Approximating vaccine delivery costs to reach zero-dose children: a Bayesian meta-regression analysis

Approximating vaccine delivery costs to reach zero-dose children: a Bayesian meta-regression analysis | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Approximating vaccine delivery costs to reach zero-dose children: a Bayesian meta-regression analysis View ORCID Profile Allison Portnoy , View ORCID Profile Emma Clarke-Deelder , View ORCID Profile Taylor A. Holroyd , View ORCID Profile Daniel R. Hogan , View ORCID Profile Tewodaj Mengistu doi: https://doi.org/10.1101/2025.10.05.25337360 Allison Portnoy 1 Department of Global Health, Boston University School of Public Health 2 Center for Health Decision Science, Harvard T.H. Chan School of Public Health Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Allison Portnoy For correspondence: aportnoy{at}bu.edu Emma Clarke-Deelder 3 Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emma Clarke-Deelder Taylor A. Holroyd 4 Gavi, the Vaccine Alliance Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Taylor A. Holroyd Daniel R. Hogan 4 Gavi, the Vaccine Alliance Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel R. Hogan Tewodaj Mengistu 4 Gavi, the Vaccine Alliance Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tewodaj Mengistu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Introduction The Immunization Agenda 2030 calls for reaching all people with immunization services, including ‘zero-dose’ children—children who have not received any routine vaccines. To plan and finance efforts to fully vaccinate these children and improve coverage and equity, decision-makers need reliable cost estimates. However, primary data on the costs of reaching zero-dose children, typically part of disadvantaged and hard-to-reach populations, are scarce. This study approximates these costs using standardized, country-level estimates of vaccine delivery unit costs for outreach delivery in low- and middle-income countries (LMICs). Methods We extracted outreach delivery cost-per-dose estimates for childhood immunization services from the 2024 update of the Immunization Delivery Cost Catalogue. Using these data, we developed a meta-regression model to estimate standardized outreach vaccine delivery unit costs. The generalized linear model assumed a Gamma-distributed outcome with a log link and included both country-level and study-level predictors: study year, economic or financial cost basis, routine or campaign delivery, and full or incremental costing approach. The fitted model was used to estimate 2024 outreach delivery costs per dose for 129 LMICs. Results The model was estimated using 48 observations from 19 countries focused on outreach or mobile vaccine delivery. The best-fitting specification included diphtheria-tetanus-pertussis (DTP1) coverage, per-capita gross domestic product, and under-five population size as predictors. For 2024, the predicted mean economic cost per dose was $8.65 (95% uncertainty interval $2.33–23.71), averaged across all 129 LMICs. To fully immunize a zero-dose child with 13 recommended vaccinations, the equivalent cost estimate was $112.45 ($30.29–308.23). Conclusion Reaching zero-dose children is crucial for improving equity in global health, and estimates of the costs of doing so are needed to inform budgeting for immunization programs. These meta-regression-based cost estimates can help countries to improve budgeting, planning, and resource allocation for efforts to reach zero-dose children. What is already known on this topic ∘ Despite global commitments under the Immunization Agenda 2030, millions of “zero-dose” children in low- and middle-income countries (LMICs) remain unvaccinated, often due to poverty, geographic inaccessibility, or conflict. ∘ Few studies have directly quantified the costs of reaching zero-dose children; existing evidence is fragmented and limited to small-scale or country-specific interventions. What this study adds ∘ Using a Bayesian meta-regression framework, this study estimates standardized, country-level estimates of vaccine delivery costs based on data for outreach delivery—a proxy for the costs of reaching zero-dose children. ∘ The analysis estimates an average 2024 economic cost of $8.65 per dose and $112.45 per fully vaccinated zero-dose child across 129 LMICs. How this study might affect research, practice or policy ∘ This study provides an evidence base to estimate the investments needed to close immunization coverage gaps under the Immunization Agenda 2030. Introduction A significant public health concern in low- and middle-income country (LMIC) settings is the persistence of children who have not received any routine vaccinations—commonly referred to as ‘zero-dose’ children [ 1 ]. The World Health Organization’s Immunization Agenda 2030 aims to fully vaccinate all people with immunization