The Use (and Misuse) of 'Cognitive Enhancers' by students at an Academic Health Sciences Center

Philosophy

Purpose
          Prescription stimulant use as âcognitive enhancersâ has been described among undergraduate college students. However, the use of prescription stimulants among future health care professionals is not well characterized. This study was designed to determine the prevalence of prescription stimulant misuse among students at an academic health sciences center.

          Method
          Electronic surveys were e-mailed to 621 medical, pharmacy, and respiratory therapy students at East Tennessee State University for four consecutive weeks in fall 2011. Completing the survey was voluntary and anonymous. Surveys asked about reasons for, frequency of, and side effects of nonprescription misuse of prescription stimulants. Given the sensitive material, an opportunity to win one of ten $50 gift cards was used as an incentive.

          Results
          Three hundred seventy-two (59.9%) students completed the survey from three disciplines (47.6% medical, 70.5% pharmacy, and 57.6% respiratory therapy). Overall, 11.3% of responders admitted to misusing prescription stimulants. There was more misuse by respiratory therapy students, although this was not statistically significant (10.9% medicine, 9.7% pharmacy, 26.3% respiratory therapy; P = .087). Reasons for prescription stimulant misuse included to enhance alertness/ energy (65.9%), to improve academic performance (56.7%), to experiment (18.2%), and to use recreationally/get high (4.5%).

          Conclusions Prescription stimulant misuse was prevalent among participating students, but further research is needed to describe prevalence among future health care workers more generally. The implications and consequences of such misuse require further study across professions with emphasis on investigating issues of academic dishonesty (e.g., âcognitive enhancementâ), educational quality, and patient safety or health care quality.

