K T Huynh

I T Castro

A Barros
email: anne.barros@utt.fr

C Bérenguer

On the Construction of Mean Residual Life for Maintenance Decision-Making

Keywords: Competing risks model, Condition-based maintenance, Condition monitoring, Conditional indicator, Mean residual life, Reliability, Stochastic process

This paper provides a methodology to assess the efficiency of mean residual life (MRL) in condition-based maintenance decision-making for a system subjected to the competing and dependent risks of degradation and shock. Based on this system, the cost models of two quite simple policies implementing MRL-based or degradation-based preventive replacement are developed and compared with each other. Analyzing the equivalence and the performance of both policies allow us to give some conclusions on the interest of the MRL indicator.

INTRODUCTION

With the dissemination of condition monitoring (CM) techniques, condition-based maintenance (CBM) is nowadays a promising approach to improve the durability, reliability and maintainability of industrial systems. The majority of CBM models are of the control-limit type where a maintenance decision is made whenever a quantity related to the system state reaches a predetermined threshold (see [START_REF] Wang | A survey of maintenance policies of deteriorating systems[END_REF]). Such a quantity is called conditional indicator. The construction of this kind of indicator is an important task that decides the performance of a CBM strategy, and its quality depend closely on the capacity to characterize precisely the system health state. For a deteriorating system, the degradation level is a traditional conditional indicator for CBM decision-making. Numerous degradation-based maintenance models have been developed and their performance has been proved through practical applications (see e.g., [START_REF] Jardine | A review on machinery diagnostics and prognostics implementing condition-based maintenance[END_REF], [START_REF] Van Noortwijk | A survey of the application of gamma processes in maintenance[END_REF]). However, since the system failure in the reality is not solely due to degradation but also is due to the other causes, the degradation level does not suffice to describe completely the system health state. In this context, it is necessary to construct a more robust indicator for CBM decision. The MRL, that corresponds to the expected time remaining to system failure (see [START_REF] Sikorskaa | Prognostic modelling options for remaining useful life estimation by industry[END_REF], is an other way to reflect the system health state. The advantage is that the definition of MRL is not strictly limited to a failure mechanism (e.g., degradation, shock, etc.), and it may be then a good alternative to characterizing a system with multiple failure modes. However, if we agree that the MRL is very promising for maintenance planning (see e.g., [START_REF] Peng | Current status of machine prognostics in condition-based maintenance: a review[END_REF], [START_REF] Ahmad | An overview of time-based and condition-based maintenance in industrial application[END_REF]), it is surprising that very few CBM strategies based on the MRL are proposed. Hence the following question arises naturally: how to develop such a strategy, and can the introducing of MRL in maintenance decision-making effectively lead to more saving in maintenance cost when degradation level is already available? The present paper aims to answer this question by proposing a MRL-based maintenance policy and investigating its performance by comparing with a classical degradation-based maintenance policy. Both policies are applied for a system which is subjected to the dependent and competing failures due to degradation and shock. Comparisons of the optimal maintenance cost rate for these policies allow us to show the efficiency of MRL indicator and to give the appropriate conditions for its application.

The remainder of this paper is organized as follows. Section 2 deals with the system modeling. In section 3, a condition approach is used to compute the MRL of the system. The structures and the cost models of two CBM policies (i.e. the policy implementing MRL-based preventive decision and the policy implementing degradation-based preventive decision) are introduced in section 4. The sections 5 and 6 are devoted to analyze and discuss the equivalence and the performance of both proposed policies. Finally, we conclude the paper in section 7.

SYSTEM MODELING

The considered system is subjected to the competing failures of degradation and shock. Such a system can be described by a Degradation-Threshold-Shock (DTS) model where the system fails when the degradation process reaches a critical threshold or when a shock occurs (see [START_REF] Lehmann | Joint modeling of degradation and failure time data[END_REF]). As advocated by [START_REF] Singpurwalla | Survival in dynamic environments[END_REF], this kind of model can be seen as a versatile and realistic extension of many classical failure models based either only on degradation or only on parametric lifetime distributions. The "classical" DTS models assume that the degradation process and the shock process are independent (see e.g., [START_REF] Li | An inspection-maintenance model for systems with multiple competing processes[END_REF]Pham (2005), Van Noortwijk et al. (2007)). But, in many practical situations, the dependence between them is of importance and should not be neglected in the modeling. Therefore, the present paper aims to add this dimension to the DTS model by considering that the shock failure rate depends on the degradation level of the system. And we call the proposed model Degradation-Threshold-Dependent Shock (DTDS).

Degradation modeling

We consider a stochastic deteriorating system, for which the degradation level at time t can be summarized by a random aging variable X t . Without any repair or replacement action, {X t } t≥0 is a continuous-time monotonically increasing stochastic process, with X 0 = 0. In this paper, we assume that {X t } t≥0 is a homogeneous Gamma process (see [START_REF] Van Noortwijk | A survey of the application of gamma processes in maintenance[END_REF] for a thorough review on the use of Gamma processes in maintenance modelling). So, for all 0 ≤ s ≤ t, the increment of {X t } t≥0 between s and t, X t -X s , follows a Gamma density function with shape parameter α • (ts) and scale parameter β:

f α•(t-s),β (u) = β α•(t-s) u α•(t-s)-1 e -βu • 1 {u≥0} Γ (α • (t -s)) , (1)
where 1 {•} and Γ (•) denote the indicator function and Gamma function respectively

Γ (y) = ∞ 0
u y-1 e -u du, for y > 0.

(2) Such a process depends on both parameters α and β which allows to model various degradation behaviors from almost deterministic to highly variable. The average degradation rate is m = α β and it variance is σ 2 = α β 2 . The system is considered to be in the failed state due to degradation when X t is greater than a fixed failure threshold L.

To characterize the degradation-based failures, we study two types of hitting time of the deteriorating Gamma process. The first hitting time, denoted by τ A , is the time at which the degradation reaches a level A. As shown in [START_REF] Van Noortwijk | A survey of the application of gamma processes in maintenance[END_REF], its survival function is given by:

FτA (t) = 1 - Γ (αt, Aβ) Γ (αt) , (3)
and its density function is computed as:

f τA (t) = α Γ(αt) ∞ Aβ {log (z) -ψ (αt)} z αt-1 e -z dz, (4) where Γ (α, x) = ∞ x z α-1 e -z dz and ψ (α) = Γ ′ (α)
Γ(α) are respectively the Gamma function and Digamma function.