services, including ‘zero-dose’ children [ 1 ]. Despite decades of progress in improving immunization coverage, the number of children remaining unvaccinated has increased in recent years, leaving them highly susceptible to preventable diseases such as measles, polio, diphtheria, and pertussis [ 2 ]. This gap in immunization not only contributes to elevated morbidity and mortality rates among unprotected children but also undermines broader efforts to achieve herd immunity and control outbreaks. The presence of zero-dose children is often concentrated in marginalized populations facing systemic barriers such as poverty, conflict, displacement, and weak health infrastructure [ 3 , 4 ]. We have limited direct evidence quantifying the costs of fully vaccinating zero-dose children with needed vaccinations and the evidence that does exist reveals important gaps. For example, Clarke-Deelder et al. (2024) examined India’s Intensified Mission Indradhanush (IMI), and estimated the incremental cost of the campaign at about US$82.99 per zero-dose child reached (with wide uncertainty) from the program perspective, and show that the strategy was likely cost-effective under per-capita GDP thresholds [ 5 ]. Meanwhile, Portnoy et al. (2020) provided “standardized” delivery unit cost estimates across LMICs (excluding vaccine price) showing an average cost of US$1.87 per vaccine dose delivered via routine services, though this does not specifically isolate zero-dose children or special outreach efforts [ 6 ]. Additionally, a commentary by Portnoy, et al. (2021) underscores how many immunization cost studies omit key details— only a small number report marginal or scale-up costs or how costs vary by coverage levels— making it difficult to draw strong conclusions about the extra investment needed to reach zero-dose populations [ 7 ]. More recently, Levin et al. (2024) carried out a scoping review of interventions explicitly aiming to reach zero-dose children in LMICs, identifying eleven such studies; intervention costs ranged widely—from about US$0.08 per additional dose for low-touch strategies like SMS reminders in Kenya to about US$67 per dose for more intensive interventions such as cash transfers in Nicaragua [ 8 ]. In order to design and budget for targeted interventions to reduce disease burden and advance health equity globally, decision-makers need to know how much it will cost to fully vaccinate zero-dose children with needed vaccinations. The objective of this study was to approximate the costs of reaching zero-dose children by producing standardized country-level estimates of outreach delivery costs for all countries meeting the World Bank’s LMIC classification [ 9 ]. The identified costs were analyzed in a Bayesian meta-regression analysis framework based on the previously published Portnoy et al. (2020) analysis [ 6 ]. Methods Study data We relied on a publicly available database describing immunization delivery costs in LMIC settings—the Immunization Delivery Cost Catalogue (IDCC) maintained by the Immunization Costing Action Network (ICAN) [ 10 , 11 ]. The IDCC is an online web catalog and downloadable Excel spreadsheet of immunization delivery cost evidence in LMIC settings, which describes the results of a systematic review of published and grey literature (covering three categories of key words: “immunization” AND “cost” AND “delivery”) available between January 2005 and December 2023. From the IDCC, we identified studies that reported delivery costs of outreach and mobile delivery efforts for childhood vaccines to fully vaccinate children up to the age of 15 with needed immunizations. “Outreach” was defined by IDCC as vaccines delivered through outreach or mobile clinics, whether as part of the routine immunization program or through a mass vaccination campaign. Delivery costs included labor, supply chain, capital, and other service delivery costs. “Supply chain” includes costs for cold chain equipment, vehicles, transport, and fuel; “other service delivery” includes costs for program management (i.e., supervision and monitoring), training, social mobilization, and disease surveillance [ 12 ]. For the identified studies, we extracted the study year, estimates of the delivery cost per dose, whether the vaccine was delivered through a campaign or routine delivery modality [ Routine ], whether the costing was full or incremental [ Full ], and whether the presented costs were from the financial or economic perspective [ Econ ]—i.e., financial costs refer to expenditures or financial outlays whereas economic costs include both financial costs as well as the opportunity cost associated with using inputs in the immunization program as compared to their next best use. Where the IDCC did not include a cost per dose (excluding vaccine costs) directly reported by the study, we reviewed the original article and calculated