Should commercial diagnostic testing be stimulated or discouraged? Analyzing willingness-to-pay and market externalities using three case studies from the Netherlands

doi:10.21203/rs.3.rs-1833777/v1

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Research Article

Should commercial diagnostic testing be stimulated or discouraged? Analyzing willingness-to-pay and market externalities using three case studies from the Netherlands

https://doi.org/10.21203/rs.3.rs-1833777/v1

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Introduction -

Consumers may purchase commercial diagnostic tests (CDT) without prior doctor consultation. This paper analyzes three CDT markets - commercial cholesterol tests (CCT), direct-to-consumer genetic health tests (DGT) and total body scans (TBS) - in the context of the universal, collectively financed healthcare system of the Netherlands.

Methods -

An online willingness-to-pay (WTP)-questionnaire was sent to a representative sample of 1500 Dutch consumers. Using contingent valuation (CV) methodology, an array of bids for three self-tests were presented to the respondents. The results were extrapolated to the Dutch population and compared to current prices and follow-up medical utilization, allowing analysis from a societal perspective.

Results -

Overall, 880 of 1500 respondents completed the questionnaire (response rate 59%). Of the respondents, 26%-44% was willing to pay a positive amount for the CDT. WTP was correlated to gender and household income, but not to health status or prior experience with these tests. At mean current prices of €29 for CCT, €229 for DGT and €1650 for TBS, 3.3%, 2.5%, and 1.1%, was willing to purchase a CCT, DGT, and TBS, respectively. All three CDT resulted in net costs to the health system, estimated at €5, €16, and €44 per test, respectively. Reducing volumes by 90,000 CCTs (19%), 19,000 DGTs (5%) and 4,000 TBSs (2.5%) in 2019 would optimize welfare.

Conclusion -

Most respondents were unwilling to consume CDT at any price or only if the CDT were provided for free. However, for a small group of consumers, societal cost exceed private benefits. Therefore, CDT regulation could provide small welfare gains.

willingness-to-pay

self-test

cholesterol test

direct-to-consumer genetic test

total body scan

contingent valuation

An increasing offer of commercial diagnostic tests (CDT) is available for consumers worldwide. CDT allow consumers to obtain insight into their health status without prior visit to a doctor or medical specialist. Examples include over-the counter diagnostic tests such as COVID-19-antigen kits, cholesterol tests and allergy tests, but also wearables such as heart monitors and sleep monitors, online orderable commercial tests, such as testing kits for sexually transmittable diseases, health questionnaires and direct-to-consumer genetic tests, and on-site commercial tests, such as COVID-19-PCR tests, fitness tests and total body scans (TBS) (1). While CDT are - by definition - private markets, they may affect the public health system through follow-up costs, for example doctors’ consultations and treatments following positive CDT results (2, 3). Proponents of CDT advocate benefits of early diagnosis, patient awareness, empowerment, and lifestyle changes, and, as a result, improved health and lower healthcare costs downstream (2, 4). Opponents warn for unnecessary medicalization, low-value care and redundant follow-up healthcare costs (2, 3).

CDT are particularly predominant in the US, where high out-of-pocket spending reduces the public burden of follow-up costs. Contrary, in countries with a broad collective healthcare system additional follow-up cost-effects are predominantly shared collectively (4). This may exacerbate externalities in the market for self-tests, and consequently increase potential for market failure. For example, if proponents are right, CDT may lead to early diagnosis, lowering future treatment costs for advanced illnesses, or may promote a healthy lifestyle, which also may lower future health costs. As these are shared collectively, other people benefit from the use of CDT in the form of lower health taxes and premiums, which are not taken into account in the decision-making process of the potential consumer. In that case, encouraging CDT consumption would enhance total welfare. Contrary, if opponents are correct, CDT may spur unnecessary follow-up tests, consultations, and treatments, boosting health costs for others through increased health taxes and premiums. As potential consumers are unlikely to take into account that CDT consumption may also incur costs to others, CDT may be overconsumed from the perspective of society. However, methodology and frameworks to assess the effects of CDT in the context of a universal health system and inform policy makers have been lacking. In other words, at the margin the costs to society outweigh the private benefits of consumption. These so-called externalities may be a reason for policymakers to intervene into the market (5). We build upon the theory of externalities to assess the market for CDT and inform policy makers.

Economist Arthur Pigou studied the effects of positive or negative externalities on market outcomes, as shown in Fig. 1. Here, a downward sloping demand curve (green) represents the willingness-to-pay (WTP) for a CDT as a function of the expected benefits, including follow-up treatment effects. The horizontal supply curves show the cost of production, where the middle line (blue) represents the private costs: the price of CDT plus the expected value of copayment in case of follow-up payments. Externalities cause social costs of production to deviate from private costs, either in the form of negative externalities (upper orange line) or positive externalities (lower orange line). If consumption of CDT causes additional costs to the health system borne by taxpayers, social costs are higher than private costs, leading to overconsumption (Q*minus Q_opt−). However, if consumption of CDT leads to cost savings to the health system, which accrue to taxpayers, social costs are lower than private costs, leading to underconsumption (Q_opt+ minus Q*). Theoretically, Pigouvian taxes (t_opt) or subsidies (s_opt) equivalent to the size of the externality correct market outcomes (5).

