What are economic costs and when should they be used in health economic studies?

Economic analyses of healthcare interventions are an important consideration in evidence-based policymaking. A key component of such analyses is the costs of interventions, for which most are familiar with using budgets and expenditures. However, economic theory states that the true value of a good/service is the value of the next best alternative forgone as a result of using the resource and therefore observed prices or charges do not necessarily reflect the true economic value of resources. To address this, economic costs are a fundamental concept within (health) economics. Crucially, they are intended to reflect the resources' opportunity costs (the forgone opportunity to use those resources for another purpose) and they are based on the value of the resource's next-best alternative use that has been forgone. This is a broader conceptualization of a resource's value than its financial cost and recognizes that resources can have a value that may not be fully captured by their market price and that by using a resource it makes it unavailable for productive use elsewhere. Importantly, economic costs are preferred over financial costs for any health economic analyses aimed at informing decisions regarding the optimum allocation of the limited/competing resources available for healthcare (such as health economic evaluations), and they are also important when considering the replicability and sustainability of healthcare interventions. However, despite this, economic costs and the reasons why they are used is an area that can be misunderstood by professionals without an economic background. In this paper, we outline to a broader audience the principles behind economic costs and when and why they should be used within health economic analyses. We highlight that the difference between financial and economic costs and what adjustments are needed within cost calculations will be influenced by the context of the study, the perspective, and the objective.

Out-of-pocket Expenses and Time Spent on Clinic Visits Among HIV Pre-exposure Prophylaxis Users and Other Clinic Attendees in Eswatini

User costs constitute a barrier to the uptake of HIV pre-exposure prophylaxis (PrEP), but their magnitude appears rarely assessed. In this prospective observational study, we assessed self-reported out-of-pocket expenses (OOPE) and time spent on clinic visits during a PrEP demonstration project in Eswatini. At six public primary care clinics, 240 PrEP users and other clinic attendees were interviewed after a clinic visit. Among the 79.2% of clinic attendees reporting any medical OOPE (e.g., expenses for consultations or drugs) and/or non-medical OOPE (e.g., expenses for transport, food, or phone use), the median total OOPE was $1.36 (IQR 0.91-1.96). Non-medical OOPE occurred mostly due to transport expenses. The median travel time for a clinic visit was 1.0 h (IQR 0.67-2.0). The median time spent in the clinic was 2.0 h (IQR 1.15-3.0). The median opportunity cost of a clinic visit was $7.54 (IQR 5.42-11) when valuing time spent on a clinic visit with Eswatini's per-capita gross domestic product. Our findings can guide measures to reduce the user costs of PrEP in Eswatini and other contexts in which oral PrEP is provided through health care facilities.

Does military expenditure crowd out health-care spending? Cross-country empirics

The trade-off between military expenditure and public health spending has remained an unsettled empirical issue. This paper investigates whether military expenditure has crowded out public health spending in 116 countries (including a subsample of 87 non-OECD countries) over the period 2000-2017. Through our system generalized methods of moments (GMM) estimations, we find that military expenditure, whether it is measured on a per-capita basis or as a proportion of total government expenditure, has a positive impact on the demand for health care. Nonetheless, we find a significant crowding-out effect of military expenditure on domestic government health spending by taking into account government fiscal capacity. The evidence we present supports the long-standing view that military expenditure has a particular ability to compete government financial resources away from publicly funded health spending. By interacting the military expenditure variable with income per capita, we find that an increase in income per capita has neutralized the crowding-out effect of military expenditure on domestic government health spending - less well-off countries stand to suffer most, and wealthy ones stand to suffer least, from the crowding-out effect. The crowding-out effect is statistically more specific to middle- and low-income countries in our samples.

Eco-efficiency in Brazilian Amazonian agriculture: opportunity costs of degradation and protection of the environment

Aiming to assist the environmental sustainability of the Brazilian Amazonian agriculture, this article developed an eco-efficiency index, indicating the possible limits to maximize economic and environmental objectives, taking into account the best practices in the municipalities of the region. Shadow prices of degraded areas and forest preservation were also estimated using data envelopment analysis with directional distance functions. The results indicate that, on average, the analyzed municipalities are able to expand the production and the forest areas by 38% and reduce degraded areas and their inputs in the same proportion. The shadow prices allowed the estimation of the annual opportunity cost of the degraded areas and the preservation of the forest on the farms. The first, US$ 3,131,571, represented 0.04% of the annual output value, indicating that the internalization of that cost should be a low burden for the eco-efficient producer. The second, the total cost of preserving 80% of the area of property, represented US$ 120,890,662 or 1.7% of the annual income of the biome producers studied. Therefore, the main conclusion of this work is that the internalization of negative and positive externalities of agricultural production in the Amazonian biome does not make agricultural production economically unfeasible in the region. In addition, the reimbursement of damages avoided by carbon sequestration, through the Clean Development Mechanism (CDM) established by the Kyoto Conference, should further increase the economic and environmental sustainability of agriculture in the area.

