Erik Landaas

Economic evaluation of New Rural Cooperative Medical Scheme in China

china pic

By Boshen Jiao

In China, while the private health insurance is growing rapidly, the government-funded basic health insurance still dominates the health care landscape. Chinese government defines three types of beneficiaries: urban employees, urban residents, and rural residents. Accordingly, three main types of healthcare coverage plans were implemented in China: the Urban Employee Basic Medical Insurance, the Urban Resident Basic Medical Insurance, and the New Rural Cooperative Medical Scheme (NCMS).

The NCMS, which was initiated in 2003 and financed by both governments and individuals, was specifically designed for rural residents in China. In some sense, the Chinese government can feel proud since 98% of the rural residents are covered and this, undoubtedly, has been viewed as a great success. In particular, many of the newly covered individuals are considered to be poor and underserved, with a long history of struggling for access to basic health care.

However, the health and economic consequence of the NCMS might not be that pleasing. While the effectiveness for mortality reduction remained controversial based on current scientific evidence, the NCMS resulted in a 61% increase in out-of-pocket spending. Given the fact that the NCMS has finite resources and impacts a large number of lives, it was critical to do a “thought experiment” and assess the cost-effectiveness of the NCMS. This is the subject of a paper I recently published with Dr. Jinjing Wu from the Asian Demographic Research Institute at Shanghai University and several coauthors from the Columbia Mailman School of Public Health. This paper, titled “The cost-effectiveness analysis of the New Rural Cooperative Medical Scheme in China,” was recently published in PloS One.

Initial estimates of NCMS’s effect on mortality were based on quasi-experimental studies that produced conflicting results. Some argued that NCMS significantly decreased the death rate among the elderly in the eastern region, while the other study using a nationally representative sample concluded to have no statistically significant effect. Although it was tempting to embrace the favorable results, our investigators decided to take on the less-favorable study. We made this call mainly because the nationally representative sample was derived from the Disease Surveillance Point system which was widely accepted as a very reliable data source. Besides, we hoped to draw from the whole country, rather than only focusing on East China where more economic resources and better healthcare are offered. In addition to the effect on mortality rate, the NCMS had proved to successfully lower the risk of hypertension, which was also included as an effectiveness parameter in our model.

Because of uncertainty around its effect on rural residents’ survival, it is likely that the NCMS is not cost-effective. Based on our analysis, the NCMS can only buy one more QALY for rural residents at the social price of 71,480 international (Int) dollars (Note: the costs and economic benefits were converted into 2013 Int dollars using purchasing power parity exchange rate reported by the World Bank). This is not optimal for China. If we believe that three times per capita GDP can be a fair willingness-to-pay threshold (Int$845,659), the NCMS had only a 33% chance to be cost-effective. The results were not surprising, however, nonetheless disappointing. One possibility that we did not explore is that the elderly benefit the most from NCMS. Using a nationally representative sample, however, the NCMS is plausibly costly for the society and failed to produce sufficient health benefits.

We discussed the reasons why the NCMS appears to be inefficient. Current literature described the NCMS as providing catastrophic coverage that mostly covers inpatient services. People may barely use the preventative care or other necessary outpatient services, which would plausibly lead to severe illness and costly complications in the future. Moreover, the NCMS is associated with high copayments, which restricts low-income rural residents’ access to health care and fails to reduce out-of-pocket expenses. We concluded that, while the Chinese government indeed achieved a great success in coverage expansion, the program’s efficiency should be a consideration for future improvements. In order to achieve this goal, cost-effectiveness analysis could be a useful tool when designing the plan.

Our study presented an overall picture of the cost-effectiveness of the NCMS, in which the effect was estimated based on an aggregated of the data collected from different regions. However, the heterogeneity across the regions, particularly at the county level, would need to be taken into account for the future study. This is because the county governments play a critical role in financing for the NCMS, and their budget constraint for the plan has a fundamental effect on the design and implementation of it. As a consequence, the health outcome of NCMS may vary dramatically across the counties. Our analysis would have been enriched and would have provided more informative policy implications if the county level data can be obtained.