WTP may be modeled as a function of perceived personal net benefits of consumption. Economic theory suggests that upon the consumption decision, consumers weigh perceived costs and benefits of consumption, which includes health-related benefits but also less tangible private benefits and costs. Health benefits may follow from early diagnosis and treatment as well as lifestyle changes resulting from diagnosis of (risk of) diseases. Consumers may also include potential negative health benefits, e.g. risk of overtreatment, in their consumption decision. Less tangible private benefits may include increased certainty, ease of mind, or feeling in control of one’s own health, amongst others. These may also be negative, like additional stress or uncertainty following positive test results, especially in the case of false-positives (6, 7). These benefits -positive or negative- are incorporated into the willingness to pay (WTP) for CDT. WTP may be influenced by income, health state, previous experience with tests, family history, and other individual characteristics (8).

WTP therefore is related to traditional cost-effectiveness measures, such as cost-per-QALY, in the sense that marginal effectiveness of CDT may be incorporated as part of the perceived benefits. However, since additional private consumer values apply to the WTP but are excluded in cost-effectiveness values, WTP for a CDT may be higher than the WTP for any QALY-gains due to CDT consumption. Furthermore, cost-effectiveness measures generally are used to evaluate whether a treatment should be reimbursed collectively, implying that the marginal cost-effectiveness value of a representative person can be used as threshold. However, this does not apply to private markets, where consumption may be valuable for some but not all persons, e.g. those who value health benefits relatively highly, or those who value CDT intrinsically highly (9). Therefore, CDT with unfavorable incremental cost-effectiveness ratios may still have net positive consumption value in the private market. However, in this case externalities should be taken into account (10).

This paper studies CDT markets in the context of the Netherlands, having a universal health system covering over 17 million inhabitants. The Netherlands has a broad benefits package (including general physician (GP) care, hospital care, medication), financed collectively through a mix of insurer premiums, employer contributions and general taxes. Cost sharing constitutes a mandatory deductible of €385 and a voluntary supplementary deductible of €100-€500, while additional cost sharing is limited to long-term care and specialty (brand) medications. The Netherlands is characterized by a GP gatekeeping system, with patients needing a GP-referral to consult inpatient medical specialists. GP visits are free of charge and exempted from the deductible (11, 12). The Dutch government offers a number of free population-wide screening programs, such as breast cancer screening, HPV screening, newborn screening and colorectal cancer screening (13). Nevertheless, the market for consumer CDT is small but growing in the Netherlands (1).

The aim of the paper is to explore externalities in three CDT markets. The aim results in three research questions: 1. Are externalities present in the market for CDT, and what is the direction and size of these externalities in the Netherlands? 2. What is the willingness-to-pay for CDT in the Netherlands? 3. Given current and future prices of CDT, what would be the optimal level of consumption from a societal perspective? The results may inform policymakers on whether regulation of these private markets may improve welfare.

2.1. Literature search

We performed a rapid review to map existing evidence regarding follow-up healthcare use for CDTs. We targeted cost-effectiveness studies that evaluate CDTs. We included a broad selection of CDTs based on existing overviews. We developed a search strategy for searches in Pubmed, Google and Google Scholar, consisting of combination of keywords, thesaurus terms and synonyms of CDT and cost-effectiveness. We consulted experts for key articles and used forward and backward snowballing methodology to find relevant and recent articles. We performed our searches between May and July 2021.

2.2. Case study selection

Based on existing overviews of CDTs and evidence on follow-up costs, three CDT were selected for this study based on representativity and recognizability: in increasing order of invasiveness and price: over-the counter commercial cholesterol tests (CCT), direct-to-consumer genetic health tests (DGT) as example of a commercial online orderable test, and Total Body Scans (TBS) as representative of on-site commercial diagnostic test.

Commercial cholesterol tests

Commercial cholesterol tests (CCT) are sold over the counter in Dutch drug stores for prices between €15-€25. Many types of cholesterol tests are available that differ on accuracy, types of cholesterol molecules and additional information provided, such as lifestyle advices. Testing includes running a blood sample through a test kit and manually or electronically reading the results (14, 15).

Commercial genetic health tests

Numerous direct-to-consumer genetic health tests (DGT) are available in the Netherlands, generally marketed by foreign-based providers. The majority of genetic tests focus on ancestry or relatedness, while few DNA-tests provide information on disease-associated genes. US regulation has only recently allowed commercial testing of specific disease-related genes, which also influences availability in the Netherlands (2). Testable diseases include among others cancers of the lung, breast, ovary, prostate, stomach, intestine, liver and esophagus, as well as a number of chronic conditions such as circulatory diseases, Type 2 Diabetes, Alzheimer’s disease, Parkinson’s disease, rheumatoid arthritis, cerebrovascular diseases and osteoporosis. Specific allele or nucleotide occurrences within certain genes may increase the likelihood of future disease development, although lifestyle factors also have major influence. DNA tests require posting saliva samples to a laboratory abroad for examination, costing between €50-€500, depending on modalities. Results are generally presented as relative chances of acquiring specific diseases given allele occurrence. Occasionally, additional health advice is provided, including lifestyle recommendations or consultation with an expert.

Total body scans

Total Body Scans may include a full physical examination, ECG, MRI and CT-scans, blood, urine and stool testing and consultations with medical specialists, targeted at early diagnosis and second opinions on undiagnosed complaints. TBS take place at commercial clinics. As Dutch law prohibits use of radiation imaging techniques for commercial tests, imaging generally take place in clinics in neighboring countries Germany and Belgium(16). TBS are marketed for around €500-€2000, depending on included features.

For each of these tests, we collected information on providers, characteristics, and prices targeting the Dutch healthcare market. Based on this information, we constructed case descriptions and set base bidding levels for our questionnaire.

