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Supporting Information

Impact assessment of emission management strategies of the pharmaceuticals Metformin and Metoprolol to the aquatic environment using Bayesian networks

Caterina Brandmayr, Heide Kerber, Martina Winker, Engelbert Schramm

Institute for Social-Ecological Research (ISOE) GmbH, Hamburger Allee 45, 60486 Frankfurt am Main, Germany

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Supporting Information

1. The BN model

Figure S 1 BN1 Metformin (A compact version of this BN is shown in Figure 2 in the main manuscript) . Components of the BN are color-coded according to their classification: green = public health measures; blue = drug design innovation measures; grey = wastewater treatment measures (i.e. environmental politics); orange = river basin characteristics; red = PEC indicator variable. Arrows define causal relationship. Probabilities shown are calculated by model compilation, while nodes displaying no probability (shown in yellow) are constants. Values at the bottom of numerical nodes are means ± standard deviations of the respective node.

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Figure S 2 BN2 Metoprolol. As for Figure S1, components of the BN are color-coded according to their classification: green = public health measures; blue = drug design innovation measures; grey = wastewater treatment measures (i.e. environmental politics); orange = river basin characteristics; red = PEC indicator variable. Arrows define causal relationship. Probabilities shown are calculated by model compilation, while nodes displaying no probability (shown in yellow) are constants. Values at the bottom of numerical nodes are means ± standard deviations of the respective node.

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Section Node title Node states CPTs generated from Information

Doctor-patient communication

Performance incentive scheme Current; ImprovedAssumption (see scenario simulation, Table S7)

Expert interviews (see SI Section 2)

Curricula for medical students Current; ImprovedAssumption (see scenario simulation, Table S7)


Training of doctors Current; ImprovedAssumption (see scenario simulation, Table S7)


Doctor-patient communication Current; Improved Elicited Probability Tables (see Table S4) Expert interviews (see SI Section 2)

Preventive health

e-Health Current; ImprovedAssumption (see scenario simulation, Table S7)


Healthcare consulting Current; ImprovedAssumption (see scenario simulation, Table S7)


Setting-approach Current; ImprovedAssumption (see scenario simulation, Table S7)


Preventive health measures Current; Improved Elicited Probability Tables (see Table S5) Expert interviews (see SI Section 2)

Participation system Bonus points; Improved bonus points ; Compulsory

Assumption (see scenario simulation, Table S7)


Risk factors

Obesity prevalence [%] 11 - 14; 14 - 17 Elicited Probability Tables (see Table S6)

Initial probabilities generated from data of overall obesity prevalence in adult German population reported in national surveys: Mikrozensus 1999, 2003, 2005 and 2009 (accessed at (Statistisches Bundesamt, 2014b) using search word ‘BMI’), Gesundheit in Deutschland aktuell 2009 (Robert Koch Institut, 2011), Gesundheit in Deutschland aktuell 2010 (Robert Koch Institut, 2012). After compiling with EM learning, EPTs generated based on expert interviews (see Table S6).

Population over 60 years [%] 22 - 25; 25 - 28 Data Percent of population of age 60 and above, years 1998-1999 and 2003-2010 (Statistisches Bundesamt, 2014a)

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Disease prevalence

Diabetes mellitus prevalence [%](BN1 Metformin) 4 - 6; 6 - 8; 8 - 10 Data

Overall prevalence of diabetes in German adult population reported in national surveys: BGS98 (Thefeld, 1999), GSTel03, GSTel04 (Ellert et al., 2006; Kohler and Ziese, 2004), Bertelsmann Healthcare Monitor survey 2004-2008 (Hoffmann and Icks, 2011), Gesundheit in Deutschland aktuell 2009 and 2010 (Robert Koch Institut, 2011; Robert Koch Institut, 2012), DEGS1 (Heidemann et al., 2013). Note that the prevalence data do not take into account the occurrence of a pre-diabetic state, nor do they consider the cases of undiagnosed Diabetes mellitus.