Considering now another degradation level B > A, τ B -τ A is the second type of hitting time considered here. Its exact survival function has been derived by [START_REF] Bérenguer | Maintenance policy for a continuously monitored deteriorating system[END_REF], but in a complex form. In order to simplify and reduce the numerical computation, we use the approximation proposed by [START_REF] Huynh | A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events[END_REF] as follows:

FτB-τA (t) ≃ Fτ B-A-1 2β (t) , (5)
where the condition of application is 1 < βA < βB -1 2 .

Shock failures modeling

In most practical situations the system failure is not solely due to degradation but also to shocks. We assume in this paper that the system fails when a shock occurs. Moreover, the degradation process and the shock process can depend on each other: the higher the degradation the more the system is vulnerable to shocks (see [START_REF] Huynh | Value of condition monitoring information for maintenance decision-making[END_REF]; [START_REF] Ye | A distribution-based systems reliability model under extreme shocks and natural degradation[END_REF]). Hence if N t is the number of shocks in (0, t], the shock arrival times can be modeled by a non-homogeneous Poisson process {N t } t≥0 with stochastic increasing intensity which depends on both degradation level X t and working time t:

r (t, X t ) = r 1 (t) 1 {Xt<Ms} + r 2 (t) 1 {Xt≥Ms} , (6)
where r 1 (t) and r 2 (t) denote two continuous and nondecreasing failure rates at time t with r 1 (t) ≤ r 2 (t), ∀t ≥ 0. The quantity M s represents a fixed degradation level. The expression 6 means that the system is more prone to shock failures when the degradation increases and exceeds a given level.

From ( 6), the survival function of failure times due to shocks can be computed by:

Fs (t) = F1 (t) FτM (t) + F2 (t) t 0 F1 (u) F2 (u) f τM s (u) du, (7)
where FτM and f τM s are given from ( 3) and ( 4), Fi , i = 1, 2, is the survival function associated with the failure rate r i : 1

Fi (t) = e - t 0 ri(u)du . (8)
T L T M s T s T f r 1 (t) r 2 (t)

CONDITIONAL MEAN RESIDUAL LIFE

Mean residual life is an important reliability indicator for maintenance decision-making since it can provide an idea about how long a system at any particular age and deteriorating is expected to survive. Generally, as shown in [START_REF] Jardine | A review on machinery diagnostics and prognostics implementing condition-based maintenance[END_REF], the MRL function at time t can be computed by:

E [τ f -t | τ f > t, Z 0:t ] , (9)
where τ f is the system lifetime, and Z 0:t is the CM information related to system state obtained at time t. In the literature, the calculation of MRL function is usually free from the effect of CM data (see e.g. [START_REF] Bebbington | Reduction in mean residual life in the presence of a constant competing risk[END_REF], [START_REF] Navarro | Comparisons and bounds for expected lifetimes of reliability systems[END_REF]). However, since this approach can lead to a large variability of MRL, the MRL is then not very useful for maintenance planning (see [START_REF] Banjevic | Remaining useful life in theory and practice[END_REF]). In order to reduce this inaccuracy, as proposed by [START_REF] Wang | A model to predict the residual life of aircraft engines based upon oil analysis data[END_REF], the system CM data must be taken into account when estimating the MRL. In this paper, the system monitoring is assumed perfect and the system evolution is memoryless, the CM data of the system are then limited to its degradation level (i.e., Z 0:t ≡ X t ). Hence from (9), the MRL at time t, given the degradation level X t = z, is calculated by:

m z (t) = E [τ f -t | τ f > t, X t = z] = ∞ t R (u | z) du, (10) where R (u | z)
is the conditional reliability of system at time u > t, given X t = z and that the system is still running at time t.

In the considered DTDS model, the system fails whenever degradation level exceeds a fixed threshold L or a shock occurs, R (u | z) is hence computed by:

R (u | z) = P (X u < L, N u = 0 | X t = z) = R 1 • 1 {z<Ms} + R 2 • 1 {z≥Ms} , (11)
where R 1 and R 2 are given respectively by:

R 1 = FτM s -z (u -t) F1 (u) F1 (t) + u t FτL-τM s (u -v) × F1 (v) F2 (u) F1 (t) F2 (v) f τM s -z (v -t) dv, (12)
R 2 = FτL-z (u -t) F2 (u) F2 (t) , (13)
where FτM s -z , FτL-z , f τM s -z and FτL-τM s are calculated from (3), ( 4) and (5) respectively, Fi , i = 1, 2, is given by ( 8). As such, the conditional MRL of DTDS model is computed by ( 10) and (11).

Fig. 2 shows the MRL surfaces as a function of t and z when the shock rate functions r 1 (t) and r 2 (t) are constant (see Fig. 2a) or linear (see Fig. 2b) of time t. Remark 1. Since the conditional MRL of DTDS model, m z (t), depend on both variables t and z, it reflect not only the system degradation level z but also the shock failure rate r (t, X t ).

• When r (t, X t ) is constant of time t, m z (t) does not depend on t (see Fig. 2a), hence m z (t) and X t return the same information on the system. The MRL prediction may be useless, and a solitary degradation level is sufficient for maintenance decision-making. • When r (t, X t ) depend on time t, m z (t) depends on both z and t (see Fig. 2b), so it contains more information on the system compared to degradation level. In this cas, it is interesting to predict and integrate the MRL in maintenance decision-making. Lemma 2. For a fixed degradation level z, 0 ≤ z ≤ L, the conditional MRL, m z (t), is a non-decreasing function of time. For a fixed time t, 0 ≤ t, the conditional MRL, m z (t), is a non-decreasing function of degradation level.

Proof. Detailed proof is represented in [START_REF] Huynh | Quantification de l'apport de l'information de surveillance dans la prise de décision en maintenance[END_REF]. Corollary 3. For all times 0 ≤ u ≤ v and all degradation levels 0 ≤ x ≤ y ≤ L, the conditional MRL of DTDS model has the following properties:

m z (u) ≥ m z (v) ≥ m y (v) and m z (u) ≥ m y (u) ≥ m y (v) .

CONDITION-BASED MAINTENANCE MODELS

In order to quantify the value of the MRL indicator in maintenance decision-making, we develop in this section the cost model of two quite simple CBM policies. For the first policy, the preventive replacement decision bases on the condition MRL, while for the second one, it relies directly on the degradation level.