Economic Evaluation Methods Part I: Interpreting Cost-Effectiveness Acceptability Curves and Estimating Costs

By Erik Landaas, Elizabeth Brouwer, and Lotte Steuten

One of the main training activities at the CHOICE Institute at the University of Washington is to instruct graduate students how to perform economic evaluations of medical technologies. In this blog post series, we give a brief overview of two important economic evaluation concepts. Each one of the concepts are mutually exclusive and are meant to stand alone. The first of this two-part series describes how to interpret a cost-effectiveness acceptability curve (CEAC) and then delves into ways of costing a health intervention. The second part of the series will describe two additional concepts: how to develop and interpret cost-effectiveness frontiers and how multi-criteria decision analysis (MCDA) can be used in Health Technology Assessment (HTA).

Cost-Effectiveness Acceptability Curve (CEAC)

The CEAC is a way to graphically present decision uncertainty around the expected incremental cost-effectiveness of healthcare technologies. A CEAC is created using the results of a probabilistic analysis(PA).[1] PA involves simultaneously drawing a set of input parameter values by randomly sampling from each parameter distribution, and then storing the model results. This is repeated many times (typically 1,000 to 10,000), resulting in a distribution of outputs that can be graphed on the cost-effectiveness plane. The CEAC reflects the proportion of results that are considered ‘favorable’ (i.e. cost effective) in relation to a given cost-effectiveness threshold.

The primary goal of a CEAC graph is to inform coverage decisions among payers that are considering a new technology, comparing one or more established technologies that may include the standard of care. The CEAC enables a payer to determine, over a range of willingness to pay (WTP) thresholds, the probability that a medical technology is considered cost-effective in comparison to its appropriate comparator (e.g. usual care), given the information available at the time of the analysis. A WTP threshold is generally expressed in terms of societal willingness to pay for an additional life year or quality-adjusted life year (QALY) gained. In the US, WTP thresholds typically range between $50,000 – $150,000 per QALY.

The X-axis of a CEAC represents the range of WTP thresholds. The Y-axis represents the probability of each comparator being cost-effective at a given WTP threshold, and ranges between 0% and 100%. Thus, it simply reflects the proportion of simulated ICERs from the PA that fall below the corresponding thresholds on the X-axis.

Figure 1. The Cost-Effectiveness Acceptability Curve

CEAC

Coyle, Doug, et al. “Cost-effectiveness of new oral anticoagulants compared with warfarin in preventing stroke and other cardiovascular events in patients with atrial fibrillation.” Value in health 16.4 (2013): 498-506.

Figure 1 shows CEACs for five different drugs, making it easy for the reader to see that at the lower end of the WTP threshold range (i.e. $0 – $20,000 per QALY), warfarin has the highest probability to be cost-effective (or in this case “optimal”). At WTP values >$20,000 per QALY, dabigatran has the highest probability to be cost-effective. All the other drugs have a lower probability of being cost-effective compared to warfarin and dabigatran at every WTP threshold. The cost-effectiveness acceptability frontier in Figure 1 follows along the top of all the curves and shows directly which of the five technologies has the highest probability of being cost-effective at various levels of the WTP thresholds.

To the extent that the unit price of the technology influences the decision uncertainty, a CEAC can offer insights to payers as well as manufacturers as they consider a value-based price. For example, a lower unit price for the drug may lower the ICER and, all else equal, this increases the probability that the new technology is considered cost-effective at a given WTP threshold. Note, that when new technologies are priced such that the ICER falls just below the WTP for a QALY, (e.g. an ICER of $99,999 when the WTP is $100,000) the decision uncertainty tends to be substantial, often around 50%. If decision uncertainty is perceived to be ‘unacceptably high’, it can be recommended to collect further information to reduce decision uncertainty. Depending on the drivers of decision uncertainty, for example in case of stochastic uncertainty in the efficacy parameters, performance-based risk agreements (PBRAs) or managed entry schemes may be appropriate tools to manage the risk.

Cost estimates

The numerator of most economic evaluations for health is the cost of a technology or intervention. There are several ways to arrive at that cost, and choice of method depends on the context of the intervention and the available data.