Hypertension prevalence [%](BN2 Metoprolol) 23 - 26; 26 - 29; 29 - 32 Data

Overall prevalence of hypertension in adult German population reported in national surveys: BGS98 (Janhsen et al., 2008), GSTel03, GSTel04 (Ellert et al., 2006; Kohler and Ziese, 2004), GEDA 2009, GEDA 2010 (accessed at (Robert Koch Institut, 2014) using search word ‘hypertonie’), DEGS1 (Neuhauser et al., 2013).

Drug prescription

Prescription of Metformin [Mio g/y](BN1 Metformin)

200 - 450; 450 - 700; 700 - 950; 950 - 1200 Data

Yearly prescription amounts of Metformin, years 1998-1999 and 2003-2010 (see (Schwabe and Pfaffrath, 2011) and same reference for the years 1999-2010). DDD = 2 g/d (WHO Collaborating Centre for Drug Statistics Methodology, 2014a)

Prescription of Metoprolol [Mio g/y](BN2 Metoprolol)

30 - 70; 70 - 110; 110 - 140 DataYearly prescription amounts of Metoprolol, years 1998-1999 and 2003-2010 ((Schwabe and Pfaffrath, 2011) and same reference from years 1999 to 2010). DDD = 0.15 g/d (WHO Collaborating Centre for Drug Statistics Methodology, 2014b)

Drug entry into wastewater

Metformin body excretion[Mio g/y](BN1 Metformin)

0 - 0; 0 - 200; 200 - 450; 450 - 700; 700 - 950; 950 - 1200

Equation

Prescription of Metformin x (1-Proportion of prescription of alternative drugs) x (Metformin bioavailability + (1-Extent of improved bioavailability) x 0.4) x (Extent of drug consumption) x Unmetabolized fraction Metformin bioavailability = 60 % (Bailey and Turner, 1996) Unmetabolized fraction = 100 % (Bailey and Turner, 1996)

Metprolol body excretion[Mio g/y](BN2 Metoprolol)

0 - 0; 0 - 3; 3 - 6; 6 - 9; 9 - 12; 12 - 15 Equation

Prescription of Metoprolol x (1-Proportion of prescription of alternative drugs) x (Metoprolol bioavailability + (1-Extent of improved bioavailability) x 0.5) x (Extent of drug consumption) x Unmetabolized fraction Metoprolol bioavailability = 50 % (Plosker and Clissold, 1992) Unmetabolized fraction = 11% (Lienert et al., 2007)

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Drug entry into wastewater

Metformin incorrectly disposed [Mio g/y](BN1 Metformin)

0 - 0; 0 - 10; 10 - 20;20 - 30; 30 - 40 Equation

Prescription of Metformin x (1-Proportion of prescription of alternative drugs) x (Metformin bioavailability + (1-Extent of improved bioavailability) x 0.4) x (1 - Extent of drug consumption) x Extent of incorrect disposalMetformin bioavailability = 60 % (Bailey and Turner, 1996)

Metoprolol incorrectly disposed [Mio g/y](BN2 Metoprolol)

0 - 0; 0 - 3; 3 - 6; 6 - 9;9 - 12 Equation

Prescription of Metoprolol x (1-Proportion of prescription of alternative drugs) x (Metoprolol bioavailability + (1-Extent of improved bioavailability) x 0.5) x (1 - Extent of drug consumption) x Extent of incorrect disposalMetoprolol bioavailability = 50 % (Plosker and Clissold, 1992)

Drug handling

Drug disposal Correct; Partly correct Assumption Assume Partly correct state probability: 100 %. For sensitivity analysis, assume equal probability for the two states.

Extent of incorrect disposal 0.089 Constant Constant calculated based on survey (Götz et al., 2014). If "Drug disposal” is Correct, assume that constant is 0.

Drug consumption Complete; Partial Assumption Assume Partial state probability: 100 %

Extent of drug consumption Low, Maximal ConstantIf Drug consumption is in state Partial, then constant is Low: 70 % (assume rate of compliance as for monotherapy, see (Bailey and Day, 2009)); if Drug consumption is Complete, then constant is Maximal: 100 %.

Drug design targeting prescription

Prescription of alternative drugs Yes; NoAssumption (see scenario simulation, Table S7)

Proportion of prescription of alternative drugs Constant

0.99 taken as maximal value; 0 for Scenario 1; 0.3 for Scenario 2 (see Table S7) If Prescription of alternative drugs is in No state, then the value for this constant is assumed to be 0 (see calculation of body excretion variables).