the cost per dose from available data. The IDCC 2024 update included delivery cost per dose estimates in 2022 US dollars. In order to present estimates in 2024 US dollars, the study values were inflated using local inflation according to the consumer price index and local currency-to-USD exchange rates. Model specification We compiled data on country-level covariates potentially associated [ 6 ] with outreach delivery unit costs: log gross domestic product (GDP) per capita [ log(GDP) ], log total under-five population [ log(Pop) ], diphtheria-tetanus-pertussis first dose coverage ( DTP1 ), log under-five mortality rate [ log(U5MR) ], log population density (people per square kilometer of land) [ log(Density) ], and urban population (percentage of total population) [ Urban ] [ 13 - 15 ]. DTP1 was included as a continuous variable ranging from 0 (0% coverage) to 1 (100%) coverage. Country-level covariates were compiled for the year of data collection for each costing study and combined with study-level indicators. By using a log transformation of specified covariates, we assumed these explanatory factors relate to the outcome on the multiplicative scale rather than linearly (for example, a doubling in per-capita GDP produces a fixed increase in the logged outcome). We used a Bayesian meta-regression model to regress outreach delivery unit costs against country-level and study-level explanatory variables. Continuous variables were standardized to mean zero and unit standard deviation before fitting the regression model. We constructed a prediction model for the intervention cost per dose outcome, specified as a generalized linear regression model (GLM), assuming a Gamma distributed outcome and a log link function. In addition to study-level predictors, we selected country-level covariates according to the best fit model using minimized Akaike’s Information Criterion (AIC). The model specification assumed a Gamma likelihood function for the observed data y i where the shape parameter a described the residual variance: This specification assumed variance proportional to c i . We assumed informative prior distributions for all regression coefficients, which were assumed to follow a normal distribution centered at zero with a standard deviation of 1 [ 16 ]. The shape parameter was assumed to follow a half-Cauchy distribution centered at zero with a standard deviation of 5 [ 17 ]. The prediction model was estimated in R software, version 4.4.1, using an adaptive Hamiltonian Monte Carlo algorithm in the Stan software package, version 2.32.6, with four chains of 5000 iterations. The first 2500 iterations were discarded (burn-in period), yielding 10,000 posterior draws for analysis [ 18 ]. Stan model diagnostics were examined to determine any problems encountered by the sampler, and the potential scale reduction factor (i.e., Rhat) for all parameters was evaluated to confirm that the model had successfully converged. Predicted outcomes The fitted prediction model was used to generate both economic outreach delivery cost per dose estimates for each country for the year 2024. As a proxy for the costs of fully vaccinating zero-dose children, we predicted delivery costs assuming routine delivery and a full costing approach. To generate these estimates, we predicted values from the fitted model, with covariate values specific to each country and year. Global cost per dose estimates were calculated as averages weighted by the estimated under-five population of individual countries and further stratified by World Bank income level to produce cost per dose estimates for low-income, lower middle-income, and upper middle-income countries [ 9 ]. We tested predictive performance by comparing model predictions to the observed cost per dose matched to country and year. Results Regression model We identified 22 studies including 48 observations across 19 countries with outreach delivery costs for childhood vaccines reported in the IDCC. Among these studies, the outreach delivery cost per dose ranged from $0.09 to $9.80 in 2024 USD. Table 1 provides summary information on the empirical studies used in the analysis (full details of these studies are provided in Appendix A). In the sample, the average GDP per capita was $1743.57 (standard deviation $1398.95) average population size was 16,700 (std 32,900) and the average diphtheria-tetanus-pertussis (DTP1) coverage was 92.7% (std 6.8%). The best fit model specification was defined as: View this table: View inline View popup Download powerpoint Table 1. Summary characteristics for outreach delivery unit cost per dose data. Note: LI = low-income; LMI = lower middle-income; SIA = supplementary immunization activities; UMI = upper middle-income. Table 2 reports point estimates and standard errors for regression coefficients and other model parameters. All included covariates were statistically