Two broadly categorized methods for costing are the bottom-up methodand the top-down method. These methods, described below, are not mutually exclusive and may complement each other, although they often do not produce the same results.

costs

Source of Table: Mogyorosy Z, Smith P. The main methodological issues in costing health care services: a literature review. 2005.

The bottom-up method is also known as the ingredients approach or micro-costing. In this method, the analyst identifies all the items necessary to complete an intervention, such as medical supplies and clinician time, and adds them up to estimate the total cost. The main categories to consider when calculating costs via the bottom-up method are medical costs and non-medical costs. Medical costs can be direct, such as the supplies used to perform a surgery, or indirect, such as the food and bed used for inpatient care. Non-medical costs often include costs to the patient, such as transportation to the clinic or caregiver costs. The categories used when estimating the total cost of an intervention will depend on the perspective the analyst takes (perspectives include patient, health system, or societal).

The bottom-up approach can be completed prospectively or retrospectively, and can be helpful for planning and budgeting. Because the method identifies and values each input, it allows for a clear breakdown as to where dollars are being spent. To be accurate, however, one must be able to identify all the necessary inputs for an intervention and know how to value capital inputs like MRI machines or hospital buildings. The calculations may also become unwieldy on a very large scale. The bottom-up approach is often used in global health research, where medical programs or governmental agencies supply specific items to implement an intervention, or in simple interventions where there are only a few necessary ingredients.

The top-down estimation approach takes the total cost of a project and divides it by the number of service units generated. In some cases, this is completed simply looking at the budget for a program or an intervention and then dividing that total by the number of patients. The top-down approach is useful because it is a simple, intuitive measurement that captures the actual amount of money spent on a project and the number of units produced, particularly for large projects or organizations. Compared to the bottom-up approach, the top-down approach can be much faster and cheaper. The top-down approach can only be used retrospectively, however, and may not allow for the breakdown of how the money was spent or be able to identify variations between patients.

While the final choice will depend on several factors, it makes sense to try and think through (or model) which of the cost inputs are likely to be most impactful on the model results. For example, the costs of lab tests may most accurately be estimated by a bottom-up costing approach. However, if these lab costs are likely to be a fraction of the cost of treatment, say a million dollar cure for cancer, then going through the motions of a bottom-up approach may not be the most efficient way to get your PhD-project done in time. In other cases, however, a bottom-up approach may provide crucial insights that move the needle on the estimated cost-effectiveness of medical technologies, particularly in settings where a lack of existing datasets is limiting the potential of cost-effectiveness studies to inform decisions on the allocation of scarce healthcare resources.

[1]Fenwick, Elisabeth, Bernie J. O’Brien, and Andrew Briggs. “Cost‐effectiveness acceptability curves–facts, fallacies and frequently asked questions.” Health economics 13.5 (2004): 405-415.

Commonly Misunderstood Concepts in Pharmacoepidemiology

By Erik J. Landaas, MPH, PhD Student and Naomi Schwartz, MPH, PhD Student

Epidemiologic methods are central to the academic and research endeavors at the CHOICE institute. The field of epidemiology fosters the critical thinking required for high quality medical research. Pharmacoepidemiology is a sub-field of epidemiology and has been around since the 1970’s. One of the driving forces behind the establishment of pharmacoepidemiology was the Thalidomide disaster. In response to this tragedy, laws were enacted that gave the FDA authority to evaluate the efficacy of drugs. In addition, drug manufacturers were required to conduct clinical trials to provide evidence of a drug’s efficacy. This spawned a new and important body of work surrounding drug safety, efficacy, and post-marketing surveillance.[i]

In this article, we break down three of the more complex and often misunderstood concepts in pharmacoepidemiology: immortal time bias, protopathic bias, and drug exposure definition and measurement.

Immortal Time Bias

In pharmacoepidemiology studies, immortal time bias typically arises when the determination of an individual’s treatment status involves a delay or waiting period during which follow-up time is accrued. Immortal time bias is a period of follow-up during which, by design, the outcome of interest cannot occur. For example, the finding that Oscar winners live longer than non-winnersis a result of immortal time bias. In order for an individual to win an Oscar, he/she must live long enough to receive the award. A pharmacoepidemiology example of this is depicted in Figure 1. A patient who receives a prescription may survive longer because he/she must live long enough to receive a prescription while a patient who does not receive a prescription has no survival requirements. The most common way to avoid immortal time bias is to use a time-varying exposure variable. This allows subjects to contribute to both unexposed (during waiting period) and exposed person time.