Improved drug bioavailability Yes; NoAssumption (see scenario simulation, Table S7)

Extent of improved bioavailability Constant

1 taken as maximal improvement; 0.15 for Scenario 1; 0.2 for Scenario 2 (see Table S7). If Improved drug bioavailability is in No state, then the value for this constant is assumed to be 0 (see calculation of body excretion variables).

River basin characteristics River basin dilution factor Low; Medium; High

Low dilution = 10 (European Medicines Agency, 2006); Medium dilution = 32 and High dilution = 124, respectively the predicted German annual median and 95thile dilution factors (Keller et al., 2014)

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Removal at WWTP

WWTP treatment CAS; MBRAssumption (see scenario simulation, Table S7)

Metformin: removal via CAS = 95 % (Scheurer et al. 2012), removal via MBR = 95% (Mousel et al. Unpublished results; Oosterhuis et al., 2013); Metoprolol: removal via CAS = 25 %, via MBR = 35 % (Radjenovic et al., 2009)

4th treatment step Activated carbon; Ozone; None


Metformin: removal via activated carbon = 20 %, via ozone = 25 % (Mousel et al. Unpublished results); Metoprolol: removal via activated carbon = 95 %, via ozone = 90 % (Margot et al., 2013)

Drug design targeting removal at WWTP

Improved drug removal WWTP Yes; NoAssumption (see scenario simulation, Table S7)

Extent of improved removal WWTP Constant

0.99 taken as maximal value; 0 for Scenario 1; 0.2 for Scenario 2 (see Table S7) If Improved drug removal WWTP is in No state, then the value for this constant is assumed to be 0 (see calculation of PEC(drug) - I).

Improved drug removal Activated carbon Yes; No


Extent of improved removal Activated carbon Constant

0.99 taken as maximal value; 0 for Scenario 1; 0.2 for Scenario 2 (see Table S7) If Improved drug removal Activated carbon is in No state, then the value for this constant is assumed to be 0 (see calculation of PEC(drug) - II).

Improved drug removal Ozone Yes; NoAssumption (see scenario simulation, Table S7)

Extent of improved removal Ozone Constant0.99 taken as maximal value; 0 for Scenario 1; 0.2 for Scenario 2 (see Table S7) If Improved drug removal Ozone is in No state, then the value for this constant is assumed to be 0 (see calculation of PEC(drug) - II).

PEC indicator(s)

PEC(Metformin) - I [ng/L]

0 - 0; 0 - 10; 10 - 50; 50 - 100; 100 - 200; 200 - 300; 300 - 500; 500 - 700; 700 - 1000; 1000 - 5000; 5000 - 10000

Equation(Metformin body excretion + Metformin incorrect disposal) x (100 - WWTP Treatment - (Extent of improved removal WWTP x (100 - WWTP Treatment))) x (10^15) x (1/ German population) / (365 x 200 x River basin dilution factor x 100)

PEC(Metoprolol) - I [ng/L]

0 - 0; 0 - 10; 10 - 50; 50 - 100; 100 - 200; 200 - 300; 300 - 500; 500 - 700; 700 - 1000; 1000 - 5000; 5000 - 10000

Equation(Metoprolol body excretion + Metoprolol incorrect disposal) x (100 - WWTP Treatment - (Extent of improved removal WWTP x (100 - WWTP Treatment))) x (10^15) x (1/ German population) / (365 x 200 x River basin dilution factor x 100)


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PEC(Metformin) - II [ng/L]

0 - 0; 0 - 10; 10 - 50; 50 - 100; 100 - 200; 200 - 300; 300 - 500; 500 - 700; 700 - 1000; 1000 - 5000; 5000 - 10000

Equation [PEC(Metformin) - I] x (1 - (4th treatment step / 100) - (Extent of improved removal activated carbon OR ozonation x (100 - 4th treatment step) / 100))

PEC indicator(s)

PEC(Metoprolol) - II [ng/L]

