCI (concentration index), WI (Wagstaff's index), and EI (Erreygers index) are all binary variables that condition the level of absolute inequality on the most unequal society, although their definitions of that state differ [41]. CCI responds to the issue of how far the society has progressed from a state in which the wealthiest individual owns all of the society's health units (without considering the upper bound of the variable) [41]. WI and EI, on the other hand, acknowledge the boundedness of the health variable; WI answers the question of how far the society is from a state where only the top half of the income distribution is healthy, regardless of prevalence, while EI answers the question of how far the society is from a state where only the top half of the income distribution is healthy, regardless of prevalence [41].

The study used Wagstaff decomposition analysis to decompose the concentration index. Wagstaff’s decomposition demonstrated that the concentration index could be decomposed into the contributions of each factor to the income-related inequalities [42]. For any linear regression model on health outcome (y) (vaccination status), such as.

$$y=\alpha +{\sum}_k{\beta}_k{x}_k+\varepsilon$$
(1)

The concentration index for y, C, can be written as follows,

$$C={\sum}_k\left({\beta}_k{\overline{x}}_k/\mu \right){C}_k+G{C}_{\varepsilon }/\mu$$
(2)

Where \(\mu\) is the mean of y, \({\overline{x} }_{k}\) is the mean of \({x}_{k}\), \({C}_{k}\) is the concentration index for \({x}_{k}\) (defined analogously to C), and \(G{C}_{\varepsilon }\) is the generalized concentration index for the error term (\(\varepsilon )\). Equation (2) shows that C is equal to a weighted sum of the concentration indices of the k regressors, where the weight for \({x}_{k}\) is the elasticity of y with respect to \({x}_{k}\) \(\left({\eta }_{k}= {\beta }_{k}\frac{{\overline{x} }_{k}}{\mu }\right)\). The residual component captured by the last term reflects the socioeconomic status related inequality in health that is not explained by systematic variation in the regressors by income, which should approach zero for a well-specified model [32]. Each contribution is the product of elasticity with the degree of economic inequality [32]. Moreover, the percentage contribution is obtained by dividing each absolute contribution by total absolute contribution multiplied by 100 to obtain the estimates [32, 38]. The positive contribution indicates the role of factors in the extent of higher inequality, and the negative contribution explains the extent of reduction in inequality. Multicollinearity was assessed using variance inflation factor (VIF) [43]. Svyset command was used in STATA 14 to account for complex survey design. Further, individual weights were used to make the estimates nationally representative. STATA 14 [44] was used to analyse the dataset.

Results

Socioeconomic and demographic profile of study population (Table 1)

Table 1 Socio-demographic profile of the study population in Bangladesh, 2007 and 2017–18

Results showed that about half of the children were males in both the survey period (2007 and 2017–18). Further, a higher proportion (about 36 per cent) of mothers belonged to the age group of 20–24 years in both BDHS rounds. Moreover, the proportion of mothers with no education decreased drastically from 21.6 per cent in 2007 to 6.1 per cent in 2017–18, and the share of working mothers increased from 21 per cent to 39.2 per cent during the last one decade. Mass media exposure among mothers increased from 62.1 per cent in 2007 to 67.2 per cent in 2017–18. However, mothers with 4 + parity decreased from 20.4 per cent to 12.2 per cent during the last one decade. The proportion of mothers who visited 4 + times for antenatal care increased by 22 point and the proportion of mothers who received postnatal care increased by about 15 points during two survey periods. There was a drastic increase among mothers who gave birth in the health facility between 2007 to 2017–18 (19.9 per cent to 50.4 per cent).