2.3. Questionnaire development

A structured questionnaire was developed to measure consumers’ WTP of three case studies. To this aim, a contingent valuation (CV) methodology was applied (17–19). CV uses consecutive bids to assess WTP. Respondents were given a starting bid in euros and were asked whether they were willing to purchase the CDT priced at this amount. If yes, the bid was doubled until respondents were unwilling to purchase the CDT for the given bid, or until the maximum amount set at eight times the market price was reached. These bids were followed by the question whether respondents were willing to consume the CDT, and if so, at what maximum euro amount (20). CV, and questionnaires in general, may lead to biased results if respondents’ WTP is influenced by the starting bid (starting point bias), or if respondents tend to settle on the middle answer (range bias) (10, 21, 22). To correct for range bias, an alternative bidding array starting at lower bids was developed. To correct for starting point bias, an alternative downward bidding array was developed, which started at the maximum amount of eight times the market price, halving until the respondent accepts the bid or when a price of zero is reached. To correct for respondent fixed effects, each respondent received each different type of CV array in random order. Three versions of the questionnaire with differently ordered CV arrays were randomly distributed among respondents. The questionnaire and bidding arrays per CDT are given in appendix 1. After the bidding, consumers were asked about their thoughts and motivations through open text fields.

Besides the bidding questions, respondents were asked about prior experience with the CDT and motivation to consume the CDT, as well as background characteristics such as household income, health status, and health use. The online survey consisted of 23 questions (see appendix 1), with a median completion time of 6 minutes.

The online questionnaire was sent to a sample of 1,500 panel members Dutch Health Care Consumer Panel of Nivel (Netherlands Institute for Health Services Research). The sample was representative regarding gender and age for the Dutch adult population. The Consumer Panel aims to measure opinions on, and knowledge of, health care as well as the expectations of, and experiences with, health care among a cross-section of the Dutch population (23, 24). The panel is a so-called access panel consisting of a large number of people who have agreed to answer questions on a regular basis. Many background characteristics of the panel members are known such as their age, gender and highest level of education completed. At the time of this study (August 2021), the Consumer Panel consisted of approximately 11,000 people aged 18 years and older. On average, an individual panel member receives a questionnaire about three to four times a year. There is no possibility of people signing up for the panel on their own initiative. The Dutch Health Care Consumer Panel is renewed on a regular basis.

Data were pseudonymized and processed according to the Consumer Panel privacy policy, which complies with the General Data Protection Regulation. According to Dutch legislation, neither obtaining informed consent nor approval by a medical ethics committee is obligatory for carrying out research in the panel (25). Participation in the panel is voluntary. Panel members are not forced to participate in surveys, and they can stop their membership of the panel at any time without mention any reason for stopping.

2.4. Response analysis

Responses were analyzed using a two-stage weighted multinomial logistic regression analysis and weighted multivariate OLS regression analysis. First, using multinomial logistic regression analysis the odds of belonging to the group of respondents having a positive WTP (WTP > 0), a WTP of zero (WTP = 0) or being unwilling to consume at any price (WTP=.) were estimated, correcting for response biases and using population weights. In the second stage, OLS was used to fit a demand curve among the fraction with positive WTP, using logarithmic fit and population weights. Population weights were calculated based on age and gender. Next, explanatory variables were added to the two-stage model. Regression formulae for the two-stage model are given below:

$$\text{ln}\left(\frac{{\pi }_{k}}{{\pi }_{j}}\right)={\alpha }_{j}+{\beta }_{j}{X} \left[1\right]$$

$${\text{l}\text{n}(WTP}_{i}^{j})={\alpha }_{i}+{\beta }_{i}{X}+\epsilon [2]$$

In [1], a multinomial logistic model is fitted for three categories k: WTP being positive, zero or nonexistent, where the group with positive WTP is considered the baseline group j. ${\pi }_{k}$ is the odds of belonging to group k, given the characteristics of respondent i, which is modulated by a vector X of explanatory variables. In [2], the -strictly positive- WTP in group j is log-transformed and regressed against explanatory variables using weighted OLS, where population weights are used. In the naïve model, X contains only response dummies; LOW, which is one if the respondent received a low bidding series, and UP, which is one if the respondent received a bidding series moving from high to low values. For WTP, the stated preference was used. In theory, stated preferences should be between marginal bidding outcomes, although discrepancies may occur in practice (9). If the stated preference was inconsistent with the bidding outcome (e.g. when the stated preference was below the bidding outcome or more than double the bidding outcome), the bidding outcome was given precedence.

In the explanatory model, the following variables were added: income, education, gender, ethnicity, previous consumer experience, health status and whether the deductible applied. Income was measured by 15 groups, ranging from gross monthly household income of under €300 to over €6000. Education contains the categories lower education (none, primary school or pre-vocational education), intermediate education (secondary or vocational education), and higher education (professional higher education or university) (26). Ethnicity is 1 if non-Dutch. Health status has previously been related to CDT-use in the Netherlands (1). Health status is measured in the questionnaire as self-reported health on a 5-point Likert scale and as presence of one or more of 40 possible morbidities (count variable). Furthermore, respondents were asked if they completed the mandatory deductible or were expecting to complete the mandatory deductible, resulting in a variable which is one if respondents expected to experience the effects of the deductible in any potential follow-up costs.

