Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria

Justice Moses K Aheto; Oliver Pannell; Winfred Dotse-Gborgbortsi; Mary K Trimner; Andrew J Tatem; Dale A Rhoda; Felicity T Cutts; C Edson Utazi

doi:10.1371/journal.pone.0269066

. 2022 May 25;17(5):e0269066. doi: 10.1371/journal.pone.0269066

Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria

Justice Moses K Aheto ^1,^2,^*, Oliver Pannell ¹, Winfred Dotse-Gborgbortsi ¹, Mary K Trimner ³, Andrew J Tatem ¹, Dale A Rhoda ³, Felicity T Cutts ⁴, C Edson Utazi ¹

Editor: Tzai-Hung Wen⁵

PMCID: PMC9132327 PMID: 35613138

Abstract

Background

Substantial inequalities exist in childhood vaccination coverage levels. To increase vaccine uptake, factors that predict vaccination coverage in children should be identified and addressed.

Methods

Using data from the 2018 Nigeria Demographic and Health Survey and geospatial data sets, we fitted Bayesian multilevel binomial and multinomial logistic regression models to analyse independent predictors of three vaccination outcomes: receipt of the first dose of Pentavalent vaccine (containing diphtheria-tetanus-pertussis, Hemophilus influenzae type B and Hepatitis B vaccines) (PENTA1) (n = 6059) and receipt of the third dose having received the first (PENTA3/1) (n = 3937) in children aged 12–23 months, and receipt of measles vaccine (MV) (n = 11839) among children aged 12–35 months.

Results

Factors associated with vaccination were broadly similar for documented versus recall evidence of vaccination. Based on any evidence of vaccination, we found that health card/document ownership, receipt of vitamin A and maternal educational level were significantly associated with each outcome. Although the coverage of each vaccine dose was higher in urban than rural areas, urban residence was not significant in multivariable analyses that included travel time. Indicators relating to socio-economic status, as well as ethnic group, skilled birth attendance, lower travel time to the nearest health facility and problems seeking health care were significantly associated with both PENTA1 and MV. Maternal religion was related to PENTA1 and PENTA3/1 and maternal age related to MV and PENTA3/1; other significant variables were associated with one outcome each. Substantial residual community level variances in different strata were observed in the fitted models for each outcome.

Conclusion

Our analysis has highlighted socio-demographic and health care access factors that affect not only beginning but completing the vaccination series in Nigeria. Other factors not measured by the DHS such as health service quality and community attitudes should also be investigated and addressed to tackle inequities in coverage.

Introduction

Childhood vaccination is one of the core strategies for achieving goal 3 of the Sustainable Development Goals (SDGs) of reducing under-five mortality to less than 25/1000 live births by 2030 [1]. Immunization Agenda 2030 (IA2030) [2] has the goal of all persons fully benefiting from vaccines to improve overall health and general well-being. IA2030 preconizes extension of immunization services to regularly reach “zero-dose” (those who receive no vaccines–often approximated by those who did not receive the first dose of pentavalent vaccine (containing diphtheria-tetanus-pertussis, Hemophilus influenzae type B and Hepatitis B vaccines)–PENTA1) and under-immunized children and to advance and sustain high coverage for all vaccines across the life course.

The global challenge of getting vaccines to all children is highlighted in the case of Nigeria. Notably, in 2019 Nigeria had the highest estimated number of infants who did not receive PENTA1 and the highest number of those who did not receive MV through routine services [3].

To increase vaccine uptake and implement optimal intervention strategies to address childhood vaccination inequalities in Nigeria, factors (both enablers and barriers) that predict vaccination coverage in children should be identified and addressed in a timely manner. Several studies, especially those conducted in sub-Saharan Africa have identified different individual, household, and community level factors to be associated with childhood vaccination. These include maternal education and age, household wealth, mother’s attendance for antenatal care, skilled birth attendance, rural/urban residence, knowledge of immunization, shorter distance from vaccination site, level of partner’s support, trust in vaccines and immunization programs, lifestyle, number of children, occupation, lack of time or language barriers, and reminders to parent’s [4–15].

In this study, we develop both binomial and multinomial multilevel statistical frameworks to identify factors that predict select indicators of childhood vaccination in Nigeria to help policymakers and program managers make informed decisions aimed at improving the survival and health of children [12,13,16,17]. In particular, we investigate potential differences in factors that predict vaccination according to the strength of evidence of vaccination (documented versus recall evidence of vaccination), and examine whether factors affecting whether children start the vaccination series differ from those related to completing it, having begun.

We examine the role of geospatial environmental and climatic (geospatial) factors as well as individual, household and additional community level attributes in predicting vaccination coverage using multilevel modelling approaches which acknowledge the nested structure of the data, i.e., child, household, community and stratum levels. Although geospatial covariates have been extensively used for predicting vaccination coverage and other health outcomes in children [18–20], there is insufficient evidence on how geospatial factors combine to influence vaccination at the individual child level, especially when adjusting for other covariate effects [19,21,22]. This is one of the objectives we aim to address in this work.

Materials and methods

Data

The study used data from the nationally representative cross-sectional 2018 Nigeria Demographic and Health Survey (NDHS) which was implemented by the National Population Commission (NPC) with technical assistance provided by Inner City Fund (ICF) International through The Measure DHS Program [23]. Data collection was conducted from 14 August to 29 December 2018 with pre-test conducted from 30 April to 20 May 2018. The survey utilised a stratified two-stage sampling approach. In the first stage, 1389 enumeration areas (EAs) or clusters were selected as the primary sampling units. At the second stage, 40427 households were selected. Stratification was achieved by separating each administrative level 1 area (i.e., the 36 states and the Federal Capital Territory) into urban and rural areas, and samples were selected independently within each stratum. Detailed information on the description of the methods employed in the study is available elsewhere [23].

Outcome variables

The primary outcome variables/indicators in this study are receipt of PENTA1 vaccine (n = 6059) and receipt of PENTA3 having received PENTA1 (PENTA3/1—the converse of dropout, n = 3937) among children aged 12–23 months, and receipt of MV (n = 11839) among children aged 12–35 months. For each of the three indicators, we assessed the binomial outcome: any evidence of vaccination versus no evidence of vaccination. For each of PENTA1 and MV, we additionally assessed the multinomial outcome: no evidence of vaccination, card invalid/history evidence of vaccination and card valid vaccination, to assess potential variation in associations that could be caused by misclassifying vaccination status when a verbal history of vaccination is accepted [24] (see Table A in S1 File). Valid and invalid vaccine doses were defined according to WHO guidance [25].