Assumptions and objective cost function

Consider a system described by the DTDS model as in section 2. We assume that the system degradation is hid-den and that the system failure is non-self-announcing. This means that the systems reveals only its degradation state and its failures through an instantaneous, perfect and non-destructive inspection (cost C i ). Two maintenance operations are available on the system: a preventive replacement (cost C p > C i ) and a corrective replacement (cost C c > C p ). A replacement can be carried out only at inspections, restores the system to an as-good-as-new (AGAN) condition, and takes negligible time. Therefore, there exits an inactivity time interval after a system failure, and an additional cost is incurred from the failure time until the next inspection time at a cost rate C d .

To evaluate the performance of the maintenance policies, we use in this work a cost criterion which is the longrun expected maintenance cost rate including the unavailability cost C ∞ . Since the system is AGAN after a replacement, we can use the classical renewal theorem to evaluate analytically C ∞ (see [START_REF] Tijms | A first course in stochastic models[END_REF]):

C ∞ = lim t→∞ E [C (t)] t = E [C (S)] E [S] , (14)
where C (•) is the cumulative maintenance cost, and S is the length of a renewal cycle.

MRL-based maintenance model -(∆T, m)

Under the (∆T, m) policy, the system is regularly inspected with period ∆T . At each inspection, the conditional MRL is estimated from the detected degradation level of system. If the value of MRL reaches threshold m and if no failure occurred, the system is preventively replaced. But if the system has already failed, it is correctively replaced. In this case, because of the system inactivity after failure, an addition cost is incurred from the failure time until the replacement time. The inspection period ∆T and the preventive replacement threshold m are the two decision variables to be optimized.

Applying ( 14), the long-run expected maintenance cost rate of (∆T, m) policy can be given by:

C ∆T,m ∞ (∆T, m) = C i E N ∆T,m i + C c 1 -P ∆T,m p E [S ∆T,m ] + C p P ∆T,m p + C d E W ∆T,m d E [S ∆T,m ] , (

Degradation-based maintenance model -(∆T, M )

The (∆T, M ) policy is used as a benchmark here. Under this policy, the system is regularly inspected with period ∆T . At each inspection, if the system degradation level exceeds a degradation threshold M and if no failure occurred, the system is preventively replaced. But if the system has already failed, it is correctively replaced. In this case, because of the system inactivity after failure, an addition cost is incurred from the failure time until the replacement time. The inspection period ∆T and the preventive replacement threshold M are the two decision variables to be optimized. Applying ( 14), the long-run expected maintenance cost rate of (∆T, M ) policy can be given by:

C ∆T,M ∞ (∆T, M ) = C i E N ∆T,M i + C c 1 -P ∆T,M p E [S ∆T,M ] + C p P ∆T,M p + C d E W ∆T,M d E [S ∆T,M ] , (16

ANALYZING THE EQUIVALENCE OF (∆T, M )

POLICY AND (∆T, m) POLICY

The structures of the considered policies show that the difference between them resides in the way to make the preventive replacement decision. So, to analyze the equivalence of them, it is sufficient to study the equivalence of their preventive replacement probabilities. We consider two cases according to r (t, X t ) independent or dependent of time t.

Case of r (t, X t ) independent of time

When the shock rate r (t, X t ) is independent of time, the value of the conditional MRL depend only on the degradation level X t = z, and we denote it simply by m z . Let z m be the minimal degradation level z at which the value of the conditional MRL is less than m:

z m = inf {0 ≤ z ≤ L, m z ≤ m} . (17)
The structure of (∆T, m) policy and the independence in time of MRL lead to represent the event of preventive replacement E p at time k∆T as follows:

E p m z (k-1)∆T > m ≥ m z k∆T , k∆T < τ L , ⇔ X (k-1)∆T < z m ≤ X k∆T , k∆T < τ L , N k∆T = 0 ⇔ {(k -1) ∆T < τ zm ≤ k∆T < τ L , N k∆T = 0} , (18)
where τ zm is the hitting time of z m . This preventive replacement event is similar to the one of (∆T, M ) policy. Thus, the preventive replacement probabilities of both policies are equivalent:

P ∆T,m p (∆T, m) ≡ P ∆T,M p (∆T, z m ) . (19
) Remark 4. For the DTDS model, when the shock rate function is independent of time, both (∆T, m) policy and (∆T, M ) policy are equivalent.

Case of r (t, X t ) dependent on time

When the shock rate r (t, X t ) is dependent of time, the value of the conditional MRL depend on both the degradation level X t = z and on the time t. For the fixed time t, let z t,m be the minimal degradation level z at which the value of the conditional MRL is less than m:

z t,m = inf {0 ≤ z ≤ L, m z (t) ≤ m} .
(20) According to (∆T, m) policy, a preventive replacement is performed at k∆T when:

E p m z (k-1)∆T ((k -1) ∆T ) > m ≥ m z k∆T (k∆T ) , k∆T < τ L , N k∆T = 0} ⇔ X (k-1)∆T < z (k-1)∆T,m , z k∆T,m ≤ X k∆T , k∆T < τ L , N k∆T = 0} ⇔ (k -1) ∆T < τ z (k-1)∆T ,m , τ z k∆T ,m ≤ k∆T < τ L , N k∆T = 0} , (21)
where τ zt,m is the hitting time of z t,m . This event is completely different to the preventive replacement event in (∆T, M ) policy. So, the preventive replacement probabilities of both policies are not equivalent in this case:

P ∆T,m p (∆T, m) ≡ P ∆T,M p (∆T, z t,m ) .

(22) Remark 5. For the DTDS model, when the shock rate depend on time, the (∆T, m) policy is not equivalent to the (∆T, M ) policy.

ANALYZING THE PERFORMANCE OF (∆T, M )

POLICY AND (∆T, m) POLICY

We have shown in the previous section that the (∆T, M ) policy and the (∆T, m) policy are equivalent (resp. different) when the shock rate of DTDS model, r (t, X t ), is independent (resp. dependent) of time. In this section, we study in detail the performance of (∆T, m) policy when r (t, X t ) depends on time. For this end, we compare the optimal maintenance cost rates of the two considered policies in different configurations of maintenance costs and system characteristics. The comparison results allow weighing the benefit of integrating the MRL in maintenance decisionmaking instead of the degradation level.

Numerical results

We consider that case that the shock rates are linear in time: r 1 (t) = 0.0025t + 0.01 and r 2 (t) = 0.0025t + 0.1. Three situations of maintenance costs are investigated:

(1) Variable inspection cost: the dashed lines reflect respectively the (∆T, m) policy and the (∆T, M ) policy. And the lower line corresponds to the policy with higher performance.