Seizing the opportunity: Lifespan differences in the effects of the opportunity cost of time on cognitive control

Previous work suggests that lifespan developmental differences in cognitive control reflect maturational and aging-related changes in prefrontal cortex functioning. However, complementary explanations exist: It could be that children and older adults differ from younger adults in how they balance the effort of engaging in control against its potential benefits. Here we test whether the degree of cognitive effort expenditure depends on the opportunity cost of time (average reward rate per unit time): if the average reward rate is high, participants should withhold cognitive effort whereas if it is low, they should invest more. In Experiment 1, we examine this hypothesis in children, adolescents, younger, and older adults, by applying a reward rate manipulation in two cognitive control tasks: a modified Erikson Flanker and a task-switching paradigm. We found that young adults and adolescents reflexively withheld effort when the opportunity cost of time was high, whereas older adults and, to a lesser degree children, invested more resources to accumulate reward as quickly as possible. We tentatively interpret these results in terms of age- and task-specific differences in the processing of the opportunity cost of time. We qualify our findings in a second experiment in younger adults in which we address an alternative explanation of our results and show that the observed age differences in effort expenditure may not result from differences in task difficulty. To conclude, we think that our results present an interesting first step at relating opportunity costs to motivational processes across the lifespan. We frame the implications of further work in this area within a recent developmental model of resource-rationality, which points to developmental sweet spots in cognitive control.

[Cost analysis of inpatient versus outpatient intravenous antibiotic treatment for periprosthetic joint infections : A simulation]

Hintergrund

Die parenterale Antibiotikagabe im Rahmen der Therapie von periprothetischen Infektionen erfordert in der Regel eine stationäre Behandlung und geht mit hohen Kosten einher.

Fragestellung

Es wurden tatsächliche stationäre Behandlungskosten („inpatient parenteral antibiotic therapy“ [IPAT]) mit simulierten Kosten einer ambulanten Behandlung („outpatient parenteral antibiotic therapy“ [OPAT]) von Patienten mit periprothetischen Gelenkinfektionen verglichen. Die Auswertung erfolgte aus Perspektive der Kostenträger (gesetzliche Krankenversicherung [GKV]) und Leistungserbringer (Krankenhäuser).

Material und Methoden

Die Analyse und Simulation erfolgten auf Grundlage einer ICD-10 (Internationale statistische Klassifikation der Krankheiten und verwandter Gesundheitsprobleme, 10. Revision) für das Behandlungsjahr 2015 mit der Diagnose T84.

Ergebnisse

Die simulierte Reduktion von 159 Bettentagen bei den in die Studie eingeschlossenen 12 Patienten erbrachte aus Sicht der Kostenträger eine Reduktion der Gesamtkosten um >18.000 €. Aus Perspektive der Leistungserbringer verbesserte sich der Reinerlös um >22.000 €. Die Gesamtkosten der OPAT für den Kostenträger beliefen sich auf >57.000 €. Für den Leistungserbringer zeigte sich in der Differenz von Poliklinikerlös und -kosten der OPAT ein Verlust von >1500 €.

Diskussion

Die OPAT ist für Leistungserbringer insgesamt finanziell vorteilhaft. Weitere Vorteile durch Opportunitätskosten erscheinen interessant. Für den Kostenträger ist die OPAT insbesondere durch die ambulanten Medikamentenkosten mit einem finanziellen Mehraufwand verbunden. Der niedergelassene Sektor sollte durch die anzunehmende Mehrbelastung ebenso wie der anzunehmende Patientenkomfort bedacht werden.

A labor requirements function for sizing the health workforce

BACKGROUND

Ensuring healthcare delivery is dependent both on the prediction of the future demand for healthcare services and on the estimation and planning for the Health Human Resources needed to properly deliver these services. Although the Health Human Resources planning is a fascinating and widely researched topic, and despite the number of methodologies that have been used, no consensus on the best way of planning the future workforce requirements has been reported in the literature. This paper aims to contribute to the extension and diversity of the range of available methods to forecast the demand for Health Human Resources and assist in tackling the challenge of translating healthcare services to workforce requirements.

