Improved calibration estimators for the total cost of health programs and application to immunization in Brazil

Abstract

Multi-stage/level sampling designs have been widely used by survey statisticians as a means of obtaining reliable and efficient estimates at a reasonable implementation cost. This method has been particularly useful in National country-wide surveys to assess the costs of delivering public health programs, which are generally originated in different levels of service management and delivery. Unbiased and efficient estimates of costs are essential to adequately allocate resources and inform policy and planning. In recent years, the global health community has become increasingly interested in estimating the costs of immunization programs. In such programs, part of the cost correspond to vaccines and it is in most countries procured at the central level, while the rest of the costs are incurred in states, municipalities and health facilities, respectively. As such, total program cost is a result of adding these costs, and its variance should account for the relation between the totals at the different levels. An additional challenge is the missing information at the various levels. A variety of methods have been developed to compensate for this missing data. Weighting adjustments are often used to make the estimates consistent with readily-available information. For estimation of total program costs this implies adjusting the estimates at each level to comply with the characteristics of the country. In 2014, A National study to estimate the costs of the Brazilian National Immunization Program was initiated, requested by the Ministry of Health and with the support of international partners. We formulate a quick and useful way to compute the variance and deal with missing values at the various levels. Our approach involves calibrating the weights at each level using additional readily-available information such as the total number of doses administered. Taking the Brazilian immunization costing study as an example, this approach results in substantial gains in both efficiency and precision of the cost estimate.

National estimates from the Youth ’19 Rangatahi smart survey: A survey calibration approach

“…In such case we recommend the use of calibrated estimators as they yield confidence intervals that are significantly narrower than those obtained using the original sampling weights. This is a well-known property of calibration since it reduces the uncertainty in the sample by incorporating information known prior to the study [ 27 , 31 , 32 ].…”

Modeling the cost-effectiveness of maternal acellular pertussis immunization (aP) in different socioeconomic settings: A dynamic transmission model of pertussis in three Brazilian states

“…The reasoning for this finding is that as fewer notified cases occur, due to high infant vaccine coverage, there is less disease burden to be averted by maternal immunization. However, current infant wP vaccine coverage levels have not significantly decreased transmission, leading to an increased disease burden in recent years, in particular among infants <1 year who are most likely to be symptomatic, require hospitalization, and die [4] , [31] , [32] . Given currently observed pertussis incidence and infant coverage, maternal aP immunization would be a cost-effective strategy if the cost-effectiveness benchmark is each state’s GDP per capita, or even half of GDP per capita.…”

“…An additional 5% wastage rate, as recommended by WHO and varied between 0 and 15% in sensitivity analyses, brings the cost for each vaccine dose to USD 2.84. The cost of delivering a dose of vaccine for any individual was obtained from a primary study conducted in 2013 in Brazil which estimated the cost of the immunization program in the country [32] .…”

Costs of childhood vaccine delivery in Iraq: a cross-sectional study

“…To account for the underlying relationship between costs and volume (ie, doses delivered), we extrapolated the costs to health facilities beyond the sample using calibration estimation following methods described by Rivera-Rodriguez et al in 2018 and 2019. 16 17 As explained in guidelines by Resch et al , this approach only requires information on the total volume of doses delivered nationally and the total number of health facilities. 18 Records on the number of doses delivered were inconsistently maintained at sampled facilities and unavailable for non-sampled facilities.…”