As no consensus exist over which bidding procedures render the optimal outcome, WTP was corrected using the mean of the effect of different bidding procedures:

$${cWTP}_{i}^{j}={\widehat{WTP}}_{i}^{j}*(1+\frac{1}{2}{\beta }_{i}) for {X}=0$$

$${\widehat{WTP}}_{i}^{j}*{(1-\frac{1}{2}\beta }_{i}) for {X}=1$$

Here, the ‘true’ WTP is assumed to lie exactly in between the estimated effects of response biases. For example, WTP responses using an upward bidding CV array are assumed to be biased downwards, while WTP responses using downward bidding CV arrays are assumed to be biased upwards. The former estimates are corrected upwards with half the percentual effect ${\beta }_{i}$, while the latter are corrected downwards with half the percentual effect. The demand curve was assumed to have a functional logarithmic form. Using ordinary least squares estimation, a demand curve was fitted to the corrected WTP:

$${\text{l}\text{n}(cWTP}_{i}^{j})={\alpha }_{i}+{\beta }_{i}\text{ln}\left(n\right)+\epsilon [3]$$

Where n is the order of respondents, ordered from low to high WTP. The outcomes of [3] were used to derive the demand equation, normalized to the interval between 0 and 1.

2.5. Open response thematical analysis

Respondents were given the opportunity to comment on their choice of WTP for each of the three CDT. All available responses were analyzed through thematic coding to inform motivations behind WTP-choices. Two researchers coded the free texts using deductive coding in MaxQDA. Through iterative rounds, codes were compared, synchronized and grouped into common themes. Codes were analyzed quantitatively to infer differences in respondents’ motivations per CDT.

2.6. Estimating externalities

For each case, estimates of follow-up utilization and potential cost savings were extracted from the literature. These data were applied to a hypothetical cohort of 10,000 consumers to calculate follow-up utilization effects per consumer. Using Dutch tariffs, total costs were calculated. By using information on cost sharing, total follow-up costs were subdivided into private costs and public costs. The public costs per consumer were used as externality in the demand function (3) together with current CDT prices to estimate actual and optimal CDT utilization.

3.1. Descriptive statistics

In total, 880 respondents filled in the questionnaire (59%), of which 32 people (4%) partly filled in the questionnaire. Descriptive statistics are given in Table 1. Using age and gender weights, responses were weighed to reflect percentages of the full Dutch population of 17 million inhabitants.

Table 1

Descriptive statistics
Variable		N (%)	Dutch population
Gender	Female	457 (52%)	51%
Education	Low	72 (8%)	8%
	Intermediate	378 (44%)	42%
	High	416 (48%)	50%
Ethnicity	Non-Dutch	82 (9%)	9%
Mean monthly household income (sd)		€3250 (€1550)	€ 3042
Health state	Excellent	49 (5.9%*)
	Very good	242 (29.1%*)
	Good	384 (46.3%*)
	Mediocre	142 (17.1%*)
	Bad	14 (1.6%*)
Deductible left		474 (54%*)
Consumed CCT in the last year		13 (1.5%*)
Consumed DGT in the last year		4 (0.5%*)
Consumed TBS in the last year		8 (1.0%*)
* percentages are weighted to be representative for the Dutch population

3.2. Willingness-to-pay for self-tests

Of 880 respondents, about a third is unwilling to consume CCT even at a price of zero. This is larger for DGT (50%) and TBS (40%). Furthermore, 22% of respondents is only willing to consume a cholesterol self-test at a price of zero. This is 22% for DGT and 20% for TBS. The remainder has a positive WTP (CCT:44%; DGT:26%; TBS:37%). Figures 2a-c show sample WTP for three cases, after correcting for discrepancies between CV and stated preferences, displaying a logistic functional form.

Table 2 shows the results of the reduced-form two-stage model. Columns 1 and 2 show the first stage MLR estimates, i.e. the odds of being in the group of persons unwilling to consume a CDT (WTP=.) or in the group of persons willing to consume but only if the CDT is free (WTP = 0) vis-à-vis the group of persons with positive WTP (WTP > 0, baseline group). Column 3 shows the estimates of the second stage OLS regression relating the height of the WTP to independent variables in the subgroup WTP > 0. The bidding procedure has a significant influence on WTP. For example, respondents receiving a downwards bidding procedure have a 50% increased chance of having a positive WTP. Furthermore, given a positive WTP, a downwards bidding procedure roughly doubles the WTP. In the case of TBS, low bidding values increase the chance of having a positive WTP by about 30%, but reduce the WTP value by 38%, given having a positive WTP. After correcting for these biases, demand equations were estimated using a log-log fit (appendix 2). These demand equations should be interpreted as the percentage of the population (x) that is willing to consume a CDT at price p. The equations have x bounded by the percentage with a positive WTP and price between < 0,∞>.

$${p}^{CCT}=\frac{{e}^{0.60}}{{x}^{0.81}} for x=\left[\text{0,0.44}\right] \left[4\right]$$

$${p}^{DGT}=\frac{{e}^{0.99}}{{x}^{1.21}} for x=\left[\text{0,0.26}\right] \left[5\right]$$

$${p}^{TBS}=\frac{{e}^{2.78}}{{x}^{1.03}} for x=\left[\text{0,0.37}\right] \left[6\right]$$