Explanatory variables

Demographic and Health Survey (DHS) covariates

The study considered covariates at the child-, household- and community-levels. The selection of these covariates was informed by literature on the predictors of vaccination coverage or of child health outcomes in general, expert opinion, and availability in the 2018 NDHS or other sources, as detailed in Table A in S1 File [6,11,17,21,26–35]. The study however excluded some pre-selected DHS covariates due to missingness or multicollinearity (see supplementary materials). These variables include preceding birth interval, antenatal and postnatal care, maternal receipt of tetanus toxoid vaccination (these variables had >15% missing cases for MV and were excluded from the analyses to make the results comparable across all the three indicators), maternal decision-making (whether mother decides health care, visits, and purchases)—and region of residence. Also, among similar covariates (e.g., mother’s occupation and employment status), one was selected for inclusion in our model based on the literature and expert opinion.

Geospatial covariates

The geospatial covariates retained in this study include travel time to health facility, enhanced vegetation Index (EVI) and livestock density index. Tertiles of the distribution of these covariate were used to allow similar number of observations for each tertile. We present further description about these covariates and their relevance in Fig A and Table B in S1 File [18–22,29,36,37]. Other geospatial covariates considered included distance to conflict locations, maximum number of conflicts, night light intensity, annual aridity index, maximum temperature, annual precipitation/rainfall, proximity to national borders, water, protected areas, and slope [18–20] were excluded from final models due to multicollinearity (as was EVI) or non-significance after adjusting for DHS variables.

Data analysis

Estimation of measures of association (fixed effects)

Cross-tabulations and single-level logistic regression analysis. We tabulated each outcome against each of the selected covariates separately to explore relationships and used Chi-squared tests to determine the significance of the associations. We then fitted frequentist single level simple logistic regression models to obtain the corresponding crude odds ratios (cORs) and associated 95% confidence intervals (CI). These results were later compared with results from the multiple multilevel analyses to determine changes in statistical significance and direction of effects.

Multiple multilevel binomial regression analysis (any evidence of vaccination) and interaction effects. We fitted Bayesian multilevel [38,39] binomial regression models to estimate adjusted odds ratios (aORs) and corresponding 95% credible intervals, accounting for the hierarchical structure of the data (child/household, community, and stratum levels) and, intrinsically, the survey design (clustering and stratification) through the last two hierarchies (see Fig B in S1 File). A detailed description of the model is included in the supplementary information (S1 File).

We investigated whether child/household covariate effects could be modified by the geospatial covariates by introducing interaction terms between both sets of covariates. To incorporate the interaction terms, we first fitted the main effects model using both DHS and geospatial covariates and then introduced the interactions between selected DHS and geospatial covariates sequentially, retaining only those that were significant in the final model.

Multiple multilevel multinomial regression analysis (vaccination according to source of evidence). The study also employed a Bayesian multinomial multivariable multilevel modelling approach to estimate the adjusted relative risk (aRR) and associated 95% credible intervals for covariates significantly associated with PENTA1 and MV using the multinomial outcomes defined previously. No interaction terms were considered for the multinomial analyses due to model complexities and non-convergence challenges.

Measures of variation (random effects analysis)

We computed summary measures [30,40] of the amount of residual variation attributable to the hierarchies in the binomial models. These included the variance partitioning coefficient (VPC) which measures residual variation between clusters/communities in different strata; the median odds ratio (MOR) which quantifies residual community level variation in the likelihood of vaccination on the odds ratio scale, and the percent change in variance (PCV) which measures change in residual variation due to the inclusion of covariates in the models [41–45].

Also, although prediction was not the main goal, we evaluated the discriminatory or predictive power of the fitted models using the area under the receiver operating characteristic (AUROC) curve (see supplementary materials for details).

All analyses were implemented in Stata version 16 [46], MLwiN version 3.05 [47], and the R programming language version 4.0.3 [48]. Additionally, we used the runmlwin [49] program to run the MLwiN multilevel modelling software from within Stata. We utilized MCMC algorithms with a burn-in length of 1000, a monitoring chain length of 60000, and thinning of 20. Convergence of the MCMC chains was assessed via visual inspection of the trace and autocorrelation plots of the parameters.

Ethical approval and consent to participate

Ethical approval was obtained from the Nigeria National Health Research Ethics Committee and the ICF Institutional Review Board for the main NDHS [23], and from the Ethics and Research Governance, University of Southampton, United Kingdom. Written informed consent was obtained from all study respondents. However, the data were analysed anonymously in the present study. All methods were performed in accordance with the relevant guidelines and regulations.

Results

Outcome indicators of vaccination coverage in Nigeria

Among 6059 children aged 12–23 months, 3937 (65%) had any evidence of receiving PENTA1 and 3041 PENTA3 vaccination hence PENTA3/1 was 77%. Among the 2039 children with documented evidence of PENTA1 vaccination at or after 6 weeks of age, 1769(86.7%) had documented receipt of PENTA3 vaccine, while PENTA3/1 was lower (67.8%) among the children with only a verbal history of vaccination. Among 11839 children aged 12–35 months, 2111 (18%) had documented receipt of a valid dose of MV (at or after age 9 months); 4522 (38%) had either invalid documented doses or a verbal history of vaccination, and 5206 (44%) had no evidence of MCV receipt (see Tables C, F, H, J, L in S1 File).

Cross-tabulation results

As expected, the receipt of PENTA1, PENTA3/1 and MV was higher among children with a health card/document (or a home-based record—HBR) than those without. Multiple other individual and family factors related to socio-economic and demographic status, access to communications technology, use of other services and length of stay in household were significantly associated with at least one of the outcome indicators using Chi-squared tests. Of geospatial/community variables, rural/urban residence, livestock density index, travel time to nearest health facility, and vegetation index were associated with all three indicators (see Tables C, F and H in S1 File).