Analysis and discussions on numerical results

The numerical results show that the (∆T, m) policy is always more profitable than the (∆T, M ) policy. This phenomenon can be explained by the fact that the conditional MRL contains more information on the system state compared to degradation level, hence making a preventive maintenance decision relied on the conditional MRL can lead to more saving than making a preventive maintenance decision based on the degradation level. Moreover, one can remark that the avantage of (∆T, m) policy is clearer when the variance of degradation process becomes more important. The reason is that, for the DTDS model, not only the failure time due to degradation but also the one due to shock are more dispersive when the variance of degradation process increases (see [START_REF] Huynh | Quantification de l'apport de l'information de surveillance dans la prise de décision en maintenance[END_REF]). So considering the amount of information incorporated into each conditional indicator, the higher the variance the more the degradation level is less advantageous in characterizing the functional behavior of system.

Nevertheless, from practical viewpoint, the (∆T, m) is not always pertinent compared to (∆T, M ) policy. One can see in the Fig. 3 that the optimal expected maintenance cost rates of both considered policies are more or less identical when the variance of degradation is low, the preventive replacement is expensive, and/or the inspection and the system inactivity correspond to low costs. For these situations, the (∆T, M ) policy is preferable, because the MRL prediction takes time but less useful. This shows the necessary of developing analytical models to make a choice on the use of condition MRL or degradation level for maintenance decision-making.

CONCLUSION

The present paper provides a case study to evaluate the advantage of constructing a MRL indicator for CBM decision-making. A condition-based approach is used to calculate the MRL of the DTDS model. Based on this model, the maintenance cost model of a CBM policy implementing MRL-based decision is developed and compared to the one implementing degradation-based decision. The theoretical and numerical results show that the MRL is a more general and effective reliability indicator for CBM compared with the degradation level. However, the choice to resort to MRL prediction has to be cautious and should rely for each configuration on precise quantification step.

In spite of very promising results, the study in this paper is based on a rather strong assumption: perfect and direct monitoring, and Markovian system. Our future work aims to relax this assumption by considering jointly the noisy measurements, the environmental covariates and the nonstationary non-Markovian failure mechanisms of systems. Also, a MRL-based CBM policy with dynamic decision structures will be considered in order avoid more efficiently inopportune interventions.

Fig. 1

 1 Fig. 1 shows an illustration of the proposed DTDS model.

Fig. 1 .

 1 Fig. 1. Illustration of DTDS model

  Fig. 2. MRL -α = 0.1, β = 0.1, L = 30, M s = 15.

  inspection number, the probability of preventive replacement, and the inactivity time interval in a renewal cycle S ∆T,m under (∆T, m) policy. The optimal values of decision variables ∆T opt and m opt are obtained by minimizing C ∆T,m ∞ (∆T, m) where 0 ≤ ∆T and 0 ≤ m ≤ M T T F .

  inspection number, the probability of preventive replacement, and the inactivity time interval in a renewal cycle S ∆T,M under the (∆T, M ) policy. The optimal values of decision variables ∆T opt and M opt are obtained by minimizing C ∆T,M ∞ (∆T, M ) where 0 ≤ ∆T and 0 ≤ M ≤ L.

Fig. 3 .

 3 Fig. 3a, Fig. 3b and Fig. 3c show the evolution of the optimal long-run expected maintenance cost rate for the previous case studies. In each figure, the solid lines and


François Briatte

The Politics of European Public Health Data

Introduction

This chapter examines the effect of public health data on European policymaking. Political stakeholders of all kinds and at all levels of government are frequently observed making non-trivial use of public health data, often in the form of indicators such as mortality rates or expenditure levels. 1 Virtually all forms of strategic reasoning and communicative action are infused with such indicators, most of which have become increasingly available to decision-makers through national and supranational administrative units, academic research centers and third parties like think tanks or international organizations. Nothing about public health data might therefore be deemed entirely new, but their widespread diffusion within and across borders suggests that their political effects may increase.

Given the essential role of the European Union (EU) as a venue for crossnational policy learning, it is then necessary to ask how public health data translate into public health politics when European-wide indicators are used as benchmarks of policy success-or failure. The political valence of public health data cannot be discounted as merely anecdotal insofar as death rates or percentage points of gross domestic product are easily transmogrified into sources of fame or shame for the governments that account for them. Specifically in the context of public health, where numbers frequently stand for either sick or dying populations, the symbolic weight of aggregate measures such as prevalence or survival rates can carry the same psychological impact that pain and death induce at the level of individuals [START_REF] Jennings | Political Responses to Pain and Loss[END_REF]. In that respect, there is nothing trivial about a mortality table, however anonymous its individual observations. Over the past decade or so, systematic indicators have been developed by EUlevel working groups to 'monitor' Member States, a terminology reminiscent of the work done by other supranational organizations like the (OECD) or the World Health Organization (WHO). The principles that underlie this practice, which can be routinely observed in bureaucratic environments, are deeply political: not only do data empower its agents with 'hard facts' and 'objective evidence', they also open up the possibility of measuring macro-level performance in the form of cross-sectional rankings or time-varying indicators. These, in turn, can be used as argumentative weaponry in discussions over the state of public health in individual Member States or at the abstract level of the European region. The myriad symbolic tokens that are mobilized in the process of data reporting, such as in the calculation of an 'average level' of mortality in European countries, are then turned into powerful cognitive cues in the speeches and writings of bureaucrats, politicians and interest groups alike.

In Scott Greer and Paulette Kurzer (eds), European Union Public Health Policy, New York, Routledge, 2012, p. 51-63. 2 This chapter specifically aims to describe the transformative effects of EU-level public health data on health policy-making. At the more general level, it also aims to show how a more precise understanding of the processes at play in public health data, namely quantification and standardization, might feed into current understandings of Europeanization. 'Europeanization' is still what semiotics refers to as a floating signifier, that is, a notion that does not point to any agreed upon meaning. 2 Yet, in its most fundamental incantation, Europeanization does entail the idea of a collective space of reference that is shared by individual Member States, and within which interactions are perceived to produce significant effects at the European and at the domestic levels. Public health data directly contribute to materialize this space of reference when such things as 'Europe-wide data' or even 'European data' start making sense to academics and to their human subjects of inquiry. In that sense, and several others, the matter of understanding how public health data has become a topic of interest for European political actors is closely linked to the scientific challenge of analyzing the Europeanization of public health policy in both practical as well as theoretical terms.