0 - 0; 0 - 10; 10 - 50; 50 - 100; 100 - 200; 200 - 300; 300 - 500; 500 - 700; 700 - 1000; 1000 - 5000; 5000 - 10000

Equation [PEC(Metoprolol) - I] x (1 - (4th treatment step / 100) - (Extent of improved removal activated carbon OR ozonation x (100 - 4th treatment step) / 100))

PEC(Metformin) surface water < 10 ng/L; 10 - 100 ng/L; > 100 ng/L PEC(Metformin) - II Summary of probability distribution of PEC(Metformin) - II for concentration

ranges < 10 ng/L, 10 - 100 ng/L and > 100 ng/L

PEC(Metoprolol) surface water < 10 ng/L; 10 - 100 ng/L; > 100 ng/L PEC(Metoprolol) - II Summary of probability distribution of PEC(Metoprolol) - II for concentration

ranges < 10 ng/L, 10 - 100 ng/L and > 100 ng/L

Table S 1 Summary of BN components. The table lists the node titles, the respective states and the information used to generate the CPTs. Variables listed refer to both BN1 Metformin (Figures 2 and S1) and BN2 Metoprolol (Figure S2).

2.

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3. Summary of information from experts

Section Node title Expert 1 Expert 2 Expert 3 Expert 4

Doc

tor-

patie

nt c

omm

unic

atio

n

Performance incentive scheme

Currently the effect is medium-low, mostly due to the current concept of "curing a disease". Currently the time-limitations do not allow trust to develop properly. In other systems (more in the direction of e.g. the pay-for-performance system) there are good results and the contact doctor-patient is also more frequent.

Need for a change of system to pay-for-performance, where incentives are given for effective prevention.

Doctors lack the opportunities to influence patients behavior in prevention (mainly due to time limitations). Contact with the doctors is too limited for prevention to be successful.

Curricula for medical students

This is extremely important. Emphasizing communication in the university education will be very important, both with respect to risk communication and disease treatment. Furthermore, it has to be also complemented by training during professional career

Training of doctors

Very important. Possibly less effective compared to teaching already starting from university, but also important to involve current medical staff. Doctors are interested in further training, but this also depends on time and options. There is particular interest especially among the general practitioners.


Currently the effect is medium-low, mostly due to the current concept of "curing a disease". If the communication was improved, then the effect would likely be very large. Important is also the role of the pharmacist. But essential is the concept of trust, and currently the time-limitations do not allow this to develop properly.

Important to emphasize prevention and to develop a communication between doctor and patient where the patient is treated as equal. Also big potential to develop communication with the pharmacist.

Motivation is essential. Doctors key in identification of risk-groups and in communicating the prevention options, but probably more relevant in disease treatment (i.e. secondary and tertiary prevention).

Doctor-patient communication is an important factor in prevention. Change in behavior linked to motivation, information and awareness about the risks so that patients can manage their lifestyle independently. Patient preferences should be considered.

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Pre

vent

ive

heal

th

e-Health Primary prevention: small effect of e-Health; secondary prevention: more effective. Also, must consider which age group is being targeted, with younger generations more likely to have an impact.

e-Health is mostly relevant in secondary prevention. Likely to be ineffective as long as people are (or feel) healthy (i.e. primary prevention).

e-Health is an interesting approach and can be quite useful, especially in the case of diabetes (e.g. monitoring glucose levels) and for children and youth. Apps could also be useful in primary prevention.

Some studies have shown a positive effect, but I cannot tell exactly.

Healthcare consulting

Short-term it might have a large effect, but not sustainable on the long term (in primary prevention). However, counselling is in general important since many people are not aware of unhealthy habits and risk factors.

Consulting could be quite useful to design simple, concrete measures to promote lifestyle changes.

Setting-approach

Education is a crucial aspect. Furthermore, need to reduce the exposure to unhealthy habits. Measures such as tobacco ban or strong emphasis on ingredients in food can also be effective. Furthermore, measures which are broadly implemented are more likely to have an impact.

Situational measures are likely more effective than behavioral, i.e. best to create an environment that promotes healthy lifestyle. Setting-approach is the most crucial preventive measure.

