Stenberg et al. Cost Ef Resour Alloc (2018) 16:11 https://doi.org/10.1186/s12962-018-0095-x Cost Effectiveness and Resource Allocation RESEARCH Open Access Econometric estimation of WHO‑CHOICE country‑specifc costs for inpatient and outpatient health service delivery Karin Stenberg1* , Jeremy A. Lauer1, Georgios Gkountouras2, Christopher Fitzpatrick3 and Anderson Stanciole4 Abstract Background: Policy makers require information on costs related to inpatient and outpatient health services to inform resource allocation decisions. Methods: Country data sets were gathered in 2008–2010 through literature reviews, website searches and a public call for cost data. Multivariate regression analysis was used to explore the determinants of variability in unit costs using data from 30 countries. Two models were designed, with the inpatient and outpatient models drawing upon 3407 and 9028 observations respectively. Cost estimates are produced at country and regional level, with 95% conf- dence intervals. Results: Inpatient costs across 30 countries are signifcantly associated with the type of hospital, ownership, as well as bed occupancy rate, average length of stay, and total number of inpatient admissions. Changes in outpatient costs are signifcantly associated with location, facility ownership and the level of care, as well as to the number of outpa- tient visits and visits per provider per day. Conclusions: These updated WHO-CHOICE service delivery unit costs are statistically robust and may be used by analysts as inputs for economic analysis. The models can predict country-specifc unit costs at diferent capacity levels and in diferent settings. Keywords: Cost, Regression analysis, Estimates, Inpatient, Outpatient Background [1]. Having access to accurate and reliable information Health planners concerned with evidence-informed deci- on the cost of health services can serve various purposes sion making and resource allocation rely on high quality including discussions on afordability and fnancial sus- information regarding the resources needed to imple- tainability, budgeting, cost-efectiveness and cost–beneft ment investment strategies, and comparing these against analysis [2]. current and future budgetary constraints. Information on Literature related to estimates on the so-called ‘unit costs is essential to inform discussions around value for costs’ of specifc health interventions is a growing feld, money and efciency. Unfortunately cost data is sparse yet the transferability of such fndings from one setting to in many settings, especially in low- and middle income another is limited [2, 3]. For example, the Access, Bottle- countries, where ambitious health targets are now being necks, Costs and Equity (ABCE) initiative from the Uni- set for the 2030 Sustainable Development Goals (SDGs). versity of Washington’s Institute for Health Metrics and Challenges include insufcient allocation and/or inef- Evaluation has been collecting primary data on health fcient allocation of resources towards health priorities care facility costs. However, the focus has been limited to Ghana, Kenya, Uganda and Zambia and there is no *Correspondence: [email protected] intention to create generalized global estimates [4–7]. 1 Department of Health Systems Governance and Financing, World Similarly, the recently established Global Health Cost Health Organization, Geneva, Switzerland Full list of author information is available at the end of the article Consortium at the University of Washington (https:// © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/ publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Stenberg et al. Cost Ef Resour Alloc (2018) 16:11 Page 2 of 15 ghcosting.org/) intends to produce unit cost estimates assumptions. Inputs into the service production process that can be adapted to local settings, but its scope is lim- (including technology, prices and production efciency) ited to TB and HIV services, and will not provide cost change over time, and WHO-CHOICE seeks to regularly estimates for general inpatient or outpatient care. provide updated estimates. WHO-CHOICE estimates To our knowledge, the WHO-CHOICE (CHOosing are particularly useful for low- and middle income coun- Interventions that are Cost Efective) project is the only tries that may not have such data readily available. programme seeking to collect and standardise estimates In this paper we describe a process whereby country of the costs related to facility-based service delivery, to data sets were gathered and multivariate regression anal- compare these across countries, and to provide country- ysis was used to explore the determinants of variability in specifc estimates of facility service costs as a global pub- unit costs, in order to produce updated WHO-CHOICE lic good. WHO-CHOICE estimates are produced for all estimates. countries and can therefore be used in settings where no Te previous round of WHO-CHOICE results for local data is available. Estimates are based on modeling facility cost are henceforth referred to as “the frst round of primary and secondary data, and are derived from a analysis” [3, 8, 9]. Estimates are publicly available (http:// model that provides the best ft according to global data. www.who.int/choice) and have been widely used by Such models will not yield perfect predictions for all researchers, academics and analysts at both global and countries, and it is therefore maintained that whenever country level [10, 11]—for example publications see good quality country unit cost data is available from a http://who.int/choice/documents/en/). representative sample, this should be used rather than using the CHOICE predictions. Methods Under WHO-CHOICE, WHO has collated facility cost Te “Methods” section frst describes the data collection data from countries since 2000. Tese data have served process followed by the econometric analysis. to inform estimates for country-specifc costs related to health service utilization—with estimates for cost per inpa- Data collection tient day and outpatient visit. Te service delivery unit cost As a frst step a literature review was undertaken with the estimates are used to support cost-efectiveness analysis aim of identifying variables and methods that should be within the WHO-CHOICE project, among other things. taken into account during data collection and analysis [12]. • Inpatient day Te estimated cost of a hospital bed- Experience from prior work suggested that on the day refects only the “hotel” component of the hospital ground ‘bottom-up’ facility-level estimation is not a cost- cost—i.e. it excludes the cost of drugs and diagnostic efective approach for producing large data sets, given tests but includes costs such as personnel, capital infra- the large costs involved with primary data collection. structure and equipment, laboratory, maintenance and Instead we made use of existing data for secondary anal- other operational costs of the hospital, as well as food ysis. Cost data were gathered 2009–2010 through three costs. Te intent is to produce a measure that covers mechanisms: those components that are assumed to be standardised across diferent diseases and treatments. a. Authors identifed through the literature review were • Outpatient visit Similarly, WHO-CHOICE outpatient directly contacted; costs include components not specifc to the disease b. A public call went out for cost data; and or treatment, but those largely assumed to be stand- c. Websites of public institutions were searched for ardised across disease conditions for which the care is publicly available data. provided: namely personnel, capital infrastructure and equipment, laboratory, maintenance and other opera- Te majority of data was gathered from respondents tional costs of the health facility. In recognition of the to the public call. Most of the sources identifed through fact that costs for equipment, maintenance etc., may the literature review did not report facility-specifc unit vary depending on the setting in which care is pro- costs estimates, but instead presented only the sample vided, inpatient and outpatient care costs are estimated average. Terefore, data from only two studies identifed for diferent types of providers and levels of the health though the literature review were ultimately included in system. the estimation dataset (Additional fle 1: Annex S1). In- depth examination of available online public data bases Te use of standardised estimates for the service deliv- revealed limited usefulness of such data as they gener- ery component of intervention costs ensures that inter- ally lacked several of the variables required to inform the ventions can be compared fairly, using consistent price Stenberg et al. Cost Ef Resour Alloc (2018) 16:11 Page 3 of 15 analysis. Only one database found on-line was considered Respondents’ fles were screened for data quality useful for extracting data [13]. and consistency with the requested data format. Unit Te public call for cost data was released and widely costs were extracted from the data fles. Quality