Multiple multilevel binomial analyses results

Figs 1–3 show the adjusted odds ratios and their corresponding 95% credible intervals for receipt of PENTA1, PENTA3/1 and MV vaccination, based on any evidence of vaccination—definitions and reference categories are described in Table A in S1 File and detailed results shown in Tables D, E, G and I in S1 File. Note that Fig 1 shows the results for both the main effects and the interaction terms for PENTA1.

Fig 1 — (a) Plots of adjusted odds ratios and corresponding 95% credible intervals based on the Bayesian multilevel binomial analysis for PENTA1. The vertical dotted lines mark the odds ratio of 1 and the blue lines mark significant covariates. (b) Interaction effects between maternal education and travel time to the nearest health facility for PENTA1. The vertical dotted line marks the odds ratio of 1.

Fig 3 — The vertical dotted line marks the odds ratio of 1 and the blue lines mark significant covariates.

Factors associated with vaccination were broadly similar for documented versus recall evidence of vaccination and included individual and community (geospatial) attributes. Although coverage of each vaccine-dose was higher in urban than rural areas, urban status was not significant in multivariable analyses. Based on any evidence of vaccination, we found that HBR, receipt of vitamin A and higher maternal educational level were significantly positively associated with each outcome. Indicators relating to socio-economic status, as well as ethnic group, skilled birth attendance, lower travel time to a clinic and reported problems seeking health care were significantly associated with both PENTA1 and MV. Maternal religion was related to PENTA1, and PENTA1/3 and maternal age related to MV and PENTA3/1; other variables were significantly associated with one outcome each.

There were few differences in determinants of receipt of PENTA1 compared to MV (Fig 1 and Figs 3 and 4).

Being Christian (aOR = 1.53, 95% Cr.I: 1.08, 2.10, compared with families practising Islam), having a length of stay of < 1 year (aOR = 2.32, 95% Cr.I: 1.10, 4.29) or > 5 years (aOR = 1.53, 95% Cr.I: 1.05, 2.17), and residence in communities with lower livestock density index (aOR = 1.66, 95% Cr.I: 1.13, 2.37, reference higher livestock index) or those with medium vegetation index (aOR = 1.45, 95% Cr.I: 1.07, 1.93, reference lower vegetation index) were associated with higher odds of receipt of PENTA1 but not MV. Significant interaction between maternal education and travel time to the nearest health facility was found in the model for PENTA1 vaccination (see Fig 1(B), and Tables D and E in S1 File). The effect of education was greatest among those with higher travel times and was not significant among the lowest travel times, while the effect of longer travel times was only significant among those whose mothers had no education (see Table E in S1 File).

Birth order, wealth, health insurance, bednet ownership and access to traditional media were significantly associated with MV receipt but not the other outcomes. Interestingly, reporting a problem seeking health care was positively associated with receipt of both PENTA1 and MV. Among those who received PENTA1, receipt of PENTA3 was associated with mother’s age, education, and knowledge of malaria, HBR possession and receipt of vitamin A (see Fig 2, and Table G in S1 File).

Fig 2 — The vertical dotted line marks the odds ratio of 1 and the blue lines mark significant covariates.

Finally, we summarized significant covariates in Fig 4 for easy reference.

Substantial residual community level variances in different strata were observed in the fitted model for each outcome (Tables D, G and I in S1 File). Specifically, 26%, 15%, and 19% of variation in PENTA1, PENTA3/1 and MV, respectively, could be attributable to communities in different strata. The median odds ratios of 2.7, 2.1, and 2.3 respectively for PENTA1, PENTA3/1 and MV, which are at least two times higher than the reference value (MOR = 1), are an indication of substantial community level variation in the likelihood of receiving PENTA1, PENTA3/1 and MV vaccinations. Also, the estimated PCV values demonstrate that the covariates included in our models led to 67%, 61%, and 57% reduction in residual variation at both the community and stratum levels in the fitted models for PENTA1, PENTA3/1 and MV respectively. Lastly, the discriminatory or predictive power of the fitted models for correctly predicting the likelihood of PENTA1, PENTA3/1, and MV vaccinations based on the AUROC curve were 91.3%, 77.1% and 80.2% respectively as displayed in Fig C in S1 File.

Multiple multilevel multinomial analyses results

Overall, there were few differences in the direction and magnitude of associations between the independent variables and the outcomes classified according to source of evidence of vaccination (“card valid” or “card invalid/history”)–Fig 5, and Fig D and Tables J-M in S1 File. A few differences were found, however. For example, for PENTA1, lower travel time significantly increased the odds of card valid vaccination but not card invalid/history vaccination and the effect of receipt of vitamin A was highest for card valid vaccination. For MV, areas with lower travel time (compared to higher travel time) and middle wealth status (compared to poorer/poorest) had significantly higher likelihood of card valid vaccination (relative to no evidence of vaccination) but not card invalid/history vaccination. Receipt of vitamin A was significantly associated with MV vaccination irrespective of source of evidence, but the effect was greater for card valid MV vaccination. Length of stay less than 1 year was associated with card invalid/history of vaccination but not card valid vaccination.

Fig 5 — The vertical dotted line marks the relative risk of 1.

Discussion

Our analyses of correlates of vaccination include several innovations. First, we examined separately factors associated with beginning (PENTA1) and completing (PENTA3/1 and MV) the vaccination series. Second, we included community-level geospatial factors as well as individual attributes. Third, we used multinomial analyses to examine potential differences in findings when analyses were stratified by the strength of evidence of vaccination. Differences between associations of receipt of PENTA1 according to source of evidence may relate to misclassification of the outcome when a verbal history is accepted. The fact that we found few differences suggests that the mother’s recall was reasonably accurate, as reported elsewhere [24,50]. For MV, differences according to source of evidence may also relate to the vaccination strategy used. Children aged 12–35 months in Aug-Dec 2018 (when the DHS fieldwork was done) were mostly eligible for the November 2017 measles campaign in Nigeria. Doses recorded on the HBR represent only those received via routine immunization while a verbal history may include doses received during campaigns although we have previously found evidence that these are under-reported in Nigeria [20].