Health should be promoted in the everyday environment (work and living environment, schools). Prevention should be done early on, holistic and "unconsciously". Other measures such as monitoring of sugar/fat/salt content in food can also contribute to prevention. Smoking is also an important risk factor and should be addressed with specific regulations.

It is important, but should be evaluated, whenever possible in an experimental design.

Preventive health measures

Successful prevention should be long-term, start early on and focus on making people aware of the unhealthy habits

Preventive measure should start early on, be perceived as fun and not as an obligation and should emphasize exercise and diet. They should also be long-term, holistic and individually-designed

Participation system

The bonus point system has a positive effect on people that take part, but these are only few (possibly because perceived as too abstract). If made compulsory, then if would probably be effective (though not sure how that can be enforced).

A compulsory system wouldn´t work, it would instead penalize the people. The bonus-point is currently inefficient since only a small fraction benefits from it.

The bonus point system currently reaches only the people who are already actively following a healthy lifestyle. However, a compulsory system would be counterproductive since it would not be perceived as "fun". A more effective way would be to show the effects of a short trial program, so to motivate participation.

A compulsory system might be effective. I am not sure if a financial penalty would enhance the effectiveness. There should be positive effects e.g. regarding safety belts in cars; however, effects of e.g. taxes for tobacco, alcohol, etc. are very complex.

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Ris

k fa

ctor

s

Obesity prevalence [%]

Preventive measures can change the lifestyle. Might have a small effect, but if broadly implemented will still be relevant. Strengthening measures targeting body weight, nutrition and sports, might result in a substantial change in lifestyle. Might be more complex in the case of other unhealthy lifestyles, e.g. smoking.

Data shows that in the last 10 years there has been an increase in the BMI, also of the proportion of people affected by obesity (BMI > 30).

Lifestyle can be changed long-term, but requires prevention that is perceived as "fun" and starts early on. In prevention studies focusing on pre-diabetic, observe a successful change for 50-60% of the cases (Tuomilehto et al., 2001). However, hard to translate these result on the overall population.BMI often used as indicator, but not always accurate. However, good indicator if considering obesity (BMI > 30)

BMI is a good indicator (although in some cases there are some limitations, waist circumference might be an alternative).

Dis

ease

pre

vale

nce

Diabetes mellitus prevalence [%]

Decrease in BMI has been shown to lead to a decrease in diabetes incidence. In fact, people who are overweight are more likely to become pre-diabetic and those who are pre-diabetic with high BMI more likely to become diabetic (therefore also reasonable to assume that if manage to reduce degree of obesity, might also lead to reduction of diabetes). If intensive prevention (for prediabetic, see Finnish study (Tuomilehto et al., 2001)), then decrease incidence by 50-60%. But could be more effective if prevention started earlier on.

Strong preventive measures can reduce diabetes incidence by 50 % (on pre-diabetic patients) (Tuomilehto et al., 2001), which may lead to a decrease in age-specific prevalence of diabetes.Suitable to describe diabetes as dependent on both age and BMI (increase of absolute numbers of individuals with diabetes due to the ageing of the population).Also reasonable to assume that reduction in BMI leads to reduced incidence of diabetes, however, in some countries the age-adjusted diabetes prevalence remained almost stable although the prevalence of obesity increased.

Table S 2 Summary of expert interviews

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Section Measures Expert 1 Expert 2 Expert 3 Expert 4Which approach has the greatest potential to improve the doctor-patient communication in the context of primary and secondary prevention? Please number the measures in order of importance (1 = most successful, 3 = least successful).


Performance incentive scheme 2 1 1 -

Curricula for medical students 1 2 2 -

Training of doctors 3 2 3 -

Which approach is most promising in the context of primary prevention? Please number the measures in order of importance (1 = most successful, 4 = least successful).

Preventive health

Doctor-patient communication 3 - 4 -

e-Health 4 - 3 -

Healthcare consulting 1 - 2 -

Setting-approach 2 - 1 -

Which participation system is most promising in the context of primary prevention to promote an effective and durable change of lifestyle (with focus on nutrition and physical activity)? Please number the systems in order of importance (1 = most successful, 3 = least successful).