The empirical research question that sustains this inquiry has to do with how European institutions think, or rather, how participants in the EU policy process make sense of what they advocate, claim and discuss among each other. The puzzle takes particular importance for us at the level of the European Commission, because the object under examination, public health data, is produced and/or analyzed in part by Commission services. Specifically, the health statistics around which much of the analysis will revolve are often borne out of joint ventures of the Directorate-General Eurostat (DG Eurostat) and Directorate-General for Health and Consumers (DG Sanco), the departments respectively responsible for statistics and for health issues within the administrative bodies of the Commission. The Directorate-General for Research and Innovation (DG Research) has also provided funding for a lot of the public health data that eventually gets spread through the EU statistical apparatus and well beyond it. Given the source of the data for this analysis, it likely expresses a strong 'Commission bias' that understates and fails to document appropriately the diffusion and discussion of public health data in other EU institutions. Nonetheless, parliamentary debates or Council meetings do show that public health data is actively mobilized in these venues as well. 3 Finally, the theoretical framework used in the present inquiry draws on what has been termed 'common knowledge' in a different political setting [START_REF] Culpepper | The Politics of common knowledge: Ideas and institutional change in wage bargaining[END_REF]. Schematically, the notion of common knowledge designates an intersubjective configuration of human cognition where actors are mutually confronted to "a social fact that cannot be individually wished away" [START_REF] Wendt | Social Theory of International Politics[END_REF]: 161, cited in Culpepper 2008: 4). The essential property of common knowledge lies in the impossibility for political actors to discard its informational content. Specifically, once this form of knowledge has been shared between them, "its invulnerability to unilateral change" then allows it to produce "an independent impact on the bargaining power of actors" (Culpepper 2008: 4-5). 4 Common knowledge requires a form of "convergent diagnosis" won through persuasion and "crossover appeal" among political actors (Culpepper 2008: 5-7). As this diagnosis erodes past conceptions and delegitimizes their institutional settings, it coerces actors into experimenting with new mental sets under a high degree of cognitive uncertainty. The result of that process is the consolidation of a new ideational model to which political actors are eventually led to adjust their future strategies.

The following sections apply this analytical framework to the production and diffusion of public health data among European policy-makers. Section 2 describes the most salient characteristics of the EU-level consensus that has gradually emerged and partially overridden former visions of public health. Section 3 then turns to EU powers over public health data and how even a constrained mandate over health policy can translate into substantial EU-driven activity in the field of public health. Section 4 concludes on not-so-future challenges for public health policy in Europe.

Technical characteristics of the issue

European public health data and government by numbers

Public health data is a compound of systematic observations that aim at measuring the state of health of selected populations. Since the inception of public health policy, these data have frequently taken the form of statistical reports commissioned by political authorities, including the European Commission, which mobilizes its own staff, national representatives from Member States, other international organizations and private interests. 5 With regards to public health, the Commission has established particularly tight collaborative links with the WHO Regional Office of Europe, which works with European as well as Central Asian countries.

The keywords of the aforementioned institutions frequently refer to the lexical universe of quantification: in each of them, "data and evidence"-or simply "databases"-support the development of cross-country reports, geographical atlases and other communication tools such as Web platforms to explore tables and graphs built out of international health data. Some of the most detailed data series echo the origins of statistical integration in nation-states and focus on mortality and causes of death, using the successive versions of the International Classification of Diseases (ICD) as well as age-standardized death rates tailored specifically for European demographics. 6 The history of modern state formation in Western countries indicates that the quantification of a territory is a precious power resource that political authorities have struggled to extract from the populations they rule over, in order to turn collective and discrete social entities into administrable labor or warfare power [START_REF] Foucault | Naissance de la biopolitique[END_REF]. By analogy, European politics is often conceptualized in the form of a conflict-ridden relationship between the supranational authority of EU institutions and the domestic preferences for national sovereignty of national governments accountable to their populations. The precise amount of statistical knowledge that the European Union has secured over nation-state territories is then not just an analogy: it measures the degree of precision with which the EU scrutinizes its Member States. 7 That enterprise of turning populations into legible entities is in itself a practice intrinsic to modern statecraft [START_REF] Scott | Seeing like a State. How Certain Schemes to Improve the Human Condition Have Failed[END_REF].

These general observations resonate significantly in public health, which has virtually always belonged to the core mandate of state power. When urbanization made it a pressing concern to maintain the health of the workforce, 'vital' statistics rose to feature prominently along census data in the history of state-driven quantification [START_REF] Desrosières | Gouverner par les nombres[END_REF][START_REF] Desrosières | La politique des grands nombres[END_REF]. The political history of disease control, however, features frequent acts of resistance to state surveillance, as in the decades since the AIDS epidemic and the emergence of claims to "democratic privacy" [START_REF] Fairchild | Searching Eyes. Privacy, the State, and Disease Surveillance in America[END_REF]. As EU institutions successfully invest in the assemblage of public health data at their own territorial level, some of these conflicts are bound to gain a European dimension, gradually shifting the political space of public health towards supranational venues of decision-making. 8 This new configuration is likely to change the rules of the game for Member States when the legitimacy of a 'European level' for the government of and by numbers becomes virtually unchallenged.

Evidence-based medicine and policy

The other language that European public health policy speaks rather fluently is the language of evidence-based policy-making, although the arcane language of European bureaucracy sounds very modestly engaged on that path as compared to the 'what works' lyricism of national bureaucracies in regulatory states. 9 At present, a striking aspect of the nexus of interests that binds medicine to politics concerns the shared legitimacy of evidence-based decision-making in clinical as well as in policy circles, as seen in the wealth of literature on the topic that has blossomed in the past two decades.