The proportion of children who did not receive full vaccination (12–23 months) in Bangladesh (Table 2)

Table 2 Percentage of children who did not receive full vaccination (12–23 months) in Bangladesh, 2007 and 2017–18

It was found in Table 2 that the proportion of children who did not get fully vaccinated decreased by more than 6 points (18.2 (confidence interval (CI): 16.0–20.6) per cent to 11.8 per cent (CI: 10.4–13.6)) between 2007 and 2017–18. The percentage of children who did not receive full vaccination were higher among women with no education in both the rounds of survey (28.2 per cent in 2007 and 19.4 per cent in 2017–18). The prevalence of children who had not fully vaccinated declined among women who had no media exposure (23 per cent in 2007 and 13.7 per cent in 2017–18) or media exposure (15.2 per cent in 2007 and 10.8 per cent in 2017–18), though it was higher among an unexposed group of women. Women who did not receive antenatal care had a higher percentage of children who were not fully vaccinated in both the survey periods. Moreover, women who delivered at home had a higher percentage of children who were not fully vaccinated. Father’s education has a significant negative association with children who were not fully vaccinated. The prevalence of children who were not fully vaccinated was significantly higher in the Sylhet division of Bangladesh (29.2 per dent in 2007 and 15.7 per cent in 2017–18), though it declined during the last one decade.

Estimates from logistic regression analysis for children who did not receive full vaccination by background characteristics (2007 and 2017–18) are presented in Table 3. Pooled analysis estimates shows that the odds of not getting fully vaccine was significantly lower in 2017–18 in reference to 2007 [AOR: 0.60; CI: 0.49, 0.74]. In 2017–18, the likelihood of children who did not receive full vaccination were 46 per cent and 75 per cent less likely among mothers who belonged to 25–29 [AOR: 0.54; CI: 0.27–1.08] and 30 + years age group [AOR: 0.25; CI: 0.11–0.61], respectively compared to mothers who were in the age group of 15–19 years. With reference to uneducated mothers, the odds of children who were not fully vaccinated were 58 per cent and 53 per cent less among mothers who had primary education in 2007 [AOR: 0.42; CI: 0.24–0.73] and 2017–18 [AOR: 0.47; CI: 0.23–0.94] respectively. Similarly, in 2017–18, the likelihood of children who did not receive full vaccination was 42 per cent less among mothers who delivered at a health facility compared to those who delivered the baby at home [AOR: 0.58; CI: 0.32–0.98]. Lastly, through pooled estimates, it was found that the odds of not getting fully vaccinated was significantly lower in 2017–18 in reference to 2007 [AOR: 0.60; CI: 0.49, 0.74].

Table 3 Estimates for logistic regression analysis for children who did not receive full vaccination (12–23 months) by background characteristics in Bangladesh, 2007 and 2017–18

Socioeconomic-related inequality among children who were not fully vaccinated in Bangladesh

Figure 1 presents the concentration curve for children who were not fully vaccinated in Bangladesh during 2007 and 2017–18. The inequality had declined between the two survey periods. For instance, Bangladesh witnessed a CCI value of − 0.13 in 2007 and -0.08 in 2017–18, which depicts that children who were not fully vaccinated was mainly concentrated among those from poor households. Moreover, Erreygers and Wagstaff normalized CCI are given in Table S1 (Additional file 1).

Fig. 1
figure 1

Concentration curve for children who were not fully vaccinated in Bangladesh during 2007 and 2017–18

Table 4 represents the CI decomposition analysis results and depict how various socioeconomic and background factors of respondents contribute to the inequality among children who did not receive full vaccination in Bangladesh. The actual CCI was -0.134, whereas the calculated CCI was -0.023. The actual CCI represents the true concentration of outcome variable in higher of lower socio-economic strata; however, the calculated CCI reveals that how much does the over model explains out of total CCI. In this case, out of -0.134 (actual CCI) the model explained -0.023 (calculated CCI). Mother’s education explained 49.2 per cent (2007) and 38.4 per cent (2017–18) of socioeconomic status related inequality, whereas antenatal care explained 27.9 per cent (2007) and 40.3 per cent (2017–18) of socioeconomic status related inequality. Mother’s age was responsible for explaining 3.9 per cent and 13.2 per cent of socioeconomic status related inequality in 2007 and 2017–18, respectively. Moreover, parity explained 5% of socioeconomic related inequality in 2007, which increased to 16.9% in 2017–18. There was a drastic increase in the economic inequality contributed by place of delivery from 2.9 per cent (2007) to 60.5 per cent (2017–18) among children who did not receive full vaccine. The geographic region contributed 14.2 per cent and 3.8 per cent of the economic inequality (in poor) in the children who were not fully vaccinated in 2007 and 2017–18, respectively.