METHODS

A method to empirically quantify the relation between healthcare services and Health Human Resources requirements is proposed. For each one of the three groups of specialties identified-Surgical specialties, Medical specialties and Diagnostic specialties (e.g., pathologists)-a Labor Requirements Function relating the number of physicians with a set of specialty-specific workload and capital variables is developed. This approach, which assumes that health managers and decision-makers control the labor levels more easily than they control the amount of healthcare services demanded, is then applied to a panel dataset comprising information on 142 public hospitals, during a 12-year period.

RESULTS

This method provides interesting insights on healthcare services delivery: the number of physicians required to meet expected variations in the demand for healthcare, the effect of the technological progress on healthcare services delivery, the time spent on each type of care, the impact of Human Resources concentration on productivity, and the possible resource allocations given the opportunity cost of the physicians' labor.

CONCLUSIONS

The empirical method proposed is simple and flexible and produces statistically strong models to estimate Health Human Resources requirements. Moreover, it can enable a more informed allocation of the available resources and help to achieve a more efficient delivery of healthcare services.

Assessing the opportunity costs of patients with multidrug-resistant organisms in hospitals

OBJECTIVES

The concept of opportunity cost can be applied to the utilization of hospital beds with special focus on patients colonized or infected with multidrug-resistant organisms. Blocked beds due to isolation measures or increased length of stay may result in opportunity costs if newly arriving patients have to be rejected and the hospital is confronted with revenue foregone. However, the amount of these costs is unclear, since different approaches are used in the literature to determine the respective costs. Our paper develops a concept to assess opportunity costs from the perspective of a hospital.

METHODS

The analysis is two-stage. In a first step, the probability of rejecting a patient due to over-occupancy in a hospital is calculated with a queuing model and a Monte Carlo simulation taking various assumptions into account. In a second step, the amount of the opportunity costs is calculated as an expected value applying a stochastic approach based on a potential patient pool.

RESULTS

Opportunity costs will occur only with a probability that is influenced, among others, by current bed occupancy rates. They have to be measured by average net revenue foregone, i.e., by the difference between average revenue foregone and average costs avoided.

CONCLUSIONS

Previous studies have a tendency of overestimating the occurrence or the size of opportunity costs with regard to the use of hospital beds. Nonetheless, its influence on the hospital budget is crucial and should be determined exactly.

Adjusting patients streaming initiated by a wait time threshold in emergency department for minimizing opportunity cost

Purpose Two different systems for streaming patients were considered to improve efficiency measures such as waiting times (WTs) and length of stay (LOS) for a current emergency department (ED). A typical fast track area (FTA) and a fast track with a wait time threshold (FTW) were designed and compared effectiveness measures from the perspective of total opportunity cost of all patients' WTs in the ED. The paper aims to discuss these issues. Design/methodology/approach This retrospective case study used computerized ED patient arrival to discharge time logs (between July 1, 2009 and June 30, 2010) to build computer simulation models for the FTA and fast track with wait time threshold systems. Various wait time thresholds were applied to stream different acuity-level patients. National average wait time for each acuity level was considered as a threshold to stream patients. Findings The fast track with a wait time threshold (FTW) showed a statistically significant shorter total wait time than the current system or a typical FTA system. The patient streaming management would improve the service quality of the ED as well as patients' opportunity costs by reducing the total LOS in the ED. Research limitations/implications The results of this study were based on computer simulation models with some assumptions such as no transfer times between processes, an arrival distribution of patients, and no deviation of flow pattern. Practical implications When the streaming of patient flow can be managed based on the wait time before being seen by a physician, it is possible for patients to see a physician within a tolerable wait time, which would result in less crowded in the ED. Originality/value A new streaming scheme of patients' flow may improve the performance of fast track system.

Health care input constraints and cost effectiveness analysis decision rules

Results of cost effectiveness analyses (CEA) studies are most useful for decision makers if they face only one constraint: the health care budget. However, in practice, decision makers wishing to use the results of CEA studies may face multiple resource constraints relating to, for instance, constraints in health care inputs such as a shortage of skilled labour. The presence of multiple resource constraints influences the decision rules of CEA and limits the usefulness of traditional CEA studies for decision makers. The goal of this paper is to illustrate how results of CEA can be interpreted and used in case a decision maker faces a health care input constraint. We set up a theoretical model describing the optimal allocation of the health care budget in the presence of a health care input constraint. Insights derived from that model were used to analyse a stylized example based on a decision about a surgical robot as well as a published cost effectiveness study on eye care services in Zambia. Our theoretical model shows that applying default decision rules in the presence of a health care input constraint leads to suboptimal decisions but that there are ways of preserving the traditional decision rules of CEA by reweighing different cost categories. The examples illustrate how such adjustments can be made, and makes clear that optimal decisions depend crucially on such adjustments. We conclude that it is possible to use the results of cost effectiveness studies in the presence of health care input constraints if results are properly adjusted.