Evidence-based medicine designates the scientific practice that aims at "generat[ing] objective knowledge of effective interventions based, where possible, on the results of unbiased experiments" [START_REF] Daly | Evidence Based Medicine and the Search for a Science of Clinical Care[END_REF]: 1; Timmermans and Kolker 2004). In the mid-nineteenth century, Edwin Chadwick, a pioneer of public health policy, formulated the oldest definition that one might accept for evidence-based policy, though today the randomization component central to "high-quality" clinical trials is still not applicable to policy-making [START_REF] Klein | Evidence and policy: Interpreting the Delphic oracle[END_REF]. Despite this difference, data-driven practices characterize high modernism and professional competition within both clinical medicine and public affairs. In these areas, establishing an evidence base has been simultaneously controversial and appealing to policy-makers [START_REF] Harrison | Policy emergence and policy convergence: The case of "scientific-bureaucratic medicine" in the United States and United Kingdom[END_REF]. 10 Regulatory reforms in European states are not limited to injecting evidencebased instructions into the machinery of government: other processes such as standardization play an increasing role in defining actual state capacity [START_REF] Borraz | Governing standards: The rise of standardization processes in France and in the EU[END_REF]. Similarly, the various tokens of New Public Management have become deeply embedded into the principles of public service reform, inducing an epistemic shift for political and bureaucratic elites towards quantifiable policy performance measured by benchmarks, rankings, indicators of cost efficiency and other forms of public delivery targets [START_REF] Bruno | Europeanization through its instrumentation: Benchmarking, mainstreaming and the Open Method of Co-Ordination… Toolbox or Pandora's Box?[END_REF][START_REF] Bruno | Y a-t-il un pilote dans l'Union ? Tableaux de bord, indicateurs, cibles chiffrées : les balises de la décision[END_REF][START_REF] Ogien | La valeur sociale du chiffre. La quantification de l'action publique entre performance et démocratie[END_REF]. The use of measurement, whether passive or active, has affected virtually all European health care systems in the past two decades, in a more or less gradual fashion and with mitigated success. 11 The overarching logic couched in the deployment of quantification processes and evidence-based discourse at the EU-level is, in the end, fundamentally dependent upon its most immediate clientele, namely, the audience of public and private actors involved in reorganizing health care and public health in the shadow of European governance. To that audience, the investment of European resources into assembling 'EU-level' public health data under the banner of informing policy by evidence has probably looked, and rightly so, like the first step toward a future of shared governance over public health issues. The current state of EU powers and initiatives further confirms that policy-makers have adjusted their behavior to the existence of a political space of European public health, where data is carried by a diversity of stakeholders as a strategic symbolic resource.

EU powers and initiatives

Public health data and the "chaordic" dynamics of EU health policy

Having considered some of the cognitive properties of public health data, we turn to its institutional and political foundations, starting with the apparent paradox that EU powers over health matters are prima facie residual. At its origins, the European Union considered the possibility of supranational health authority through a 'European Health Community' but briskly discarded that alternative, as it also discarded several other policy initiatives that tried to pool national resources under a European heading (Guigner 2008a). Concordantly, the EU treaty base has always explicitly rejected the possibility that health care would fall under the remit of European rule-setters, as illustrated today in the unambiguous wording of Article 168 TFEU. 12 The political legacy and legal framework of European health policy-making hence seems oriented against the principle of European intervention.

However, a model of policy-making that pays attention to spillover dynamics effectively shows that, during the past two decades, policy arbitrages over health care have become increasingly Europeanized through both political and social learning, as shown by the contingencies of European court rulings [START_REF] Greer | Uninvited Europeanization: Neofunctionalism, health services and the EU[END_REF], the adaptation of Member States to European directives in the health care sector (Greer 2009a), and the parallel formation of advocacy coalitions and interest groups around health issues in the Brussels beltway (Greer 2009b). Additionally, strategic interactions between the European Commission and the WHO Regional Office of Europe have led EU institutions to strike a series of ideational bargains over multilateral issues such as pharmaceutical market expansion or tobacco control (Guigner 2008b). The overall public health policy arena has thus become increasingly Europeanized, through 'vertically' binding regulatory measures and through 'horizontally' diffused incentive measures (Guigner 2011a).

For more than thirty years, European public health policy has also been shaped by exogenous shocks that have opened policy windows for public health advocates to extend European powers to new areas of public health concern ( Hervey and Gillmore 2010). The cumulative sum of EU responses to issues like nuclear radiation, food safety or organ donation has spun into a perhaps 'spineless and toothless' but still identifiable European public health system, currently headed by DG Sanco and populated by three regulatory agencies, a federally inspired health surveillance unit, 13 and an executive branch responsible for EU programs in health promotion, consumer protection and food safety. 14 This trend has been progressively matched by explicit mandate adjustments in the EU treaty base, which today prominently asks for the European 'mainstreaming' of health protection at Article 9 TFUE. 15 While this state of affairs is explained largely by accidental logics and often unwarranted spillover effects, the expansion of public health data collection also contributes purposively to structuring the politics of European public health. EU cross-national comparative data provides a salient and shared argumentative resource on which to sustain policy initiatives and build evaluative judgments about the performance of domestic health systems. Steps taken by EU institutions that engage data reinforce the path of European integration of public health policy. This interplay of institutional and ideational factors has been insightfully coined the "chaordic" dynamics of European integration in health policy. On the one hand, the European health policy field has been formed through unpredictable spillovers and shocks. On the other hand, the accretion of deliberate European strategies aimed at governing over health issues form an "issue-specific, fragmented, and incremental process, necessarily technocratic and patchy, but quite consistent" [START_REF] Lamping | European Union and Health Policy: The "Chaordic" Dynamics of Integration[END_REF]Steffen 2009, 1375, emphasis removed).

The possibility for EU actors to be more involved-and more influential-in health policy relies on successful statistical integration, which requires organizational development and widespread trust in the reliability of the data. The history of EU statistical integration shows that producing reliable statistics that are available to market agents, policy-makers and the general public has so far been a largely successful initiative for the European Commission and specifically its 'Eurostat' directorate-general [START_REF] Sverdrup | Administering Information: Eurostat and Statistical Integration[END_REF]. 16 EU statistical services can furthermore count on the cognitive and institutional support of other members in the epistemic community of transnational data purveyors, such as the OECD or the World Bank, which also actively develop standardized health indicators and many other cognitive assets for global governance [START_REF] Davis | Indicators as a technology of global governance[END_REF]. Over the years, the EU has produced several collections of such indicators against which health system performance assessments can be elaborated in domestic policy environments.

The measurement of EU health system performance

Health performance indicators have been adopted rapidly by international organizations like the WHO, which ratified the principle and then published performance targets in its Health for All strategy in the early 1980s. 17 The recent history of EU public health, however, tends to show that EU decision-makers expressed more measured ambitions in that regard. Calls to monitor domestic health systems at the EU-level appeared regularly in decisions from the European Parliament and the Council since at least the Community Action Programme on Health (Baggott 2011: 96). Soft law instruments that encourage cross-national comparison within Member States in order to identify "best practices" for policymaking, like the Open Method of Coordination (OMC), have also had to tread very carefully over health care, partly due to data limitations but primarily because some Member States have expressed their outright hostility to specifically supranational target-setting and performance indicators for health services (Greer and Vanhercke 2010: 209-212). 20 Even in the case of a policy instrument like Structural Funds, which can be allocated under a 'what works' performance objective, the possibility of running comparative assessment that would concern "the running of the healthcare system" has been prudently ruled out so far (Hervey and Vanhercke 2010: 91).