Table 4 Decomposition estimates for who did not receive full vaccination (12–23 months) by background characteristics in Bangladesh, 2007 and 2017–18

Discussion

Although the child vaccination program is considered one of the most cost-effective interventions worldwide, the present study provides strong evidence of socioeconomic gradient in non-coverage of full vaccination among Bangladeshi children. Using two rounds of BDHS, our study revealed that most of the non-coverage in full vaccination occurs in poor households over time (from 2007 to 2017–18). However, the decrement in the concentration of inequality from -0.134 in 2007 to -0.084 in 2017–18 among children, together with the pooled analysis results on non-coverage of full vaccination, suggested a reduction in inequality over time. Even if the inequality has reduced, the question arises what are the factors which are still hindering the coverage of full vaccination among children aged 12–23 months in Bangladesh. Further, the present analysis tried to decompose the socioeconomic inequalities in non-coverage of full vaccination in children to examine the significant associated factors.

The present estimates revealed that mother’s age and parity, mother’s and father’s education, antenatal care, place of delivery, preceding childbirth interval, and wealth index had a consistent positive association in explaining the socioeconomic inequality in non-coverage of full vaccination in children across the decade (2007 to 2017–18). This means that if the covariates are equally distributed among rich and poor, the socioeconomic inequality in receiving basic vaccination will be less pro-rich (i.e., favouring richer household children). Factors such as the working status of the mother, having mass media exposure among women, postnatal care of the child, residing in a rural area were also positively associated with socioeconomic inequality in 2007; however, over time (till 2017–18) these factors helped in narrowing the poor-rich gap in the non-coverage of full vaccination among children.

Mother’s age and education play a significant role in childhood vaccination. This study explicitly found that non-coverage of full vaccination was higher among mothers of lower age groups and those with lesser education. These results are consistent with the previous studies, which have shown that the younger age group mothers usually lack the knowledge of providing basic health care to their children [45]. Higher maternal education also creates a big difference in the approach of their child care as compared to those who are illiterate. We found that both mother and father education plays a prominent role in child vaccination coverage in both rounds, and these results were consistent with the Ethiopian nation's evidence [46, 47]. The present study found that 4 + antenatal visits and child delivery at the health facility were associated with the socioeconomic inequality in vaccination coverage, which is consistent with a Lancet study [48]. The study shows that mothers accessibility to antenatal-care services and proper care at the time of delivery helps them seek information for their child's health and record the child's birth status by health professionals. This, in return, helps the professionals to track the health and nutrition of mother and child.

Although media exposure among women had helped uplift the health of their children and family, the dilemma is that most of the women in Bangladesh have not even access to basic media [49]. This brings the disadvantage of providing them basic knowledge of protecting their child through vaccination by mass media sources. Before the era of growing mass media networks, mothers were provided knowledge and awareness face-to-face and through direct consultation. However, with the passing years, a shift in communication has been noticed worldwide. Such shift may affect the direct consultancy of women to health professionals who still lag in receiving simple media information. The results of the present study had shown the positive association of mass media exposure on socioeconomic inequality in vaccine uptake in 2007, but this was not the case in 2017–18. This contradicts a study from Ethiopia where radio and television exposure had favoured the vaccination uptake [50]. This brought the importance of mass media exposure to build knowledge about health over time in Bangladesh.

The household wealth quintile was highly associated with inequality in non-coverage child full vaccination. However, it has also been noticed that children from poor wealth quintile households witnessed higher vaccination prevalence. This may be due to high poverty-centered programs to uplift their health and survival status [51]. However, the rural children remain more disadvantaged than their urban counterparts. This could be explained by the poor basic services and transportation facilities across rural areas, restricting the family from taking their child for vaccination. The lack of basic vaccination among rural children is consistent with few previous studies [23, 52]. The geographical location of Bangladesh is also found to be associated with the socioeconomic inequality in lack of complete child vaccination. Vast inequalities across different regions were noticed in 2007 when observing the situation of lack in child vaccination and these are consistent with a 2011 Bangladesh Demographic Health Survey study [53]. Over the decade, the vaccination coverage has increased across all the regions, leaving little socioeconomic inequality in inter-regional vaccination coverage; however, it persists.