Overall, our analyses showed that for both PENTA1 and MV, factors that were predictive of card invalid/history vaccination were broadly similar to those that were predictive of card valid vaccination (i.e., the multinomial analysis), and to those found when considering any evidence of vaccination (i.e., the binomial analysis). There was a difference for travel time, where for both PENTA1 and MV, lower travel time to a health facility significantly increased the odds of card valid vaccination but not card invalid/history evidence of vaccination, indicating that proximity to a health facility has a marked influence on the timeliness and validity of vaccinations [4,21,51]. In what follows, we focus on the factors identified in the binomial analyses.

Ownership of a health card/document, receipt of Vitamin A—both of which are indicators of access to health services, and maternal education were positively associated with all three coverage indicators, the effect being modified by travel time for PENTA1, with mothers lacking education in remote areas being least likely to attend for vaccination. Skilled attendance at birth had significant positive associations with both PENTA1 and MV, further highlighting the importance of access to health services in improving vaccination coverage and corroborating findings in previous studies [5,10,11,15,16,28,34,35,52–55]. Wealth-related indicators were associated with both PENTA1 and MV although some specific indicators differed, for example for PENTA1, maternal employment and having a bank account were important while for MV, wealth, health insurance, and bednet ownership were important. As is commonly found, economically empowered mothers make better health choices for their children [56,57]. The association of PENTA1 with access to a mobile phone/internet may reflect both wealth and access to health information. Children born to Igbo mothers compared to Hausa/Fulani and those born to Christian mothers compared to Muslim mothers were more likely to receive PENTA1, which contributes to the geographical disparities in routine immunization (RI) coverage in the country [58,59]. Associations of PENTA1 with livestock density and vegetation index may also reflect geographical disparities. Interestingly, the likelihood of PENTA1 receipt was higher among children born to mothers who reported a problem seeking medical advice/treatment, which we speculate to be a consequence of higher motivation and better health-seeking behaviour among these women. Thus, interventions seeking to improve vaccination coverage in remote areas should be designed especially with mother’s educational level in mind [10,11,16].

While socio-economic factors helped predict PENTA1 receipt, among those who started the PENTA series, mother’s education, knowledge of malaria, being at least ≥ 20 years old, possibly an indication of better knowledge of vaccination [16,17,28,34,52,60,61] and practising traditional/other religion (compared to Islam) were associated with increased likelihood of completion of the three-dose PENTA series (i.e. PENTA3/1), further highlighting the importance of maternal education and knowledge.

MV receipt was related both to socio-economic variables and access to media, birth order, health insurance, and bednet ownership. Children who had a birth order above two were less likely to receive MV, signalling that even though older mothers were more likely to have their children vaccinated, this propensity could change with subsequent births [13,14,16,58,62,63]. Health insurance and bednet ownership could reflect both wealth and a positive attitude to health care.

Finally, our analyses revealed substantial variation in the likelihood of vaccination at the community level, demonstrating the need for estimation of coverage and targeting of interventions at granular spatial scales [18,19].

Study limitations

First, the study could not establish causal relationships due to the cross-sectional sampling design. Secondly, our study did not comprehensively assess all the factors that could affect vaccination coverage, particularly attitudes towards vaccination and supply-side factors such as vaccine and health worker availability and missed opportunities for vaccination [64] due to data limitations. Some important covariates such as antenatal care, postnatal care, and mother’s receipt of tetanus toxoid injections before birth were excluded from the analysis because they had > 15% missing data for MV, but these would likely correlate with skilled birth attendance. Lastly, our analysis may have excluded important at-risk populations such as those living in conflict-affected areas and urban slums if the sampling frame used in the DHS did not fully capture these populations.

Conclusion

The study has identified several factors to be predictive of indicators of childhood vaccination coverage pointing to the need for an integrated approach to addressing inequities in vaccination coverage in Nigeria. This should include improvements in access to health facilities and services (e.g., skilled birth attendance), socioeconomic conditions of households, and improvements in maternal education through targeting uneducated and teenage mothers with health literacy programmes including familiarization with the vaccination schedule and the importance of retention of home-based records. Also, better utilization of means of communication such as the traditional media and mobile phones/internet for disseminating vital health information is likely to yield improvements in coverage. Furthermore, community-focused interventions, and further research will be required to identify other supply- and demand-side factors as part of an overall strategy to improve childhood vaccination coverage in Nigeria. Also, the effects of the geospatial covariates are estimated for the entire country in our models. It will make sense to display maps of these if they were estimated for each region, for example. We will explore this detailed analysis in our future work.

Supporting information

S1 File

Supplementary file containing Tables A–M, Figs A-D and additional texts referenced in the manuscript.

(DOCX)

Click here for additional data file.^{(1.5MB, docx)}

Data Availability

DHS data are publicly available from https://dhsprogram.com/data/available-datasets.cfm. Other data (i.e., geospatial covariates) are publicly available via the sources referenced in the methods section, and also presented in the Supplementary Information Table S2. The authors did not have any special access privileges that others would not have.

Funding Statement

This work was supported by funding from the Bill and Melinda Gates Foundation (Investment ID INV-003287). CEU and AJT received the grant. The funder did not play any role in the study design, data collection, analysis and interpretation of data, the report writing, and the decision to submit the manuscript for publication.

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PLoS One. doi: 10.1371/journal.pone.0269066.r001

Decision Letter 0

Tzai-Hung Wen

22 Nov 2021

PONE-D-21-28231Multilevel analysis of predictors of multiple indicators of childhood vaccination in NigeriaPLOS ONE

Dear Dr. Aheto,

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Overall comment:

This study investigates multiple indicators of childhood vaccination in Nigeria. Using data from the 2018 Nigeria Demographic and Health Survey and geospatial data sets, authors built Bayesian multilevel binomial and multinomial logistic regression models to analyze independent predictors of three vaccination outcomes. Although authors claimed several innovations, including they examined separately factors associated with beginning (PENTA1) and completing (PENTA3/1 and MV) the vaccination series; included community-level geospatial factors as well as individual attributes, and used multinomial analyses to examine potential differences in findings when analysis, I think this study has normal quality. A major revision can be reached. The model design and the description of results are good, but there are still some issues that should be improved before publish.

1. Line 66-68. The major finding is not so attractive for me. Because it is basically a piece of general knowledge, we can easily to imagine the result.

2. Line 92-114: Although the motivation and objective are clear, the quality of the introduction is improvable. There must be several relative studies that use a similar approach to investigate the problem, but the authors didn’t mention about it.