In 2008, the ratification of the Lisbon Treaty provided another opportunity to further the case of EU-level public health monitoring. The Commission indicated in its draft that it would seek to support, 'initiatives aiming at the establishment of guidelines and indicators, the organization of exchange of best practice, and the preparation of the necessary elements for periodic monitoring and evaluation.' 21 This text now appears verbatim in Article 168-1 TFEU. In a similar fashion, and in line with successive EU and WHO commitments to tackle health inequalities in European countries, an elaborate list of health determinants has now been published under the form of European Community Health Indicators (ECHI), ranging from infant mortality to waiting times for elective surgery. 22 It seems, then, that the defusing of EU-level health system comparison by understandably concerned domestic political elites has come to existence through the parallel policy pathway of EU public health programmes.

EU health system performance monitoring still bears the marks of opposition from several Member States. Its current state of development is that of an advanced battery of precise and systematic indicators that closely mirror national initiatives in that domain. At the same time, many national governments have invested in prioritysetting through targets in the past decade. In mapping out the strengths and weaknesses of public health in Europe, the EU has found close allies in other international organizations, as well as in more discrete, but nevertheless active and vocal, members of its epistemic community, such as the European Public Health Alliance and countless collectives of transnational medical elites. As of today, the joint work of EU services are not only inside rather than outside of the political space of European public health, as underlined in the previous section. They have also built a robust frameset of quantitative measurements that bring together various forms of accountability indicators, ranging from expenditure levels to health system inputs and outputs.

Cancer survival rankings and EU health policy

Cancer control provides an interesting illustration of the logic outlined so far. Using this example helps understand how the emergence of performance measures can affect future policy-making, but also reveals the still largely accidental ways in which the politics of European public health data unfold over time. The history of EU activity in cancer control indeed predates and surpasses its involvement with the tackling of other diseases, as cancer, along with AIDS and drug use, has been a privileged target of EU programs and regulations. Since the inception of the "Europe Against Cancer" program (Trubek, Nance and Hervey 2008) in the aftermath of the Chernobyl nuclear accident of 1986, several other EU policies have affected all aspects of cancer control, including clinical trials, screening recommendations, biomedical funding, and most visibly tobacco control, where comparative public health data has played an interesting role in the identification of well-disciplined and less-disciplined Member States (Guigner 2011b).

One aspect of cancer control involves measuring cancer survival. Domestic governments are confronted with geographical inequalities in cancer epidemiology that make them aware of regional leaders and laggards within their territory. However, cross-national comparative data in cancer survival rates is a more recent element of common knowledge, which has been crafted by a small epistemic community of European epidemiologists over the past two decades. The EUROCARE research project, which received EU funding from the BIOMED and FP4 programmes, was at the origin of cross-national cancer survival rankings that played an important role in shaping British health policy in the years 1999-2001; at the outset of its reform episode, the British decision created a policy point of reference for the "European average" of health expenditure, therefore injecting European criteria into national health care reform [START_REF] Briatte | The Europeanization of Health System Performance. The EUROCARE study and Cancer Control in England[END_REF].

There is now a significant amount of consensus in reference to EU levels of conformity, in the form of either passive or active promotion of EU average measurements. These express the same logic of appropriateness and conformance as the "average American" did when surveys began having an impact on models of the mass public in the United States [START_REF] Igo | The Averaged American. Surveys, Citizens, and the Making of a Mass Public[END_REF]. The political substance of European average levels also intermeshed domestic and supranational interests through the passive uploading of measurements and indicators, leading to a situation where they express crossover appeal to all actors. From 2001 onwards, as McHale (2010: 291) recalls, EU discourse started to act as a bandwagon on health issues as domestic interests had previously defined them, largely through the derivation of institutional health system characteristics:

In Scott Greer and Paulette Kurzer (eds), European Union Public Health Policy, New York, Routledge, 2012, p. 51-63. 9

… the European Committee of Social Rights expressed concern that there were increased waiting list times in the United Kingdom and they stated that, in light of the data, they considered that 'the organization of health care in the United Kingdom is manifestly not adapted to ensure the right to health for everyone.'

The referential and disciplinary properties of European public health data are only one instance of their political character. This section only scratches the surface of EU initiatives that revolve around public health data: other EU-level projects exist addressing biobanks of genetic information and tissue, as well as clinical trials. Some cross-national initiatives have also been suggested for the regulation of pharmaceutical markets, where domestic interests limit efforts at regulatory harmonization and comparative effectiveness assessments (Hancher 2010: 681). These initiatives follow a different but perhaps more predictable pattern of EU-level interests, strongly associated with market harmonization principles and standardization strategies. 23 They also operate jointly to mass information programs that advertise, for instance, patient information on public-private partnerships, as well as many other programs that assume a highly educated population capable of (responsible for) making its own health-promoting choices.

Overall, these policy developments have brought market concerns to intermesh with health safety and public health concerns. They have affected the mental models and interest structures of states, markets and societies, reminiscent of what Michel Foucault tentatively captured in his theorization of biopolitics. 24 The politics of European public health data, which eventually revolve around the definition of health system performance issues across European states, match this general characterization, which carries its own risks, as currently reflected under radical austerity and financial crisis.

Future issues

In this analysis, the origins and politics of European public health data can be explained by the success of cross-national public health data becoming common knowledge that is shared by virtually all policy stakeholders in the emergent field of European public health policy. The cognitive properties of EU public health data have been gradually matched by institutional opportunities at the EU level, and finally by a structure of interests where Member States tacitly compete to demonstrate high levels of comparative health performance. Every reference to EU benchmarks-such as the 'European average' value of a given health indicator-adds to the cognitive harmonization and statistical 'creep' of European public health data, leading in some cases to significant reform initiatives by domestic political elites in Member States.