Table 2

reduced form two-stage regressions
	CCT			DGT			TBS
	Multinominal logistic regression (MLR)		Second stage OLS (WTP > 0)	Multinominal logistic regression (MLR)		Second stage OLS (WTP > 0)	Multinominal logistic regression (MLR)		Second stage OLS (WTP > 0)
	1a. (WTP=.)	2a. (WTP = 0)	3a.	1b. (WTP=.)	2b. (WTP = 0)	3b.	1c. (WTP=.)	2c. (WTP = 0)	3c.
Low-bid	0.027 (0.191)	-0.329 (0.215)	-0.164 (0.105)	0.146 (0.204)	-0.050 (0.238)	-0.697*** (0.16)	-0.380* (0.189)	-0.268 (0.219)	-0.382** (0.14)
Downwards-bid	-0.445* (0.194)	-0.577** (0.213)	1.032*** (0.101)	-0.524** (0.196)	-0.681** (0.243)	0.933*** (0.149)	-0.222 (0.184)	-0.650** (0.233)	0.531*** (0.127)
Constant	-0.184 (0.139)	-0.409** (0.148)	1.591*** (0.076)	0.688*** (0.142)	0.039 (0.163)	3.593*** (0.11)	0.348* (0.135)	-0.276 (0.158)	4.788*** (0.104)
N	880		397	880		243	880		347
(Pseudo) R²	0.0068		0.3112	0.0078		0.1391	0.0109		0.047
Note: Three groups are used in the MLR: WTP=. (unwilling to consume); WTP = 0 (willing to consume, but only when free) and WTP > 0 (Willing to pay a positive amount). WTP > 0 is the baseline group in the multinomial logistic regression. 5% significance 1% significance **0.1% significance

Table 3

extended two-stage regressions
	CCT			DGT			TBS
	Multinominal logistic regression (MLR)		Second stage OLS (WTP > 0)	Multinominal logistic regression (MLR)		Second stage OLS (WTP > 0)	Multinominal logistic regression (MLR)		Second stage OLS (WTP > 0)
	1a. (WTP=.)	2a. (WTP = 0)	3a.	1b. (WTP=.)	2b. (WTP = 0)	3b.	1c. (WTP=.)	2c. (WTP = 0)	3c.
Low bid	-0.311 (0.252)	0.017 (0.225)	-0.120 (0.116)	-0.162 (0.267)	0.040 (0.236)	-0.552** (0.17)	-0.240 (0.253)	-0.415 (0.23)	-0.321* (0.150)
Upwards bid	-0.542* (0.248)	-0.511* (0.229)	1.04*** (0.11)	-0.746** (0.276)	-0.561* (0.228)	0.845*** (0.154)	-0.861** (0.277)	-0.360 (0.229)	0.543*** (0.133)
Age	-0.522* (0.211)	0.031 (0.188)	-0.050 (0.093)	-0.421 (0.226)	0.144 (0.192)	0.052 (0.131)	-0.300 (0.224)	0.42* (0.192)	0.262* (0.118)
Gender	0.007 (0.006)	0.040*** (0.006)	0.004 (0.003)	0.021** (0.007)	0.047*** (0.006)	-0.002 (0.004)	0.014* (0.007)	0.061*** (0.006)	0.003 (0.004)
Education	-0.268 (0.179)	0.086 (0.166)	0.052 (0.080)	-0.023 (0.193)	0.469** (0.172)	0.118 (0.110)	-0.422* (0.189)	0.232 (0.172)	0.008 (0.108)
Income	-0.073 (0.077)	0.048 (0.067)	0.086** (0.033)	-0.101 (0.082)	0.053 (0.068)	0.099* (0.046)	-0.103 (0.081)	0.018 (0.068)	0.059 (0.041)
Health state	0.009 (0.133)	0.069 (0.122)	0.025 (0.065)	-0.099 (0.145)	-0.082 (0.125)	0.033 (0.082)	-0.024 (0.142)	0.143 (0.125)	0.080 (0.074)
Full deductible	0.935*** (0.237)	0.294 (0.211)	-0.075 (0.103)	0.120 (0.252)	-0.034 (0.216)	-0.146 (0.151)	0.261 (0.248)	-0.108 (0.215)	-0.136 (0.133)
Prior experience	-0.060 (0.695)	-1.011 (0.861)	0.095 (0.305)	-1.157 (1.243)	-2.138 (1.302)	0.723 (0.552)	-0.100 (0.979)	-0.790 (1.008)	0.931 (0.559)
Constant	-0.243 (0.766)	-3.098*** (0.714)	0.962** (0.362)	-0.124 (0.838)	-2.854*** (0.74)	2.882*** (0.509)	0.313 (0.830)	-4.221*** (0.749)	4.065*** (0.459)
N	699		334	699		211	699		309
(Pseudo) R2	0.0714		0.3154	0.0749		0.1711	0.1196		0.0678
Note: WTP > 0 is the baseline group in the multinomial logistic regression. 5% significance 1% significance **0.1% significance

3.3. Explanatory analyses

For our explanatory analyses, WTP is correlated to explanatory variables by estimating expanded equations [1] and [2]. 159 respondents were unwilling to disclose household income and were excluded from the analysis. Another 12 respondents were excluded due to missing data on health status. Results indicate that females are less likely to have a positive WTP. Furthermore, the height of the WTP is correlated to income, especially in the case of CCT (Table 3).