3. Line 104-106: More than ten cited references are included in only one sentence. Authors are suggested to address more detail about them.

4. Line 155: I think authors include the geospatial covariates in models is a very good approach. But I suggest authors can present the geospatial covariates by maps.

5. Line 175-211: The model design is praiseworthy.

6. Line 308-313: Similarly, I suggest authors can display the results of the effect of geospatial factors by maps.

7. Figures: The quality and resolution of figures are very bad. It’s hard to know the results.

8. Tables: I think some numerical statistical findings can be presented as tables.

Reviewer #2: Review of PLUS ONE

PONE-D-21-28231: " Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria "

The manuscript presents a reasonably well-articulated discussion for the developed framework to address childhood vaccination inequalities in Nigeria. Based on my reading, the applications of multilevel logistic models to produce new insights to data analysis makes the current manuscript contributing to the literature of public health. The work will be of also interest to a wide readership in the journal. Although the manuscript is worth published, its present form needs some revisions and I have the following suggestions.

1. In the Introduction, the authors omitted the brief description about multilevel models. Some details can be found at supplementary file but I think the associated discussions could be summarized into the Introduction. In so doing, the article would be more complete and clear.

2. In the Data section, the authors may provide a figure for the hieratical structure of the data, which can furnish the rationale why to use multilevel models.

On the other hand, for readability and the ease of understanding, the authors should at least include some model equations for the multilevel models in the section of Data analysis, rather than just assemble all the materials in the supplementary.

3. Is there any specific reason to use Bayesian multilevel models? As the ‘frequentist’ multilevel logistic regression models exist as alternative ways to analyze the data, I suggest the authors explain a bit what to motivate the Bayesian multilevel analyses and why to use them both in the Introduction and Data analysis sections.

4. Regarding the model specifications, do the multilevel models only include random intercepts? Is there any random coefficients for the predictors? From the model equation 2 and equation 3 in the supplementary file, it is not clear to me how the multilevel representation is given.

5. The authors indicated in the supplementary that non-informative priors can be alternative choice of priors for the model parameters. I was actually thinking that would it be possible to generate better analysis results as there is lack of information on reliable priors. Although I don‘t demand the authors to do so, the discussion needs to be added.

6. Lines 168-174: It is not clear whether the single-level logistic regression analysis was taken with Bayesian approach or not. This needs to be clarified.

7. Lines: 190-194: The authors did not consider interaction terms in the multinomial multilevel regression analysis. I wonder if this is due to the computational difficulty or convergence problems for the considered models? Also, does it need proportional odds assumption here? The authors may discuss these in the manuscript.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2022 May 25;17(5):e0269066. doi: 10.1371/journal.pone.0269066.r002

Author response to Decision Letter 0

22 Dec 2021

Dear Editor,

Thank you for the opportunity to once again revise our manuscript titled “Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria”. We are extremely grateful to the Editors and the Reviewers for the constructive criticism of the manuscript and the positive feedback which certainly helped to improve the message and the quality of our manuscript. We are indeed grateful.

We have duly addressed all the concerns raised by the Editors and the Reviewers to the best of our ability and wish to re-submit the revised version for your consideration and subsequent publication in your cherished journal, and together we can all help in addressing this serious public health challenge facing millions of children globally, especially in developing countries like Nigeria.

Response to Editorial comments

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Response: We have revised the manuscript to address the issues about the style requirements of the journal throughout the manuscript text.

2. Thank you for stating the following in the Acknowledgments/ Funding Section of your manuscript:

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

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Response: We removed the funding information under the Funding section of the manuscript and stated this as “This work was supported by funding from the Bill and Melinda Gates Foundation (Investment ID INV-003287). CEU and AJT received the grant. The funder did not play any role in the study design, data collection, analysis and interpretation of data, the report writing, and the decision to submit the manuscript for publication” in the cover letter as directed. The Editors should use this for the publication.

Response: Thank you for this information. We revised the corresponding author’s affiliation to reflect this. The corresponding author’s address and affiliations are as follows in order as they appear:

1) WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK.

2) Department of Biostatistics, School of Public Health, College of Health Sciences, University of Ghana, Accra - Ghana”.

We have revised the affiliations of the other authors accordingly to reflect the changes made. (see the title page).

In your revised cover letter, please address the following prompts:

Response: All the data sets supporting the analyses in this manuscript are publicly available and can be obtained by sending data request to the owners or by accessing them from the owner’s website. We provided full details on the data sources, and references to enable readers to access these data. We are unable to directly share this data with third parties because we did not have permission from the data owners to do so. We have provided additional text as “The authors did not have any special access privileges that others would not have” to the data availability section.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Response: All the data (both DHS and Geospatial variables) supporting the analyses presented in this manuscript are publicly available and sources provided in the methods section. In addition, we presented the description, the sources, and the references to the geospatial covariates used in our work in Table S2, and also under “Geospatial covariate description and processing steps” in the Supplementary Information.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

a. You may seek permission from the original copyright holder of Figure S1 to publish the content specifically under the CC BY 4.0 license.

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Response: The Figure S1 was produced by the authors for this manuscript. Thus, we did not need any permission to use it, and so did not make any changes to the manuscript.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

Response: The maps and its contents presented in Figure S1 in the Supplementary Information was produced by the authors using publicly available data in ArcGIS software for this paper, and the data source were cited in Table S2 in the Supporting Information. We did not need any permission to use this figure as it was produced by us. We have published similar maps in previous studies[1-3]. Hence, we did not make any changes to the manuscript.

Reference

1. Utazi CE, Thorley J, Alegana VA, Ferrari MJ, Takahashi S, Metcalf CJE, et al. Mapping vaccination coverage to explore the effects of delivery mechanisms and inform vaccination strategies. Nature Communications. 2019;10(1):1633. doi: 10.1038/s41467-019-09611-1.

2. Utazi CE, Wagai J, Pannell O, Cutts FT, Rhoda DA, Ferrari MJ, et al. Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys. Vaccine. 2020;38(14):3062-71. doi: https://doi.org/10.1016/j.vaccine.2020.02.070.