significantly different from zero with the exception of the routine delivery modality indicator. Figure 1 displays the in-sample fit comparing observed versus predicted values for the study sample. View this table: View inline View popup Download powerpoint Table 2. Results for regressions of outreach delivery unit cost per dose on predictors. * Significant at 1% level ** Significant at 0.1% level *** Significant at 0.001% level Download figure Open in new tab Figure 1. Comparison of predicted cost per dose and published literature cost per dose for outreach delivery. The fitted model demonstrated increasing costs as price levels (GDP per capita), service delivery volume (under-five population), health system capacity (DTP1 coverage) increased. Doubling per-capita GDP would be associated with a 6.7% increase in the cost per dose of outreach-based delivery, whereas doubling the under-five population would be associated with a 34.4% increase. An increase in DTP1 coverage by one percentage point would be associated with a 7.7% increase in the cost per dose of outreach-based delivery. Figure 2 shows how the estimated outreach-based delivery cost per dose varies with coverage, holding all other covariates constant. Download figure Open in new tab Figure 2. Predicted economic cost per dose in 2024 for routine outreach vaccine delivery by DTP1 (first dose diphtheria-tetanus-pertussis-containing vaccine) coverage for 129 low and middle-income countries. Predicted outcomes For the year 2024, the population-weighted average economic cost per dose was estimated to be $8.65 (95% uncertainty interval $2.33–23.71) for the full costs of routine outreach vaccine delivery across 129 LMICs. By income level, the average predicted economic cost per dose was $2.09 ($0.59–5.47) for low-income countries, $9.91 ($2.59–27.48) for lower middle-income countries, and $10.88 ($2.37–33.93) for upper middle-income countries. Figure 3 presents the country-level cost per dose estimates by GDP per capita and 2025 World Bank income level. The set of country-specific economic cost estimates for outreach delivery cost per dose can be found in Appendix Table B. Download figure Open in new tab Figure 3. Predicted economic cost per dose in 2024 for routine outreach vaccine delivery by gross domestic product (GDP) per capita for 129 low and middle-income countries. Using these predicted routine outreach delivery costs per dose as a proxy for reaching zero-dose children, the costs of fully vaccinating a zero-dose child would be scaled by the number of vaccine doses in the vaccine program. For example, if we assume the vaccine program requires 13 doses (e.g., 3 pentavalent vaccine, 3 oral polio vaccine, 3 pneumococcal conjugate vaccine, 3 rotavirus, 1 measles-containing vaccine) for a child to be considered fully vaccinated, and we assume that zero-dose children can be reached at the average unit cost per dose of outreach delivery, the predicted average economic cost per dose would be multiplied by 13, which would be equivalent to $112.45 ($30.29–308.23) to fully vaccinate a zero-dose child across the study countries. An alternative approach would take into account the observed positive association between coverage and unit costs, and therefore focus on the marginal cost per dose. Under this approach, for a country at 90% DTP1 coverage, the estimated marginal cost per dose of outreach-based delivery is $12.11; multiplied by 13, this would imply a cost of $157.43 to fully vaccinate an unvaccinated child. Discussion For the year 2024, the average economic cost per dose across all LMICs was estimated to be $8.65 ($2.33–23.71) for the full costs of routine outreach vaccine delivery, excluding vaccine costs. These estimates are consistent with the empirical estimates reported in a recent scoping review for the costs of interventions to reach zero-dose children, which ranged from $0.04 to $67 per dose [ 8 , 19 ], but are notably greater than estimates of $2.34 ($0.80–5.47) in 2024 USD to deliver routine childhood vaccinations on average [ 6 ]. These estimates can be useful in providing a broad indication of delivery costs to inform resource allocation to reach currently un- and under-reached populations with needed vaccinations. In the best fit model, the continuous explanatory variables included were GDP per capita, under-five population size, and DTP1 coverage. A possible explanation is that GDP per capita is an indicator of price levels, population is an indicator of service delivery volume, and coverage is an indicator of routine health system capacity and overall strength of the immunization program, all of which could have relevant associations with the costs of vaccine delivery. Unit costs were predicted to be greater for countries with greater GDP per capita, under-five populations, and DTP1 coverage. Finally, we assigned the year of data collection