Over the decades, the prevalence of receiving full childhood immunization has increased across Bangladesh. However, there still exists some room for slackness when we talk about the universalisation of this program. Ample evidence from Bangladesh has shown different factors affecting childhood vaccination. However, limited evidence had brought forward the presence of socioeconomic inequality in non-coverage of child full vaccination across a decade (2007 to 2017–18). Moreover, the associated factors of such socioeconomic inequality are still unexplored. The present study explored the overtime dynamic of full vaccination coverage among children aged 12–23 months in Bangladesh and the prominent factors associated with such socioeconomic inequality. However, the study backs with some limitations too. The cross-sectional nature of the data does not allow us to establish causality. Moreover, recall bias of vaccination status may exist when the children do not have their vaccination cards.

Conclusion

Although a decrement in the number of children who were unable to be fully vaccinated was noticed over the decade, there still exists the need to provide knowledge and awareness about the protective effect of childhood vaccination due to socioeconomic inequalities. The present study shows pro-poor inequality in non-coverage of full vaccination among 12–23 months aged children in Bangladesh, which is mainly associated with factors like mother’s education, father’s education and working status and household wealth index across the two rounds. These factors suggest multifaceted pro-poor interventions to protect them from hardship and reduce their socioeconomic inequalities. The women’s utilization of health services during pregnancy and during or after delivery were found to be associated with the child vaccination coverage. This brings the need for coordinated policy responses at the household level, primarily focusing on providing all basic health care services to women right from the beginning. Also, there is a need for sensitization among both women and men through different modes in which face-to-face consultancy can also be provided in weaker sections of society., The changing status in inequalities (i.e., from positive to negative) across the two rounds due to mass media exposure, proper postnatal care, residing in rural areas shows the achievement of government policies focused in these areas and insists for wide interventions across all regions to benefit from such policies. Moreover, researchers should also bring knowledge to policymakers to improve such approaches over time.

Availability of data and materials

The study utilizes a secondary source of data that is freely available in the public domain through, https://dhsprogram.com/data/dataset/Bangladesh_Standard-DHS_2017.cfm?flag=1

Abbreviations

SDG:

Sustainable Development Goals

BDHS:

Bangladesh Demographic and Health Survey

NIPORT:

National Institute for Population Research and Training

EAs:

Enumeration Areas

BBS:

Bangladesh Bureau of Statistics

PSU:

Primary Sampling Unit

BCG:

Bacille Calmette-Guerin

DPT:

Diphtheria-Pertussis-Tetanus

CI:

Confidence Interval

AOR:

Adjusted Odds Ratio

CCI:

Concentration Index

CC:

Concentration Curve

References

  1. UNDP. Challenges and opportunities in the implementation of the sustainable development goals. United Nations Development Programme and United Nations Research Institute for Social Development. New York; 2017.

  2. You D, Hug L, Ejdemyr S, Idele P, Hogan D, Mathers C, et al. Global, regional, and national levels and trends in under-5 mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the un inter-agency group for child mortality estimation. Lancet. 2015;386(10010):2275–86.

    PubMed  Google Scholar 

  3. World Health Organization. Weekly epidemiological record. Geneva: WHO. 2017;92(19):241–68.

  4. Hajizadeh M. Decomposing socioeconomic inequality in child vaccination in the Gambia, the Kyrgyz Republic and Namibia. Vaccine. 2019;37(44):6609–16.

    PubMed  Google Scholar 

  5. Sarker AR, Akram R, Ali N, Sultana M. Coverage and factors associated with full immunisation among children aged 12–59 months in Bangladesh: insights from the nationwide cross-sectional demographic and health survey. BMJ Open. 2019;9(7):1–11.

    Google Scholar 

  6. Li Z, Li M, Subramanian SV, Lu C. Assessing levels and trends of child health inequality in 88 developing countries: from 2000 to 2014. Glob Health Action. 2017;10(1):1408385.

    PubMed  PubMed Central  Google Scholar 

  7. Hajizadeh M. Socioeconomic inequalities in child vaccination in low/middle-income countries: what accounts for the differences? J Epidemiol Community Health. 2018;72(8):719–25.