3. Utazi CE, Thorley J, Alegana VA, Ferrari MJ, Takahashi S, Metcalf CJE, et al. High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries. Vaccine. 2018;36(12):1583-91. Epub 2018/02/14. doi: 10.1016/j.vaccine.2018.02.020. PubMed PMID: 29454519; PubMed Central PMCID: PMCPMC6344781.

Response: We revised the manuscript to reflect these changes. The marked changes can be found after the reference list under the heading Supporting Information captions.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

5. Review Comments to the Author

Reviewer #1: Overall comment:

Response: Thank you for the positive and helpful comments.

1. Line 66-68. The major finding is not so attractive for me. Because it is basically a piece of general knowledge, we can easily to imagine the result.

Response: Thank you. To our knowledge, our study is the first of its kind to examine the relationships between vaccination and a range of predictor variables based on strength of evidence of vaccination, i.e., documented versus recall evidence of vaccination. We consider our finding that “factors associated with vaccination were broadly similar for documented versus recall evidence of vaccination” to be highly significant as this will help guide policymakers when making decisions using different evidence of vaccination available to them. Moreover, our findings are important because we were also interested in establishing which predictor might be associated with all the three outcomes (Penta1, Penta3/1, and MV) under investigation. Hence, policies addressing the common predictors of all three outcomes is likely to generate greater improvements in coverage levels.

Response: Thank you. We revised the manuscript to reflect this. See line 122, and lines 125-138 at page 4.

3. Line 104-106: More than ten cited references are included in only one sentence. Authors are suggested to address more detail about them.

Response: Thank you. We revised the manuscript to expatiate on this. See lines 115-121 at pages 3 and 4.

4. Line 155: I think authors include the geospatial covariates in models is a very good approach. But I suggest authors can present the geospatial covariates by maps.

Response: Thank you for the positive feedback. We have already provided the maps for the geospatial covariates used in our models in Figure S1 in the Supplementary Information at page 6.

5. Line 175-211: The model design is praiseworthy.

Response: Thank you. We appreciate this feedback.

6. Line 308-313: Similarly, I suggest authors can display the results of the effect of geospatial factors by maps.

Response: Thank you. The effects of the geospatial covariates are estimated for the entire country in our models. It will make sense to display maps of these if they were estimated for each region, for example. We will explore this detailed analysis in future work as we have now highlighted in the discussion (see lines 440-443 at page 17). However, we mapped the values of all the geospatial covariates retained in our models across the entire Nigeria to help the readers appreciate and understand the spatial variation in these covariates. See Figure S1 in the Supplementary Information at page 6.

7. Figures: The quality and resolution of figures are very bad. It’s hard to know the results.

Response: Thank you. The figures as appeared on our system as .tiff file are all clear when you open them. Perhaps, what you observed might be due to the online system combining all the files as PDF. Note that we used the ‘Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool recommended by PLOS ONE and available at https://pacev2.apexcovantage.com/ to prepare our figures to the acceptable format and the standard required by PLOS ONE. Also, we saved these figures in R software directly as .tiff files and then used the PACE tool to prepare them as required by the journal, and all the figures were approved as meeting the PLOS ONE standard by the PACE system.

8. Tables: I think some numerical statistical findings can be presented as tables.

Response: Thank you. Given the numerous numbers of parameters involved in our models, it is preferable to summarise the results in figures instead of tables for the main manuscript text to sustain readers interests while the tables for the same results are sent to the Supplementary Information. It is also easier to understand the results as presented in the figures than in tables while maintaining the scientific meaning of these results. However, note that we have already provided same results in the supplementary tables in the Supplementary Information for those who might also be interested in reading these results in tables (see Tables S3-S13). Thus, we did not make any changes to the manuscript.

Reviewer #2: Review of PLUS ONE

PONE-D-21-28231: " Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria "

Response: We appreciate your positive feedback.

Response: Thank you. We appreciate your feedback. We wish to state that the Introduction section is not where models are described and that the details of the models used in the work are already given in the Data analysis section from pages 7-9 and in the Supplementary Information section from page 7-12. Thus, we did not make any changes to the manuscript.

2. In the Data section, the authors may provide a figure for the hieratical structure of the data, which can furnish the rationale why to use multilevel models.

Response: Thank you for this useful feedback. We now produced the suggested figure, but we prefer to put this figure in the Supplementary Information (see Fig S2 at page 7). We revised the manuscript to reflect this. See line 212-213 at page 8.

Response: Thank you. The authors were very careful about the need for the general audience to understand the paper. Thus, this paper hinges on both public health and statistical methodology. Our approach of sending our multilevel model equations to the Supplementary Information while describing them in the main manuscript text is strategic – (1) not to make the paper an overkill of statistical methodology which will make it very difficult for the general public health experts to understand and (2) to sustain readers interests. Fortunately, the statistical experts or modellers who are interested in these multilevel model equations can easily access them in detail in the Supplementary Information. In addition, we provided sufficient information about the multilevel models used in this paper in the main manuscript text to allow readers to follow the paper easily. As a result, we are of firm believe that is best to leave the multilevel model equations in the Supplementary Information. Thus, we did not make any changes to the manuscript.

Response: Thank you. Firstly, one of our interests is to employ Bayesian modelling approach to investigate the problem. Undoubtedly, the Bayesian modelling is one of the novel approaches in model fitting and model diagnostics. For example, in the Bayesian approach, two different sources of uncertainties (i.e., uncertainty in the parameter values and sampling uncertainty) in our estimates can be quantified and the 95% credible intervals fixed while the estimated parameters are allowed to vary unlike the frequentist (i.e., classical) approach. Also, the type of the modelling implemented in this study, especially the multiple multilevel multinomial and binomial regression models can effectively and efficiently be implemented in the Bayesian framework compared to the frequentist due to the model complexity and computational cost. These are the key reasons we opted for the Bayesian approach. However, we explore the data using cross-tabulations, and single-level logistic regression model based on frequentist approach to determine how each of the covariates relates with the outcomes. We revised the manuscript to reflect this in the Supplementary Information under the heading ‘Bayesian models’ in lines 208-219.

Also, as presented already in the original manuscript in the ‘Supplementary Information’ under the heading ‘Prior distributions for fixed and random effects’, note that the results from the frequentist models were used to provide initial values for the Bayesian multilevel models (i.e., both the binomial and the multinomial). Thus, the results from the frequentist models were used as an input for the final Bayesian models as already captured in the Supplementary Information (see lines 193-203).