Of 880 respondents, 466 (53%) commented on their choice in the free comment section of at least one CDT. These motivations were coded and analyzes per CDT, rendering 53 codes (appendix 3). Codes were categorized hierarchically into common themes, as displayed in Fig. 3. Codes were labeled as positive attitude towards CDT, negative attitude towards CTD and doubt. For positive attitudes, second level hierarchical distinction were made between medical benefits (prevention, early detection, health improvements) and non-medical benefits (interest, comfort, trust, experience). For negative attitudes, three second level hierarchies were distinguished: irrelevance, conditionality and objectional. Irrelevance relate to the CDT being not relevant for the responder at the moment (e.g. due to good health, medical supervision, or old age). Conditionality refers to respondent commenting CDT consumption depends on circumstances and context (e.g. under supervision of a (family) doctor, distributed at low cost/free, or only if specific complaints arise). Objectional includes respondents having objections regarding CDT (e.g. relating to reliability/trust or fear of procedure/ outcomes). The distribution of comments over these themes differs per test. A larger percentage of respondents reported positive attitudes towards TBS (appendix 3), with potential health gains and general interest in health status as frequently mentioned motivations. For CCT, respondents more often found tests irrelevant for their situation, e.g. being in good health or already being under medical treatment for related complaints. Furthermore, a large proportion of responses suggested these tests to be performed by a GP or medical specialist. Besides doubting usefulness of CCT, a large proportion of respondents suggested that insurers should reimburse these tests. For DGT, respondents more often reported objections to the test, such as unwillingness to know the outcome, doubts on usefulness and fears of elevated uncertainty.

3.4. Market externalities

Commercial cholesterol tests

While cardiovascular prevention programs have been recommended for high-risk patients, cholesterol screening under the non-risk population has not been found cost-effective under Dutch guidelines. For example, cholesterol screening in not-at-risk US-populations renders cost-effectiveness of $25,198 to $50,871 per quality-adjusted life year (QALY), while at-risk population displays significantly more beneficial cost effectiveness ratios (27). Estimation of follow-up health utilization related to self-test use is challenging, as most studies focus on patients at risk or with a clinical indication, which may not be representative consumers of CCT. Therefore, we focus on descriptive studies that include willing consumers or subjects representative of the population. A US-based study on cholesterol screening found a positive test rate (LDL of > 130) of between 29.8% and 30.7% (28). The authors found that for every euro spend on preventative treatment, 36 cents were saved on treatment costs of myocardial infarction, strokes and other cardiovascular diseases. A similar recoup of 24 cents per dollar spend on preventative treatment was reported by Choudry et al., 2011. A 1996 study providing free cholesterol tests at a pharmacy for interested consumers found elevated cholesterol levels in 42 of 106 consumers (40%) (29). Of these 42, 20 (48%) received lipid-lowering drugs prescriptions. Applying these findings to the Dutch situation, we assume:

additional GP checkups twice a year for patients with high cholesterol costing €9.97 per consultation (2019 prices) for 40% of consumers
use of lipid-lowering drugs for 19% of consumers, costing €14.71 annually (2019 prices)
savings on secondary treatment of 24 cents for every euro spend on primary CVD-prevention in the consumer group, accruing €1.93 per consumer

Using these figures, CCTs lead to an estimated follow-up cost of €6.12 per consumer (appendix 4). Given that 46% of the respondents indicate to have depleted the deductible, and that GP consultations are exempt from the deductible, an estimated €0.76 of €6.12 is paid for out-of-pocket (12%). The net externality is estimated at €5.36 per consumer, given a 1-year time horizon (€4.50-€7.12 under different assumptions, data not shown).

Direct-to-consumers genetic health tests

Little information is available on follow-up costs of DGT. A 2010 study inferred follow-up costs of DGT genetic tests by surveying health professionals, finding 22 cases with follow-up costs between $40 and $20,600 (30). However, the percentage of consumers that seek referral remains unclear. Follow-up costs may include further genetic testing, mammography, CT/MRI chest scan, prostate-specific antigen (PSA) testing and CA-125 test (30). Furthermore, numbers are relatively low. A 2016 study reports that 10.7% of consumers shared their results with a medical professional (31). However, it is unclear whether this occurred during regular visits, or whether new visits were initiated by consumers. Furthermore, which medical professional was contacted was not disclosed. A 2019 survey from Australia reports that 55 of 205 users of DGT report seeing a health professional (27%). This resulted in medical treatment in about a third of the cases (32). Following pharmacogenomic self-tests, 5.6% of consumers reported changes in prescription medication, generally following doctors’ consultation (33). A 2019 survey of US doctors found that 40% of DGT consumers received additional referrals, of which 78% to a clinical geneticist (34).

Using this limited data, we conservatively assume that 10.7% of the consumers visits a GP as result of DGT consumption (31), resulting in additional referrals in 40% of GP visits (34). Referrals include further genetic testing (78%), and additional tests (22%) (30, 34). Any treatments following potential positive results of these additional tests are not taken into account. Using these parameters, externalities are estimated at €16 per consumer (appendix 4).

Total body scans

For TBS, a high percentage of positive test results may be expected, most of which will be false positives. For example, a study from 2005 estimates a positive rate of whole-body CT-scanning of 93%, comprising of 91 percent point false-positives and 2 percent point true positives (35). Follow-up costs of additional screening and biopsies totaled $5 per patient - or $296 per true positive patient -, rendering cost-effectiveness of $151,000 per QALY. This would not be considered cost-effective under current Dutch reference values of €20,000 per QALY for screening interventions.

A 2020 study on TBS in the Netherlands and Germany found that 13% of consumers experienced positive test results, of which 9 percent point followed-up these results with a GP or medical specialist consultation (36). Applying 2019 Dutch tariffs on follow-up utilization of this study (see appendix 4) renders an estimated €55 in collective medical follow-up costs after TBS if the deductible had been depleted and €34 if the deductible still applied. Assuming 46% of the respondents had depleted the deductible, the net externality is estimated at €44 per TBS consumed.

