The cost determinants of routine infant immunization services: a meta-regression analysis of six country studies

Better evidence on immunization costs serves several goals—in the context of new vaccine adoption and service expansion, accurate cost estimates are critical inputs for cost-effectiveness and budget impact analyses of proposed policies. Understanding variation in costs across service outlets can also provide insight on site operations and suggest opportunities for improving efficiency. Despite these needs, there have been few empirical studies with sufficient sample size to provide precise cost estimates or describe inter-site cost variation, and studies have commonly used national-level budgeting data to investigate determinants and trends in immunization costs [1‐3]. Prior empirical studies have found substantial variation both within [4, 5] and between countries [6], though some of these studies are now many years old.

The Expanded Program on Immunization Costing and Financing (EPIC) Project was designed to fill this knowledge gap, providing detailed data on routine immunization costs and financing in a large, representative sample of immunization sites in six countries (Benin, Ghana, Honduras, Moldova, Uganda, and Zambia) [7]. Information from these studies has already been used to improve information on unit costs [8‐11], cost trends [12, 13], and financing [14, 15] within individual countries. We synthesized data from these country studies to create a unique pooled dataset of 316 sites to explore cross-country determinants of costs. We use these data to describe country- and site-level variation in routine immunization costs and identify systematic cost differences related to site operating characteristics. Given the observational nature of the data, the relationships we estimate are purely descriptive. However, the larger sample size allows us to provide a fine-grained description of how costs vary between similar sites, which in turn can suggest potential approaches for improving the efficiency of service delivery and allow a more precise understanding of resource needs.

Costs per dose ranged from $2.75 (2011 USD) in Benin and Uganda to $13.63 in Moldova. Costs per DTP3 ranged from $27 in Uganda to $139 in Moldova. These values are substantially greater than those reported by earlier studies [4‐6, 29], reflecting both higher price levels and expanded vaccine schedules. Inter-country differences in this study are likely due to similar factors, with countries with higher costs per DTP3—Moldova and Honduras—experiencing comparatively higher income levels and longer vaccine schedules. Some of our estimates differ from earlier estimates from the EPIC country studies [7], reflecting refinements to cost and outcomes data during data cleaning. Different approaches to calculating the average cost per outcome (Table 2) also contributed to variation in estimates, due to the strong relationship between service volume and the site-level cost per outcome.

Regression analyses revealed several predictors with a statistically discernable relationship with costs. In each country, higher service volume was strongly associated with lower average costs, with output elasticity ranging from 1.27 to 2.52 across the six countries, with a mean value of 1.63, and the largest 20% of sites in each country had a cost per dose that was on average 61% lower than that observed for the smallest 20% of sites. For most countries there were many small sites with high average costs, and these small sites exhibited substantial variation in unit costs. The reduction in unit costs associated with higher service volume was only minimal for sites at the upper end of the service volume distribution, and these large sites exhibited only minor variation in unit costs. While this suggests that greater reductions in average costs might be achieved through efforts to improve efficiency in small sites, it is not clear that these sites should be prioritized, as large total cost reductions might be possible with only small reductions in unit costs at large sites.

Higher health system level (proxied by hospital status) was associated with higher costs, possibly reflecting differences in available staffing and infrastructure. Outreach-based delivery was also associated with higher costs, possibly due to the additional resources required to deliver mobile services while maintaining infrastructure for facility-based provision. We designed the staffing index to test whether additional productivity of more highly trained and senior staff would counterbalance their higher salaries, yet the positive coefficient on this variable suggests otherwise. Greater distance to vaccine collection and more days open per week (implying lower daily service volume) were both associated with higher costs. A higher fraction of costs for management and greater completion of the vaccination schedule (proxied by the DTP3:doses ratio) were both found to have a strong positive relationship with costs. The positive coefficient on the dedication index implied lower cost for sites where staff are spread across multiple services (rather than being fully committed to immunization services), consistent with economies of scope and a greater ability to adjust staffing with variable demand. Some of these relationships were stronger than others, but they were generally robust to changes in regression specification. After controlling for site characteristics, there was little difference between rural and non-rural sites, despite rural sites having substantially higher costs per dose in crude comparisons. Per capita GDP was found to have a positive relationship with costs per dose. This likely reflects differences in price levels between countries, but it could also be related to the many other factors that vary with country income level. As only six countries were represented in this sample, we were unable to decompose the effect of these country-level factors.

The negative coefficient estimated for coverage was unexpected. We expected costs to increase with coverage, reasoning that improving coverage would require attracting progressively harder-to-reach clients, driving up marginal costs. It is possible that for a given service volume, sites with lower coverage have more widely dispersed or immunization resistant catchment populations, leading to higher average costs. Alternately, if staffing and other immunization resources were determined by catchment population size, lower attendance could produce both lower coverage and higher costs per outcome. While mismeasurement of reported coverage dictates that no firm conclusions should be drawn, the importance of coverage for immunization program strategy suggests that further investigation would be valuable.

Several relationships described above have been investigated in country-level studies [12, 13], with generally consistent results. While we have described possible mechanisms for these findings, the ability of our study design to identify causal relationships is weak. Many of the findings are consistent with multiple explanations (including omitted variable bias) and should be viewed as hypothesis generating rather than confirming any specific causal relationship. For this reason, the total and average cost curves we estimate for each country (Fig. 1) demonstrate how costs vary between sites of different size, but additional (and potentially unwarranted) assumptions are required to interpret them as the cost curves that would be experienced by sites attempting to increase service volume. Other limitations include the exclusion of predictors that could not be calculated for a sufficient number of sites to be considered in the analysis (for example, vaccine stock-outs and wastage rates) or that primarily varied at the country level (for example, input prices and vaccine schedules), and for which we had minimal power to investigate. While we attempted to create explanatory variables with consistent interpretations, it is likely that some of these constructs varied between countries, such as the definition of outreach services, or hospital status. Finally, the results for Ghana showed noticeably greater unexplained variation compared to the other countries (Fig. 1, Additional file 1: Figure S2), yet the reasons for this are unclear.

The EPIC Project is the largest study to systematically investigate routine immunization costs. Prior studies of immunization costs have been hampered by small sample sizes, producing noisy average cost estimates and providing little systematic evidence on the variation in costs within countries. By enrolling a large number of immunization sites and using standardized data collection approaches across countries, the EPIC Project provides a unique opportunity to investigate and understand variation in the costs of immunization service delivery as well as generate more precise national cost estimates. This analysis examined many features of sites and their operating environments, but further investigation of these data is possible. The combined EPIC data are freely available for use by other investigators [19], with the hope that open access will maximize the utility of these data for understanding the costs of routine immunization services. The major findings of this study have relevance for policy makers at country and global levels. The substantial variation within and between countries suggests it is challenging now to use a single number to represent average immunization costs. This variation can also lead to an open dialogue about ways to reduce variation and improve value for money in immunization services.