Response: Thank you. We used only random intercept multilevel models as already stated in the Supplementary Information, and we did not include random coefficient for the predictors because our goal is to examine whether the outcomes vary by the levels of hierarchy. Thus, our models did not include random slopes. This can be seen in the texts and our multilevel model equations presented in the Supplementary Information (see lines 109-111 and Equation (1) at page 8, line 120 Equation (2) at page 9, and lines 172-173, 180-181 at page 11).

Response: Thank you. We have already discussed this in the ‘Supplementary Information’ under the heading ‘Prior distributions for fixed and random effects’ (see lines 193-203). We stated that due to lack of information on reliable priors, we fitted multilevel models via the frequentist approach and used these results as the initial values (priors) for the Bayesian models. Even though this is a reasonable approach and better than the non-informative priors approach, we were informing the readers that there is another approach where one can also use the non-informative priors, and we stated this in the same section already (see lines 193-203). Definitely, our approach of fitting the frequentist model to the data and using the parameters from these models as the priors for the Bayesian models is better than using the non-informative priors. Our approach was successfully used in a previous study [1]. We now provided the reference to support our approach in the Supplementary Information (see line 202 at page 12).

Reference

1. Aheto JMK, Taylor BM, Keegan TJ, Diggle PJ. Modelling and forecasting spatio-temporal variation in the risk of chronic malnutrition among under-five children in Ghana. Spat Spatiotemporal Epidemiol. 2017;21:37-46. Epub 2017/03/02. doi: 10.1016/j.sste.2017.02.003. PubMed PMID: 28552186.

6. Lines 168-174: It is not clear whether the single-level logistic regression analysis was taken with Bayesian approach or not. This needs to be clarified.

Response: Thank you for the feedback. For the single level models presented, we used the frequentist approach as already stated in the original manuscript in lines 170-171 (i.e., “…fitted frequentist single level simple logistic regression models to obtain the corresponding …” but now in lines 202-205at page 7 of the revised main manuscript. Also, we already stated this in the supplementary tables. We already reported this under ‘Cross-tabulations and single-level logistic regression analysis’ in the main manuscript under ‘Data analysis’ (see lines 202-205 at page 7), and in supplementary Tables S4, S5, S7, and S9 in the Supplementary Information.

Response: Thank you. We did not consider the interaction terms for the multiple multilevel multinomial models due to model complexities and non-convergence challenges. We revised the manuscript to reflect this (see line 229 at page 8). Also, our multinomial outcomes PENTA1 and MV are nominal outcomes and not ordinal outcomes and so does not require the proportional odds assumptions.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0269066.r003

Decision Letter 1

Tzai-Hung Wen

1 Mar 2022

PONE-D-21-28231R1Multilevel analysis of predictors of multiple indicators of childhood vaccination in NigeriaPLOS ONE

Dear Dr. Aheto,

Please submit your revised manuscript by Apr 15 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Tzai-Hung Wen, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

The reviews think all comments have been addressed. However, the reviewer concerns the quality and resolution of the figures. We are happy to publish the paper if the quality of the figure can be further improved.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I have no more comments. However, the quality and resolutions of figures listed in this manuscript are not good enough. I suggest authors should correct them.

Reviewer #2: The revised manuscript has addressed all my concerns. I am happy to see it published in the journal.

**********

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2022 May 25;17(5):e0269066. doi: 10.1371/journal.pone.0269066.r004

Author response to Decision Letter 1

2 Mar 2022

Dear Editor,

We have duly addressed all the concerns raised by the Editors and the Reviewers to the best of our ability and wish to submit the revised version for your consideration and subsequent publication in your cherished journal, and together we can all help in addressing this serious public health challenge facing millions of children globally, especially in developing countries like Nigeria.

Response to Editorial comments

Journal Requirements:

Response: We reviewed our reference list and can confirm that they are correct and complete. Also, we did not find any retracted paper among our references. Thus, we did not make any changes to the manuscript and the reference list.

Additional Editor Comments:

Response: Thank you for handling our manuscript smoothly and your decision for us to further improve our figures before our manuscript is accepted for publication. We suspect that the reviewer might not have opened the figure from the link provided at the top of each figure in the reviewer’s PDF document and appeared to have relied on what he/she saw directly on the reviewers PDF document to arrive at this concern. Please, we prepared our figures using the correct figure formats, including correct resolutions based on PLOS ONE recommended free tool (https://pacev2.apexcovantage.com/) and the quality and resolution was great on our systems. Please, may I ask the Editor to download the original figures from the link at the top of each figure in the reviewer’s PDF document for your own assessment. Always, the system that generates the reviewers document for review is responsible for the relatively low figure quality observed by the reviewer so reviewers must always download the actual figures from the link at the top of each figure which always have the quality and the required resolution from the PACE tool as recommended by PLOS ONE.

However, we prepared new figures again using much higher resolution and used the PACE software to format the figures as recommended by PLOS ONE to improve the quality and resolution as requested.

Per my own experience with publishing figures in PLOS ONE, and reviewing manuscripts containing figures for PLOS ONE, the quality and resolution of such figures is mostly very poor in the reviewers document even if one submitted a very high quality and resolution images/figures just as in our case now. As a result, reviewers who are not aware of this will surely raise concerns about the figures as in our case, but same figures look great when reviewers download them from the link in the reviewer’s PDF document at the top of each figure and they also look good when they are finally published in the journal once they were prepared from the PACE tool like in our case.

Reviewer #1: I have no more comments. However, the quality and resolutions of figures listed in this manuscript are not good enough. I suggest authors should correct them.

Response: Thank you. However, the reviewer might not have opened the figure from the link provided at the top of each figure in the reviewer’s PDF document and appeared to have relied on what he/she saw directly on the reviewers PDF document to arrive at this concern. Please, we prepared our figures using the correct figure formats, including correct resolutions based on PLOS ONE recommended free tool (https://pacev2.apexcovantage.com/) and the quality and resolution was great on our systems. Always, the system that generates the reviewers document for review is responsible for the relatively low figure quality observed by the reviewer so reviewers must always download the actual figures from the link at the top of each figure in the reviewers PDF document which always have the required quality and resolution from the PACE tool as recommended by PLOS ONE.