3.5. Solving for the social optimum

Mean prices offered by the dominant provider(s) are €29 for CCT¹(37), €229 for DGT(38), and €1650 for TBS(39, 40) (2022 prices). Plotting these prices into the demand functions (4–6) renders estimated fractions of 3.3%, 2.5% and 1.1%, respectively. With an 18 + population of 13.95 million in 2019(41), this would translate to quantities of 460.000 CCTs, 350.000 DGTs and 160.000 TBSs. At the margin, the societal costs of the test outweigh the WTP for a number of consumers. Setting the WTP equal to the societal costs instead of the private costs would give the socially optimal quantities of 380.000 CCTs, 340.000 DGTs and 150.000 TBSs (Table 4). Price elasticities of a 10% price reduction are 13.9% for CCT, 9.1% for DGT and 10.8% for TBS. For example, a 10% reduction in price of TBS would increase consumption by .1 percent point, overuse by 892 TBS and burden the health system with an additional €0.9 million.

Table 4

solving for the social optimum for the Netherlands
	CCT	DGT	TBS
Mean price	€29	€229	€1,650
Externality per unit consumed	€5.36	€15.87	€43.67
Demand equation	${p}^{CCT}=\frac{{e}^{0.60}}{{x}^{0.81}}$	${p}^{DGT}=\frac{{e}^{0.99}}{{x}^{1.21}}$	${p}^{TBS}=\frac{{e}^{2.78}}{{x}^{1.03}}$
Private Q	459,975 (3.30%)	354,469 (2.54%)	156,832 (1.12%)
Total externality costs	€ 2,812,811	€ 8,238,645	€ 8,580,672
Socially optimal Q	373,185 (2.68%)	335,376 (2.40%)	152,906 (1.10%)
Difference	-86,790 (-18.9%)	-19,093 (-5.4%)	-3,926 (-2.5%)

¹ excluding shipping costs

This paper applies a novel perspective to the market for CDT by analyzing these markets according to the economic theory of externalities. This allows analysis from a societal perspective while providing guidance to policymakers in regulation of CDT. The results suggests that in the Netherlands, CDT tend to burden the health system, leading to overconsumption from a societal perspective. However, the negative externalities are limited, requiring volume reductions of 3%-19% to reach the social optimum, depending on the test. As a small percentage of consumers reports high consumption value of CDT, fully banning CDT is likely to harm social welfare. A lumpsum -Pigouvian- taxation of CDT equal to the size of the externality would optimize social welfare, irrespective of current or future prices. Other forms of discouragement may be easier for the government to enact. For example, TBS-regulation currently pushes travel times as patients need to visit clinics abroad, thereby increasing private costs in a matter similar to a Pigouvian tax.

While our approach offers novel insights into evaluation of private markets for CDT, some reservations apply. WTP measurements were only applied to -specific variants of- three CDT. For other variants of these CDT, or for other CDT, other WTP-valuations may apply. Furthermore, consumers may overstate their willingness to pay for CDT, in relation to actual consumption volumes. In our sample, actual consumption was estimated at 0.5–1.5%, while estimated consumption based on WTP was 1.1–3.3% at actual prices, or about double the actual reported consumption. A study using the same panel setup estimated total CDT use (including other available CDTs) in the Netherlands at 5% in 2016 (42). Although these authors do not differentiate between CDTs, their results could signal some underreporting in our sample. Regarding the follow-up costs, evidence was of low quality, especially for commercial genetic health tests. Included studies had low sample size and limited time horizons, which risks excluding long-term cost-reducing effects of lifestyle changes and prevention. However, a number of studies show no lifestyle changes following CDT (2, 4, 43).

A randomized controlled trial would be preferred to assess follow-up costs, although this is often infeasible or impractical in relation to CDT. In absence of a control group, we have to assume that no additional health use would be observed in absence of a CDT, which may be a strong assumption. Therefore, the reported effects may be overestimating externalities. For example, in case of CCT we assume additional follow-up utilization during one year, after which the consumer returns to care as usual. For some patients, utilization would also increase in absence of CCT, thereby overestimating the externality, while other patients may continue for years with undiagnosed high cholesterol in absence of a CCT, thereby underestimating follow-up of cardiovascular-risk-related utilization but also potential reductions in costs of complications. Additional research is required to improve estimates of follow-up use. Also, the characteristics of the CDT used to examine follow-up costs may deviate slightly from the CDT-descriptions given in the WTP-questionnaires in terms of number and types of tests performed as part of the CDT. This could reduce the accuracy of the estimates. For some CDT, such as DGT, additional positive externalities may be present; if heritable disease risk is present, this may positively affect family members’ health. This may not be fully reflected in the consumer’s WTP, thereby potentially underestimating net benefits of CDT to society. This may also apply to COVID-19 antigen tests, which may exhibit positive externalities through reductions in virus spread due to quarantines after positive test results. In light of these limitations, our estimates should be seen as a first indication, potentially overestimating the societal costs of CDT.

To measure WTP, contingent valuation methods were applied. Contrary to existing CV literature, we explicitly test and correct for common biases in CV methodology (44). Furthermore, while most literature censors non-responses, our research explicitly takes into account that people may be unwilling to consume a CDT at any price including zero. Additional research could elicit a negative WTP for these persons, e.g. a Willingness-to-accept (WTA) to have the test performed, to express that some persons may experience a negative utility of consumption. However, this may be beyond the interest of policymakers. Furthermore, simultaneous evaluation of three tests could affect valuations (45). While we employ these simultaneous evaluations to correct for response biases, we cannot exclude the possibility that the order of evaluations, starting with CCT and ending with TBS, would affect WTP. Ideally, a larger sample size would allow random ordering of CDT evaluations and joint evaluation of WTPs, e.g. using seemingly unrelated regression techniques (45).