    PubMed  Google Scholar 

  8. Clouston S, Kidman R, Palermo T. Social inequalities in vaccination uptake among children aged 0–59 months living in Madagascar: an analysis of demographic and health survey data from 2008 to 2009. Vaccine. 2014;32(28):3533–9.

    CAS  PubMed  Google Scholar 

  9. Shenton LM, Wagner AL, Bettampadi D, Masters NB, Carlson BF, Boulton ML. Factors associated with vaccination status of children aged 12–48 months in India, 2012–2013. Matern Child Health J. 2018;22(3):419–28.

    PubMed  Google Scholar 

  10. Rainey JJ, Watkins M, Ryman TK, Sandhu P, Bo A, Banerjee K. Reasons related to non-vaccination and under-vaccination of children in low and middle income countries: findings from a systematic review of the published literature, 1999–2009. Vaccine. 2011;29(46):8215–21.

    PubMed  Google Scholar 

  11. Lauridsen J, Pradhan J. Socio-economic inequality of immunization coverage in India. Health Econ Rev. 2011;1(1):1–6.

    Google Scholar 

  12. Troeger C, Sack DA, Chao DL. Evaluation of targeted mass cholera vaccination strategies in Bangladesh: a demonstration of a new cost-effectiveness calculator. Am J Trop Med Hyg. 2014;91(6):1181–9.

    PubMed  PubMed Central  Google Scholar 

  13. Bishai D, Koenig M, Ali M. Measles vaccination improves the equity of health outcomes: evidence from Bangladesh. Health Econ. 2002;2003(419):415–9.

    Google Scholar 

  14. Sibeudu FT, Uzochukwu BSC, Onwujekwe OE. Investigating socio-economic inequity in access to and expenditures on routine immunization services in Anambra state. BMC Res Notes. 2017;10(1):1–8.

    Google Scholar 

  15. Hu Y, Liang H, Wang Y, Chen Y. Inequities in childhood vaccination coverage in Zhejiang, province: evidence from a decomposition analysis on two-round surveys. Int J Environ Res Public Health. 2018;15(9):1–12.

    Google Scholar 

  16. Vasudevan L, Labrique AB, Mehra S, Wu L, Levine O, Feikin D, et al. Maternal determinants of timely vaccination coverage among infants in rural Bangladesh. Vaccine. 2014;32(42):5514–9.

    PubMed  Google Scholar 

  17. Ataguba JE, Ojo KO, Ichoku HE. Explaining socio-economic inequalities in immunization coverage in Nigeria. Health Policy Plan. 2016;31(9):1212–24.

    PubMed  Google Scholar 

  18. Afzal N, Zainab B. Determinants and status of vaccination in Bangladesh. Dhaka Univ J Sci. 2012;60(1):47–51.

    Google Scholar 

  19. Haque SMR, Bari W. Positive role of maternal education on measles vaccination coverage in bangladesh. Int J Psychol Behav Sci. 2013;3(1):11–7.

    Google Scholar 

  20. Khan AI, Islam MS, Islam T, Ahmed A, Chowdhury MI, Chowdhury F, et al. Oral cholera vaccination strategy: self-administration of the second dose in urban Dhaka Bangladesh. Vaccine. 2019;37(6):827–32.

    PubMed  Google Scholar 

  21. Calhoun LM, Van Eijk AM, Lindblade KA, Odhiambo FO, Wilson ML, Winterbauer E, et al. Determinants and coverage of vaccination in children in western Kenya from a 2003 cross-sectional survey. Am J Trop Med Hyg. 2014;90(2):234–41.

    PubMed  PubMed Central  Google Scholar 

  22. Rahman M, Obaida-Nasrin S. Factors affecting acceptance of complete immunization coverage of children under five years in rural Bangladesh. Salud Pública México. 2010;52(2):134–40.

    Google Scholar 

  23. Boulton ML, Carlson BF, Power LE, Wagner AL. Socioeconomic factors associated with full childhood vaccination in Bangladesh, 2014. Int J Infect Dis. 2018;2018(69):35–40.

    Google Scholar 

  24. Sarker AR, Sultana M, Mahumud RA, Der Meer V, Morton A. Cost-effectiveness analysis of introducing universal childhood rotavirus vaccination in Bangladesh. Hum Vaccines Immunother. 2017;14:5515.