While every policy decision may produce positive feedback effects from which emerge path dependence and institutional "stickiness," it is possible that a macroeconomic decision, for instance, to some extent 'reverses' or even 'cancels out' practical policy effects. In contrast, public health policy and the statistical integration of European public health data are hardly concerned by radical reversals that would lower the policy sustainability of the public health field. 25 Harsher privacy rights, for instance, might thwart but very rarely decrease the precision of disease surveillance. Furthermore, the hypothetical policy decision to axe a public health agency like the European Centre for Disease Control would require many efforts not to look like a completely irresponsible move from a public relations perspective, given the critical context in which such agencies have often emerged in the first place. Finally, as research programs and quantitative information progress incrementally, their cumulative sum clearly pushes public health data towards more legibility rather than less, with ever-larger amounts of information becoming available through increasingly sophisticated instruments.

The future of public health is likely a trend toward more integration in the form of data streams under favorable political and technological circumstances. The internationalization of scientific activity is probably also a major contributor in assuring that public health practitioners look forward to a more collaborative epistemic culture. The future of public health looks quite similar to its past, which has also been a history of gradual expansion if one considers the longue durée of public health data. Additional innovations in computer and network infrastructures as well as in statistical and data science, helped by recent initiatives in favor of democratizing access to "open data", are likely to push that trend forward.

There is a bleak side to this rather optimistic narrative. Permanent fiscal austerity, now topped by financial crisis and dubious democratic politics in several European countries, both inside and outside the European Union, are important causes for concern to anyone with a professional interest in health. The universalization of health systems is a reversible trend: it might progress spectacularly somewhere and gradually retrench on a different subcontinent. European countries are not at all immune of the health effects of austerity on welfare, as [START_REF] Stuckler | The public health effect of economic crises and alternative policy responses in Europe: An empirical analysis[END_REF] have shown using the most recent Eurostat data. A country like Greece, which is currently among the most stricken, is at serious risk of bearing higher death rates under radical austerity, whether due to suicide, drug use or lack of access to medical facilities as affordability collapses for large fractions of the population [START_REF] Kentikelenis | Health effects of financial crisis: omens of a Greek tragedy[END_REF].

A cruel irony, of course, might be that the same institutions that are actively supporting these austerity shocks are also providing scientists with the means to analyze their effects on the health of societies. As McKee and Stuckler (2011: 2-3) recently put it in their correspondence to the European Journal of Public Health: "As researchers, the least we can do is to document the human costs of the crisis, to tell the stories of ordinary people throughout Europe whose lives are being blighted by radical austerity and risky bank maneuvers. There will be more unintended consequences, albeit difficult to predict, calling for a close monitoring of the situation. Our initial studies, recently published in the Lancet, were very simple to do and cost only our time as researchers." However close the "monitoring", though, the body politic is the locus where the political economy of life and death unfolds [START_REF] Foucault | Naissance de la biopolitique[END_REF]). Modern states are hardly the sole site of these biopolitics anymore. The macroeconomic agenda of the European Union is, in itself, a public health policy that might enhance or forestall future public health efforts. The data will tell us.

  Monitoring of 1997, though they were implemented within narrower bounds than those recommended by the Commission. In reference to the first EU public health program, published in 2002,18 Hervey and Vanhercke (2010: 89) note that limitations were applied to the instrumentation of EU public health data into what would have otherwise read like the formation of a supranational framework of comparative health system performance assessment: the Commission's proposals 'to stimulate EU-level action on comparing and assessing health care systems' through the program were removed during the first reading in the co-decision procedure in 2001, highlighting great reluctance by the Member States to accept interference in this domain, even if it 'merely' implied comparisons of performance.Similarly, when another pledge to monitor public health progress in Member States came out ofParliament and Council in 2007, 19 several Member States requested that the budget of the proposed program be axed to thwart any significant advance in that direction

In

Scott Greer and Paulette Kurzer (eds)

, European Union Public Health Policy, New York, Routledge, 2012, p. 51-63.

7

The term 'data' is used hereinafter as an analogue for statistical series made of standardized, quantified measurements, even if a narrative is technically a form of datum. The focus is hence set on numerical rather than literal information.

For a more formal introduction to the conceptual issues that come with the notion of Europeanization, see, inter alia,[START_REF] Radaelli | Conceptual issues[END_REF].

Some quantitative studies of health issues in the European Parliament do exist, however; for a fullfledged approach and comparison to environmental issues, see[START_REF] Princen | Agenda-Setting in the European Union[END_REF].

4 This perspective has the intrinsic merit of reconciling the long-established opposition between rationalist and constructionist views, as it grants causal power to 'ideas' deemed appropriate by actors within a scheme of action that otherwise draws on strategic bargaining and consequentialist reasoning from actors who follow their individual 'interests'. The main background assumption of this analytical framework consists in translating the opposition between rationalist and constructionist arguments, initially exposed in the area of international relations[START_REF] Risse | Communicative Action in World Politics[END_REF], to the study of European politics. That is arguably not a huge step to take. Common knowledge does indeed seem to be a very suitable candidate for a study of European politics that aims at avoiding the theoretical clash between intergovernmental and constructionist views of the European Union

.5 There is no reason to believe that this pattern of production is fundamentally different in other families of statistical data, and the observations made here might therefore carry implications for other sectors of European policy-making.

The relevant policy documentation, such as the Draft Joint Report on Social Protection and Social Inclusion 2010, read like succedanea to the otherwise prolific research on national health accounts and comparative health outcomes developed by the OECD or the World Bank.

Similar objectives can also be found in the white paper Together for Health, published by the Commission in 2007.

A shortlist of 88 ECHI indicators was initially conceived by national public health representatives, DG 'Eurostat', DG 'Sanco' (now DG 'Health & Consumers'), WHO and OECD. For further details, see the ECHI Web pages accessible at http://ec.europa.eu/health/indicators/echi/list/ (last accessed 13 February 2012).

On the mix of public health and market interests that govern over the work of the European Food Safety Agency (EFSA) and the European Medicines Agency (EMA), see[START_REF] Demortain | Institutional polymorphism: The designing of the European Food Safety Authority with regard to the European Medicines Agency[END_REF]. The third European regulatory public health agency, the Community Plant Variety Office (CPVO), which enforces EU intellectual property rights over genotypes, also fits that pattern.

In a nutshell, biopolitics designate the balance of power established over the lifestyle of populations, in a dual process that both compels governments to rule over the vital signs of their citizenry and confronts individuals to their own responsibility in maintaining a state of health[START_REF] Bossy | Les formes contemporaines de la biopolitique[END_REF]. While originally studied in West European nation-states, biopolitics is currently understood to have potential empirical applications at all levels of government.

On policy sustainability, see[START_REF] Patashnik | Reforms at Risk. What Happens After Major Policy Changes are Enacted[END_REF].