Participation system

Bonus point system 3 - 3 -

Improved bonus point system 2 - 1 -

Compulsory system 1 - 2 -

Table S 3 Summary of expert questionnaire responses (missing answers are indicated by the sign “-“)

4. Stakeholders discussion

The stakeholder consultation regarding the BN variables was done in a one day workshop. Stakeholders were already familiar with the three sectors and the specific measures discussed in this study (sectors and measures had in fact been previously selected through a participatory process, see (Kerber et al., 2014a)). After introducing the BN structure, stakeholders were divided in two groups, the first one discussing the public health market sector, the second one focusing on the environmental politics.

For the group discussing the public health market sector, stakeholders were asked to comment on the relative efficiency of the different measures considered. Groups of measures were discussed in the context of preventive health measures (e-Health, Healthcare consulting, Setting-approach, Doctor-patient communication), Participation system (Bonus points system, compulsory system) and Doctor-patient communication (Performance incentive scheme, Training of doctors and Curricula for medical students).

The second group was asked to comment on the most likely 4th treatment step option to be implemented for wastewater purification among treatment with Activated carbon, Ozone or nanofiltration. Additionally, they were asked to identify the most likely combination of standard biological treatment (CAS or MBR) and subsequent 4th treatment step technology.

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5. Elicited CPTs

Elicited Probability tables (EPTs) for the variables “Doctor-patient communication”, “Preventive health measures” and “Obesity prevalence” were generated based on the information gathered from the experts interviews and stakeholder consultations presented in the sections 2 and 3 above. From the EPTs, the respective CPTs where then calculated according to Cain (2001).

Parent nodesProbability (%) of node “Doctor-patient communication” to be in improved stateTraining of doctors Curricula for medical

studentsPerformance

incentive scheme

Improved Improved Improved 100

Current Current Current 0

Current Improved Improved 95

Improved Current Improved 90

Improved Improved Current 40

Table S 4 Elicited Probability Table (EPT) for the variable “Doctor-patient communication”.

Parent nodes Probability (%) of node “Preventive health measures” to be in the improved

statee-Health Healthcare consulting

Setting-approach


Improved Improved Improved Improved 100

Current Current Current Current 0

Current Improved Improved Improved 95

Improved Current Improved Improved 80

Improved Improved Current Improved 50

Improved Improved Improved Current 75

Table S 5 Elicited Probability Table (EPT) for the variable “Preventive health measures”.

Parent nodesProbability (%) of node “Obesity prevalence”

to be in 11 - 14 (%) statePreventive health measures Participation system

Improved Improved bonus points 70

Current Bonus point 0

Current Compulsory 5

Current Improved bonus points 30

Improved Bonus points 40

Improved Compulsory 40

Table S 6 Elicited Probability Table (EPT) for the variable “Obesity prevalence”.

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6. Scenarios probabilities

Starting probabilities of each measure where decided based on the scenario outline as described by Kerber et al. (Kerber et al., 2014b). Note that the scenarios simulated in this study using the BN model refer to the extent of implementation of each measure in Germany in 2030.

Section Node title Description Scenario 1 (Trend scenario) Scenario 2 (Sustainability scenario)


Performance incentive scheme

Shift in the performance incentive scheme from a curative approach to a preventive one where doctors are encouraged to prevent the disease rather than curing it.

Probability of Improved state: 0 %Probability of Improved state: 100 % ("pay for performance" system or similar fully implemented in Germany)

Curricula for medical students

Change in the curricula for medical students, stronger emphasis on prevention and health promotion and teaching of techniques to improve doctor-patient communication

Probability of Improved state: 10 % Probability of Improved state: 100 %

Training of doctors

Training of doctors on the subjects of prevention, health promotion, polypharmacy, adherence and geriatrics; participation is made compulsory for general practitioners

Probability of Improved state: 40 % Probability of Improved state: 80 %

Preventive health

e-HealthSupport of primary prevention using mobile apps, telemedicine and electronic case files for the patient

Probability of current state: 50 % Probability of current state: 60 %

Healthcare consulting Individual health consulting, especially for high risk groups (e.g. children and youth, elderly)

Probability of current state: 20 % (pro-actively proposed by health insurance for high-risk patients)

Probability of current state: 50 % (prevention is strongly advertised and people are more aware also due to implementation of setting approach measures)

Setting-approach Systemic preventive health approach in the local setting (e.g. schools, workplace, district) Probability of current state: 20 %

Probability of current state: 65 % (not complete implementation, there will always be groups which cannot be reached effectively, e.g. due to infrastructure, socio-economic difference, etc.)