    Google Scholar 

  25. Chowdhury F, Khan IA, Patel S, Siddiq AU, Saha NC, Khan AI, et al. Diarrheal illness and healthcare seeking behavior among a population at high risk for diarrhea in Dhaka Bangladesh. PLoS ONE. 2015;10(6):1–14.

    Google Scholar 

  26. Manzoor S, Hanifi A, Rahman M. BCG vaccination in Bangladesh: should it be given at birth or given along with pentavalent? Int J OfEpidemiology. 2020;49:1–4.

    Google Scholar 

  27. Driessen J, Razzaque A, Walker D, Canning D. The effect of childhood measles vaccination on school enrolment in Matlab Bangladesh. Appl Econ. 2016;47:6846.

    Google Scholar 

  28. Srivastava S, Fledderjohann J, Upadhyay AK. Explaining socioeconomic inequalities in immunisation coverage in India: new insights from the fourth national family health survey (2015–16). BMC Pediatr. 2020;20(1):1–12.

    Google Scholar 

  29. National Institute of Population Research and Training, Mitra and Associates, ICF International. Bangladesh Demographic and Health Survey, 2007; 2009.

  30. NIPORT, Ministry of Health and Family Welfare, ICF. Bangladesh Demographic and Health Survey 2017–18. Dhaka: NIPORT/ICF; 2020. Available from: https://www.dhsprogram.com/pubs/pdf/FR344/FR344.pdf.

  31. National Institute of Population Research and Training, ICF International. Bangladesh Demographic and Health Survey, 2017–18; 2020.

  32. Singh SK, Srivastava S, Chauhan S. Inequality in child undernutrition among urban population in India: a decomposition analysis. BMC Public Health. 2020;20(1):1852.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Upadhyay SK, Srivastava S, Patel R, Chauhan S, Alagarajan M. Is there any link between death of preceding child and child health care services utilization for subsequent birth? Indian J Pediatr. 2020;88:363.

    PubMed  Google Scholar 

  34. Filmer D, Pritchett LH. Estimating wealth effects without expenditure data or tears: an application to educational enrollments in states of India. Demography. 2001;38(1):115–32.

    CAS  PubMed  Google Scholar 

  35. Yaya S, Zegeye B, Ahinkorah BO, Oladimeji KE, Shibre G. Inequality in fertility rate among adolescents: evidence from Timor-Leste demographic and health surveys 2009–2016. Arch Public Health. 2020;78(1):1–10.

    Google Scholar 

  36. Oleribe O, Kumar V, Awosika-Olumo A, Taylor-Robinson SD. Individual and socioeconomic factors associated with childhood immunization coverage in Nigeria. Pan Afr Med J. 2017;26:1–14.

    Google Scholar 

  37. Srivastava S, Kumar S. Does socio-economic inequality exist in micro-nutrients supplementation among children aged 6 – 59 months in India ? evidence from national family health. BMC Public Health. 2021;21:1–12.

    PubMed  PubMed Central  Google Scholar 

  38. Kumar P, Patel R, Chauhan S, Srivastava S, Khare A, Kumar PK. Does socio-economic inequality in infant mortality still exists in India? an analysis based on national family health survey 2005–06 and 2015–16. Clin Epidemiol Glob Health. 2020;9:116–22.

    Google Scholar 

  39. Osborne J, King JE. Binary logistic regression. In: Best practices in quantitative methods. California: SAGE Publications, Inc.; 2011. p. 358–84.

  40. Kumar P, Srivastava S, Chauhan S, Patel R, Marbaniang SP, Dhillon P. Associated factors and socio-economic inequality in the prevalence of thinness and stunting among adolescent boys and girls in Uttar Pradesh and Bihar, India. PLoS ONE. 2021;16(2):e0247526.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Kjellsson G, Gerdtham U-G. On correcting the concentration index for binary variables. J Health Econ. 2013;32(3):659–70.

    PubMed  Google Scholar 

  42. Wagstaff A. Socioeconomic inequalities in child mortality: comparisons across nine developing countries. Bull World Health Organ. 2000;78(1):19–28.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Miles J. Tolerance and variance inflation factor. Wiley statsref: statistics reference online; 2014.