However, we prepared new figures again using much higher resolution and used the PACE tool to format the figures as recommended by PLOS ONE to improve the quality and resolution as suggested.

Also, note that we now combined Figures 1 and 2 to form Figure 1 because they are both coming from the same model, and we thought is better to present them as one figure. Accordingly, we revised the manuscript to correct the figure numbers and added few text (see lines 270, 273, and 274 at page 10, line 285-286, and 294 at page 11, and lines 302-309 at page 12, lines 332 and 344 at page 13).

Reviewer #2: The revised manuscript has addressed all my concerns. I am happy to see it published in the journal.

Response: Thank you.

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0269066.r005

Decision Letter 2

Tzai-Hung Wen

28 Mar 2022

PONE-D-21-28231R2Multilevel analysis of predictors of multiple indicators of childhood vaccination in NigeriaPLOS ONE

Dear Dr. Aheto,

Please submit your revised manuscript by May 12 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Tzai-Hung Wen, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Additional Editor Comments:

The quality and resolutions of figures listed in this manuscript are not good enough. I suggest authors should correct them.

[Note: HTML markup is below. Please do not edit.]

PLoS One. 2022 May 25;17(5):e0269066. doi: 10.1371/journal.pone.0269066.r006

Author response to Decision Letter 2

28 Apr 2022

Dear Editor,

Response to Editorial comments

Journal Requirements:

Additional Editor Comments:

The quality and resolutions of figures listed in this manuscript are not good enough. I suggest authors should correct them.

Response: Thank you for handling our manuscript smoothly and your decision for us to further improve our figures before our manuscript is accepted for publication.

We have prepared our figures following the recommended guidelines from PLOS ONE, including correct resolutions based on the recommended free tool (https://pacev2.apexcovantage.com/) and the quality and resolution was great on our systems (see original figures in the PLOS ONE submission system).

We believe our figures were produced with the required quality and resolution. However, we suspect that the Reviewers might not have accessed the original figures provided via the links at the top of each figure in the PDF proof of submission which was reviewed by the reviewers. This might have led them to persistently request that we produce new figures with high quality and resolution.

In our first revision, we responded to item 7 from reviewer #1 and the query was “Figures: The quality and resolution of figures are very bad. It’s hard to know the results.” Clearly, for reviewer #1 to say “… It’s hard to know the results”, the reviewer was only relying on the figures as appeared in the PDF Proof of submission (images in the PDF proof) rather than accessing the original figures embedded in the proof links above each figure. Nonetheless, we improved the quality and resolution of our figures in our revised submission dated on 3rd March 2022, but the comments on the quality of figures came up in subsequent decision letter. Also, we have previously written to the Editor on 2nd March 2022 but we are yet to receive any response except a new request to improve our figures.

As presented below, the reviewers seem to be basing their quality assessment of our figures on Fig 2 (i.e., the direct images shown in the pdf proof) instead of assessing Fig 1 (i.e., the original images embedded in the PDF proof links at the top right of each figure).

Fig 1 Screenshot of Figure 1 from the link on the top right of Figure 1 in the PDF Proof of submission (see the Response to Reviewers letter submitted on word document via the submission system for the figures).

Fig 2. Screenshot of the image shown in Figure 1 directly from the PDF Proof of submission (see the Response to Reviewers letter submitted on word document via the submission system for the figures).

Per the above evidence, our figures have good quality and resolution, and we believe strongly that our figures met PLOS ONE requirements to be accepted for publication. Thus, we did not make any changes to the figures after the second revision we have done to improve it further.

Note: we have now included acknowledgement section to our revised manuscript (see lines 456-457 at page 17).

Attachment

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PLoS One. doi: 10.1371/journal.pone.0269066.r007

Decision Letter 3

Tzai-Hung Wen

16 May 2022

Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria

PONE-D-21-28231R3

Dear Dr. Aheto,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Tzai-Hung Wen, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0269066.r008

Acceptance letter

Tzai-Hung Wen

17 May 2022

PONE-D-21-28231R3

Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria

Dear Dr. Aheto:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Tzai-Hung Wen

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File

Supplementary file containing Tables A–M, Figs A-D and additional texts referenced in the manuscript.

(DOCX)

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Attachment

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Data Availability Statement

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[pone.0269066.ref002] 2.WHO. Immunization Agenda 2030: A Global Strategy To Leave No One Behind. Geneva, Switzerland: World Health Organization; 2020. [Google Scholar]

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PERMALINK

Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria

Justice Moses K Aheto

Oliver Pannell

Winfred Dotse-Gborgbortsi

Mary K Trimner

Andrew J Tatem

Dale A Rhoda

Felicity T Cutts

C Edson Utazi

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Data

Outcome variables

Explanatory variables

Demographic and Health Survey (DHS) covariates

Geospatial covariates

Data analysis

Estimation of measures of association (fixed effects)

Measures of variation (random effects analysis)

Ethical approval and consent to participate

Results

Outcome indicators of vaccination coverage in Nigeria

Cross-tabulation results

Multiple multilevel binomial analyses results

Fig 1.

Fig 3. Plots of adjusted odds ratios and corresponding 95% credible interval based on Bayesian multilevel binomial analysis for MV vaccination coverage.

Fig 4. Summary of factors predictive of PENTA1, PENTA3/1 and MV vaccinations including the interaction term for PENTA1.

Fig 2. Plots of adjusted odds ratios and corresponding 95% credible interval based on Bayesian multilevel binomial analysis for PENTA3/1 (completion of the PENTA series among those who received PENTA1).

Multiple multilevel multinomial analyses results

Fig 5. Plots of relative risks and corresponding 95% credible interval based on Bayesian multilevel multinomial analysis for MV vaccination coverage.

Discussion

Study limitations

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Tzai-Hung Wen

Roles

Author response to Decision Letter 0

Decision Letter 1

Tzai-Hung Wen

Roles

Author response to Decision Letter 1

Decision Letter 2

Tzai-Hung Wen

Roles

Author response to Decision Letter 2

Decision Letter 3

Tzai-Hung Wen

Roles

Acceptance letter

Tzai-Hung Wen

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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