This paper finds that a relatively large proportion of the sample (33–50%) was unwilling to consume a CDT even at a zero price, compared to existing literature. A study on PSA testing among middle-aged men in Japan found that 20% was unwilling to consume a PSA-test at a zero price. This figure was 22% in a US-based study on Alzheimer’s disease prevention (46). The difference could be due to different tests being evaluated, although cultural differences could also play a role. The Netherlands is generally considered reserved with respect to medical consumption (47). In accordance with literature, this paper finds weak evidence of age, gender and income effects on WTP (46, 48, 49). Contrary, no effect of health status or previous experience was found, while some literature finds weak effects. Possibly, the effect of health status was too limited to be found significant given our sample size. It could also be the case that consumers with low health status are already in treatment, and therefore have no need for CDT. This was a common theme in qualitative analysis, especially for CCT.

For policy-makers, the results suggest that when CDT does not apply for collective financing, private consumption may still produce net welfare for society, despite additional follow-up costs to the health system. For private consumption, non-health benefits, such as ease of mind, curiosity, experience, etc. should be taken into account (as well as non-health costs). While these effects are difficult to monetize individually, they are represented in an individual’s WTP (9). Therefore, the framework applied in this paper is more suited than traditional cost-effectiveness analysis to guide policymakers in whether regulation of CDT as a private market would be welfare-enhancing. One potential objection to CDT is that consumers often have difficulties correctly interpreting the outcomes, which could bias perception of value when purchasing a CDT, and thereby bias WTP. Especially in DGT interpretability was a predominant theme in the open-field responses. This could be considered as internalities (50). While internalities may be a concern for policymakers, this is not unique to CDT. Any good or service that potentially exhibit internalities, e.g. gym memberships, casino visits, or alternative medicine, may benefit from additional regulation with respect to consumer education. In this, CDT are no exception, and specific regulation beyond general consumer education regulation in this area may not be necessary. Furthermore, it is unclear whether the WTP increases or declines if consumers would be fully rational. For example, a 2006 study found no significant changes in WTP for PSA testing after consumer education (48). Secondly, an argument could be made that private provision of CDT may increase inequality in health care use, as predominantly high incomes may consume CDT. This may be politically sensitive in a country with a broad solidarity in health use and financing such as the Netherlands. Current regulation dictates that collective access is provided for cost-effective care. In this aspect, CDT may be beneficial for selected -wealthy- individual consumers, but not for a group of (low-income) consumers. To avoid this public-private market paradox, policymakers may wish to limit access to CDT to preserve solidarity. Additional research into societal acceptance of high-income consumption of cost-ineffective care may be informative.

At current prices of three commercial diagnostic tests, it is estimated that 1–3% of the population would purchase these tests. CDT carry follow-up healthcare expenditures that result in overuse from a societal perspective. This is largest for commercial cholesterol tests (18.9%), and smallest for Total Body Scans (2.5%). Due to the relatively low price and elastic demand of CCT, externalities are relatively high. However, the total burden of CCT to the health system is relatively low (€3 million). Furthermore, part of the additional burden to the healthcare system enhances total welfare, as consumption of CDT produces consumer surplus. So while externalities do exist, these are relatively limited and may not require government intervention, especially for DGT and TBS. However, as the costs for these tests may decline in the future, inefficiencies increase and government scrutiny may be required.

CCT Commercial cholesterol tests

CDT Commercial diagnostic tests

CV Contingent valuation

DGT Direct-to-consumers genetic health tests

GP General Physician

PSA Prostate-specific antigen

QALY Quality-adjusted life year

TBS Total Body Scans

WTA Willingness to accept

WTP Willingness to pay

Ethics approval and consent to participate: not applicable
Consent for publication: not applicable
Availability of data and material: The data that support the findings of this study are available from NIVEL but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of NIVEL
Competing interests: none
Funding: none
Authors' contributions: study design: NS, WD, SO; questionnaire development: NS, EV, AB, SO; Analysis: NS, EV; All authors read and approved the final manuscript.
Acknowledgements: We thank Bram Wouterse, Johan Polder and Johan Melse for their constructive comments.

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appendix1questionnaireinDutch.docx
Appendix 1: Questionnaire (in Dutch and translated to English)
appendix1translatedQuestionnaire.docx
Appendix 1: Questionnaire (in Dutch and translated to English)
Appendix2regressionoutput.docx
Appendix 2: Fitting the demand equation: estimation results
Appendix3thematiccodeanalysis.docx
Appendix 3: Thematic coding analysis
Appendix4calculationoffollowupcosts.docx
Appendix 4: Calculation of follow-up costs of CDT.

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Should commercial diagnostic testing be stimulated or discouraged? Analyzing willingness-to-pay and market externalities using three case studies from the Netherlands

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Abstract

Introduction -

Methods -

Results -

Conclusion -

Figures

1. Background

2. Methods

2.1. Literature search

2.2. Case study selection

2.3. Questionnaire development

2.4. Response analysis

2.5. Open response thematical analysis

2.6. Estimating externalities

3. Results

3.1. Descriptive statistics

3.2. Willingness-to-pay for self-tests

3.3. Explanatory analyses

3.4. Market externalities

3.5. Solving for the social optimum

4. Discussion

5. Conclusions

List of abbreviations

Declarations

References

Supplementary Files

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