  44. StataCorp LP. Stata 14. Coll stn statacorp LP; 2014.

  45. Amin R, Shah NM, Becker S. Socioeconomic factors differentiating maternal and child health-seeking behavior in rural Bangladesh: a cross-sectional analysis. Int J Equity Health. 2010;9(1):1–11.

    Google Scholar 

  46. Wado YD, Afework MF, Hindin MJ. Childhood vaccination in rural southwestern Ethiopia: the nexus with demographic factors and women’s autonomy. Pan Afr Med J. 2014;17(Suppl 1):9.

    PubMed  PubMed Central  Google Scholar 

  47. Sullivan M-C, Tegegn A, Tessema F, Galea S, Hadley C. Minding the immunization gap: family characteristics associated with completion rates in rural Ethiopia. J Community Health. 2010;35(1):53–9.

    PubMed  Google Scholar 

  48. Kerber KJ, de Graft-Johnson JE, Bhutta ZA, Okong P, Starrs A, Lawn JE. Continuum of care for maternal, newborn, and child health: from slogan to service delivery. Lancet. 2007;370(9595):1358–69.

    PubMed  Google Scholar 

  49. Biswas RK, Rahman N, Islam H, Senserrick T, Bhowmik J. Exposure of mobile phones and mass media in maternal health services use in developing nations: evidence from urban health survey 2013 of Bangladesh. Contemp South Asia. 2021;29(3):460–73.

    Google Scholar 

  50. Bobo FT, Hayen A. Decomposition of socioeconomic inequalities in child vaccination in Ethiopia: results from the 2011 and 2016 demographic and health surveys. BMJ Open. 2020;10(10):e039617.

    PubMed  PubMed Central  Google Scholar 

  51. Uddin MM, Chowdhury MM, Ahmad A. The impact of rural development program on poverty alleviation: a case of Bangladesh. Glob J Manag Bus Res. 2015;15(4):17–24.

  52. McGavin ZA, Wagner AL, Carlson BF, Power LE, Eboreime E, Boulton ML. Childhood full and under-vaccination in Nigeria, 2013. Vaccine. 2018;36(48):7294–9.

    PubMed  Google Scholar 

  53. Vyas P, Kim D, Adams A. Understanding spatial and contextual factors influencing intraregional differences in child vaccination coverage in Bangladesh. Asia Pac J Public Health. 2019;31(1):51–60.

    PubMed  Google Scholar 

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Authors

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The concept was drafted by SS and PK; SS and PK contributed to the analysis design; PK, TM and RR advised on the paper and assisted in paper conceptualization; TM and RR contributed to the comprehensive writing of the article. All authors read and approved the final manuscript.

Authors’ information

Shobhit Srivastava completed his M.Phil. in Population studies and currently pursuing his PhD in Population studies at International Institute for Population Sciences, Mumbai, India. His area of interest is public health issues in India.

Muhammad T completed his M.Phil. in Population studies and currently pursuing his PhD in Population studies from International Institute for Population Sciences, Mumbai, India. His area of interest include maternal and child health, health disparities among vulnerable populations and population ageing.

Rashmi Rashmi is currently a doctoral scholar at the Department of Mathematical Demography and Statistics of the International Institute for Population Sciences, Mumbai, India. She completed her post-graduation in Biostatistics and Demography from IIPS, Mumbai.

Pradeep Kumar completed his M.Phil. in Population studies and currently pursuing his PhD in Population studies from International Institute for Population Sciences, Mumbai, India. His area of interest is Maternal and Child Health, Adolescent Health Issues, and Ageing.

Corresponding author

Correspondence to Pradeep Kumar.

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Additional file 1:

 Table S1. CCI for non-coverage of full immunization of children in Bangladesh

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Srivastava, S., Muhammad, T., Rashmi, R. et al. Socioeconomic inequalities in non- coverage of full vaccination among children in Bangladesh: a comparative study of Demographic and Health Surveys, 2007 and 2017–18. BMC Public Health 22, 183 (2022). https://doi.org/10.1186/s12889-022-12555-9

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Keywords

  • Vaccination
  • Children
  • Decomposition
  • Socioeconomic
  • India