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Section Node title Description Scenario 1 (Trend scenario) Scenario 2 (Sustainability scenario)

Preventive health Participation system

System supporting people´s participation in prevention; bonus point system: current system supported by public health insurance; improved bonus point system: strengthening through improved communication and accessibility of the services and modification of the premium system following the current dentist-reimbursement model; compulsory system: regular participation is compulsory for high risk groups and for people above the age of 40, in the case of non-participation there is a penalty imposed

Probability of Bonus points state: 70 % Probability of Improved bonus points state: 30 %

Probability of Improved bonus points state: 90 % Probability of Compulsory state: 10 %

Drug design targeting prescription

Prescription of alternative drugs

Shift in prescription practice towards drugs that are more environmentally friendly. Implies the development of drugs using "green chemistry" and awareness of health care practitioners of environmental impact of pharmaceuticals

Probability of Yes state: 100 % Probability of Yes state: 100 %

Proportion of prescription of alternative drugs

Extent to which prescription shifts towards alternative, environmentally friendlier drugs Constant value: 0 (Due to lack of incentives)

Constant value: 30 (Assumes availability of environmentally friendlier drugs and awareness by the medical staff of the environmental impacts of pharmaceuticals)

Improved drug bioavailability Improvement of drug bioavailability Probability of Yes state: 100 % Probability of Yes state: 100 %

Extent of improved bioavailability

Extent to which the drug bioavailability is improved compared to standard bioavailability Constant value: 15 Constant value: 20

Removal at WWTP WWTP treatment

Removal during standard WWTP either by Conventional Activated Sludge or Membrane Bioreactor

Probability of MBR state: 10 % (refers to proportion of newly constructed WWTP which have space restrictions)

Probability of MBR state: 10 % (refers to proportion of newly constructed WWTP which have space restrictions)

Section Node Title Description Scenario 1 (Trend scenario) Scenario 2 (Sustainability scenario)

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Removal at WWTP 4th treatment step 4th purification step at WWTP (after standard

treatment using CAS or MBR)

Probability of Activated carbon state: 17.5 %Probability of Ozone state: 17.5 %

In this scenario, about 35 % of WWTP will be upgraded (with Activated Carbon or Ozone). Upgrading will take place at sensitive spots, i.e. those contributing to drinking water production, and according to funding possibilities.

Probability of Activated carbon state: 25 % Probability of Ozone state: 25 %

A modified financing mechanism enables a broader implementation of the 4th treatment step. However, towards 2030 discussions are taking place concerning the need to upgrade WWTPs, since the broader set of preventive measures is showing positive effects in reducing the contamination.

Drug design targeting removal at WWTP

Improved drug removal WWTP

Improvement of drug removal during standard wastewater treatment Probability of Yes state: 100 % Probability of Yes state: 100 %

Extent of improved removal WWTP

Extent to which removal via standard wastewater treatment is improved Constant value: 0 (Due to lack of incentives)

Constant value: 20 (modification of the current drug to improve its environmental impact; assumes doctors prescribe improved drug)

Improved drug removal Activated carbon

Improvement of drug removal during treatment with activated carbon Probability of Yes state: 100 % Probability of Yes state: 100 %

Extent of improved removal Activated carbon

Extent to which removal via treatment with activated carbon is improved Constant value: 0 (Due to lack of incentives)


Improved drug removal Ozone

Improvement of drug removal during treatment with ozone Probability of Yes state: 100 % Probability of Yes state: 100 %

Extent of improved removal Ozone

Extent to which removal via treatment with ozone is improved Constant value: 0 (Due to lack of incentives)


Table S 7 List of starting probabilities of each measure for Scenario 1 and 2.

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7. Scenario sensitivity analysis

Table S 8 Sensitivity analysis of “PEC(Metformin) – II” and “PEC(Metoprolol) – II” for Scenario 1 and Scenario 2.

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