as one of the study-level covariates, which serves as a proxy for time-varying characteristics not captured by other model covariates. Costs were predicted to decrease over time, but the magnitude was small and this coefficient was not significant. However, it is important to note that regression coefficients from this model do not have a causal interpretation. There are several limitations to this analysis. First, the studies we included in the analysis did not have a standardized design, and the resulting heterogeneity was not fully captured by our meta-regression analysis. Therefore, the residual variance could reflect both sampling uncertainty as well as non-sampling error due to methodological heterogeneity. The 19 countries included in the dataset might not be representative of the 129 LMICs to which we extrapolated predicted costs. Therefore, estimates will be less reliable for countries with covariates not included in the analyzed sample. In particular, only two observations from currently-defined upper middle-income countries were included in the dataset used for model fitting. Second, this analysis assumed that the estimated unit costs of vaccine delivery are independent of the number of doses in the immunization program. However, we would expect different costs for vaccines requiring unique immunization visits compared to vaccines that can be administered in the same immunization visit. Finally and importantly, outreach and mobile efforts to reach children already reached by ongoing immunization efforts might not be representative of efforts required to fully vaccinate zero-dose children, who are not reached by the programs informing this analysis by definition. We assume that it might be even more costly to fully vaccinate zero-dose children, as they have proven to be the most challenging for immunization programs to reach. In particular, we expect higher costs in geographically hard-to-reach or conflict-affected settings, which likewise increase the risk of a child being zero-dose. Our cross-sectional analysis suggests that costs increase as coverage increases; if this relationship holds true longitudinally as individual countries increase coverage, then the average cost per dose would increase significantly as countries reach more children. In light of these limitations, this analysis may not fully address the decision- and policy-making needs to improve immunization coverage and equity, and reach the Immunization Agenda 2030 goals. However, the need to make policy choices based on imperfect information is unavoidable, and the estimates we report provide an additional evidence source for budgeting and planning around closing immunization coverage gaps. Our study provides estimates produced via meta-regression analyses that can help countries to improve budgeting and planning for implementation of future interventions to reach zero-dose children, offering critical insights for global health stakeholders and policymakers. Bridging the immunization gap for zero-dose children not only advances universal health coverage but also strengthens health systems and improves population resilience to future public health threats. As countries and partners mobilize resources to close this immunization gap, these cost estimates serve as a valuable foundation for strategic planning, prioritization, and sustainable financing. Data Availability All data produced in the present study are available upon reasonable request to the authors References 1. ↵ World Health Organization . Immunization Agenda 2030: a global strategy to leave no one behind . Last updated: 1 April 2020. [Online] Accessed 6 June 2025 . Available at: https://www.who.int/publications/m/item/immunization-agenda-2030-a-global-strategy-to-leave-no-one-behind . 2. ↵ World Health Organization . The Big Catch-Up: An Essential Immunization Recovery Plan for 2023 and Beyond . Last Updated; 26 July 2023. [Online] Accessed 6 June 2025 . 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Prior Choice Recommendations . Last updated: 27 April 2025. [Online] Accessed 25 May 2025 . Available at: https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations . 17. ↵ Gelman A. Prior distributions for variance parameters in hierarchical models . Bayesian Analysis . 2006 ; 1 ( 3 ): 515 – 34 . doi: 10.1214/06-BA117A . OpenUrl CrossRef Web of Science 18. ↵ Hoffman MD , Gelman A. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo . Journal Of Machine Learning Research . 2014 ; 15 : 1593 – 623 . OpenUrl 19. ↵ UNICEF . Costs of Fully Vaccinating a Child . August 2024 . [Online] Accessed 25 September 2025 . Available at: https://www.unicef.org/documents/costs-fully-vaccinating-child . View the discussion thread. Back to top Previous Next Posted October 07, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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