Figure 1. Immortal Time Bias

Lévesque, Linda E., et al. “Problem of immortal time bias in cohort studies: example using statins for preventing progression of diabetes.” Bmj 340 (2010): b5087.

Protopathic Bias or Reverse Causation

Protopathic bias occurs when a drug of interest is initiated to treat symptoms of the disease under study before it is diagnosed. For example, early symptoms of inflammatory bowel disease (IBD) are often consistent with the indications for prescribing proton pump inhibitors (PPIs). Thus, many individuals who develop IBD have a history of PPI use. A study to investigate the association between PPIs and subsequent IBD would likely conclude that taking PPIs causes IBD when, in fact, the IBD was present (but undiagnosed) before the PPIs were prescribed. This scenario is illustrated by the following steps:

Patient has early symptoms of an underlying disease (e.g. acid reflux)
Patient goes to his/her doctor and gets a drug to address symptoms (e.g. PPI)
Patient goes on to develop a diagnosis of having IBD (months or even years later)

It is easy to conclude from the above scenario that PPIs cause IBD, however the acid reflux was actually a manifestation of underlying IBD that was not yet diagnosed. Protopathic bias occurs in this case because of the lag time between first symptoms and diagnosis. One effective way to address protopathic bias is by excluding exposures during the prodromal period of the disease of interest.

Drug Exposure Definition and Measurement

Defining and classifying exposure to a drug is critical to the validity of pharmacoepidemiology studies. Most pharmacoepidemiology studies use proxies for drug exposure, because it is often impractical or impossible to measure directly (e.g. observing a patient take a drug, monitoring blood levels). In lieu of actual exposure data, exposure ascertainment is typically based on medication dispensing records. These records can be ascertained from electronic health records, pharmacies, pharmacy benefit managers (PBMs), and other available healthcare data repositories. Some of the most comprehensive drug exposure data are available among Northern European countries and large integrated health systems such as Kaiser Permanente in the United States. Some strengths of using dispensing records to gather exposure data are:

Easy to ascertain and relatively inexpensive
No primary data collection
Often available for large sample sizes
Can be population based
No recall or interviewer bias
Linkable to other types of data such as diagnostic codes and labs

Limitations of dispensing records as a data source include:

Completeness can be an issue
Usually does not capture over-the-counter (OTC) drugs
Dispensing does not guarantee ingestion
Often lacks indication for use
Must make some assumptions to calculate dose and duration of use

Some studies collect drug exposure data using self-report methods (e.g. interviews or surveys). These methods are useful when the drug of interest is OTC and thus not captured by dispensing records. However, self-reported data is subject to recall bias and requires additional considerations when interpreting results. Alternatively, some large epidemiologic studies require patients to bring in all their medications when they do their study interviews (eg. bring your brown bag of medications). This can provide a more reliable method of collecting medication information than self-report.

It is also important to consider the risk of misclassification of exposure. When interpreting results, remember that differential misclassification (different for those with and without disease) can result in either an inflated measure of association, or a measure of association that is closer to the null. In contrast, non-differential misclassification (unrelated to the occurrence or presence of disease) shifts the measure of association closer to the null. For further guidance on defining drug exposure, please look at Figure 2.

Figure 2. Checklist: Key considerations for defining drug exposure

Velentgas, Priscilla, et al., eds. Developing a protocol for observational comparative effectiveness research: a user’s guide. Government Printing Office, 2013.

As alluded to above, pharmacoepidemiology is a field with complex research methods. We hope this article clarifies these three challenging concepts.

[i](Pinar Balcik, Gulcan Kahraman “Pharmacoepidemiology.” IOSR Journal of Pharmacy (e)-ISSN: 2250-3013, (p)-ISSN: 2319-4219 Volume 6, Issue 2 (February 2016), PP. 57-62)