Copyright © by the Association of American Medical Colleges. Unauthorized reproduction of this article is prohibited. Academic Medicine, Vol. 88, No. 7 / July 2013 1 Research Report Misuse of prescription stimulant medications is a growing problem, particularly among high school students and college-aged young adults.1,2 According to the National Survey on Drug Use and Health, nonmedical prescription use (or misuse) is defined as use of prescription drugs not prescribed for the respondent or respondent use only for the experience of the feeling they caused.3 Often, people who misuse prescription stimulants are hoping to improve their ability to concentrate and also take advantage of potential side effects of the medications, including decreased need for sleep. A recent review by Smith and Farah4 concisely summarized the use of prescription stimulants in normal individuals by evaluating a variety of studies using tasks designed to assess learning, working memory, and executive function. They concluded that prescription stimulants "appear to enhance retention of recently learned information and, in at least some individuals, also enhance working memory and cognitive control." However, "there remains great uncertainty regarding the size and robustness of these effects and their dependence on dosage, individual differences, and specifics of the task."4 The only consensus regarding the use of these drugs is that people genuinely appear interested in cognitive enhancement. At the undergraduate level, frequency of prescription stimulant misuse as "cognitive enhancers" by college students was estimated at 3% to 10% in a 2005 study, with the majority of responding students taking the medication to prepare for exams.5 However, evidence of prescription stimulant misuse among medical students and other future health care professionals is scant. Tuttle and colleagues6 described a 10.1% lifetime incidence of nonmedical prescription stimulant misuse within one public college of medicine. Lord and colleagues7 reported a 6.7% incidence of prescription stimulant misuse at a private college of pharmacy. McNiel and colleagues8 surveyed dental and dental hygiene students and found that 10% of dental students and 18% of dental hygiene students reported misusing Abstract Purpose Prescription stimulant use as "cognitive enhancers" has been described among undergraduate college students. However, the use of prescription stimulants among future health care professionals is not well characterized. This study was designed to determine the prevalence of prescription stimulant misuse among students at an academic health sciences center. Method Electronic surveys were e-mailed to 621 medical, pharmacy, and respiratory therapy students at East Tennessee State University for four consecutive weeks in fall 2011. Completing the survey was voluntary and anonymous. Surveys asked about reasons for, frequency of, and side effects of nonprescription misuse of prescription stimulants. Given the sensitive material, an opportunity to win one of ten $50 gift cards was used as an incentive. Results Three hundred seventy-two (59.9%) students completed the survey from three disciplines (47.6% medical, 70.5% pharmacy, and 57.6% respiratory therapy). Overall, 11.3% of responders admitted to misusing prescription stimulants. There was more misuse by respiratory therapy students, although this was not statistically significant (10.9% medicine, 9.7% pharmacy, 26.3% respiratory therapy; P = .087). Reasons for prescription stimulant misuse included to enhance alertness/ energy (65.9%), to improve academic performance (56.7%), to experiment (18.2%), and to use recreationally/get high (4.5%). Conclusions Prescription stimulant misuse was prevalent among participating students, but further research is needed to describe prevalence among future health care workers more generally. The implications and consequences of such misuse require further study across professions with emphasis on investigating issues of academic dishonesty (e.g., "cognitive enhancement"), educational quality, and patient safety or health care quality. Dr. Bossaer is assistant professor, Department of Pharmacy Practice, Gatton College of Pharmacy, East Tennessee State University, Johnson City, Tennessee. Dr. Gray is assistant professor, Department of Pharmacy Practice, Gatton College of Pharmacy, East Tennessee State University, Johnson City, Tennessee. Dr. Miller is assistant professor, Department of Pharmacy Practice, Gatton College of Pharmacy, East Tennessee State University, Johnson City, Tennessee. Mr. Enck is a PhD candidate, Philosophy Department, University of Tennessee, Knoxville, Tennessee, and integrated medical ethics fellow, MD Anderson Cancer Center, Houston, Texas. Mr. Gaddipati is an internal medicine resident, University of Cincinnati Medical Center, Cincinnati, Ohio. Dr. Enck is professor, Department of Internal Medicine, Quillen College of Medicine, East Tennessee State University, Johnson City, Tennessee. Acad Med. 2013;88:00–00. First published online doi: 10.1097/ACM.0b013e318294fc7b Academic Medicine 1040-2446 10.1097/ACM.0b013e318294fc7b XXX Research Report 88 7 00 00 2013 xxx July 2013 The Use and Misuse of Prescription Stimulants as "Cognitive Enhancers" by Students at One Academic Health Sciences Center John B. Bossaer, PharmD, Jeffrey A. Gray, PharmD, Stacy E. Miller, PharmD, Gavin Enck, Vamsi C. Gaddipati, MD, and Robert E. Enck, MD Acad Med. 2013;88:00–00 LWW Correspondence should be addressed to Dr. Bossaer, Box 70657, Johnson City, TN 37614; telephone: (423) 439-6757; fax: (423) 439-6784; e-mail: email@example.com. Research Report Copyright © by the Association of American Medical Colleges. Unauthorized reproduction of this article is prohibited. Research Report Academic Medicine, Vol. 88, No. 7 / July 20132 prescription stimulants for nonmedical purposes at least once during their lifetime. Whereas adverse event data from the prescribed use of stimulants are known from clinical trial data,9,10 the long-term consequences of prescription stimulant misuse are unknown, but worth considering. The use of prescription stimulants as cognitive enhancers to improve academic performance may constitute academic dishonesty in the eyes of some. It is possible that the misuse of prescription stimulants may make students dependent on these effects in order to function as competent health care professionals. We speculate that such dependence could affect a clinician's wellness and potentially have an impact on patient care. On the basis of these concerns, we designed a survey to ask health care students within a single institution about their nonmedical misuse of prescription stimulants. Students from several disciplines were surveyed about their perceptions, history, and motives regarding prescription stimulant misuse. To our knowledge, this is the first study of its kind to survey multiple disciplines within the same institution. Method We collected data through the use of a voluntary, confidential online survey distributed to 786 students at East Tennessee State University. Faculty leaders within each discipline (medicine, pharmacy, advanced nursing, respiratory therapy, and physical therapy) were invited to distribute the survey. We only evaluated disciplines with a meaningful survey response rate (deemed by the investigators to be >40%) to protect the generalizability of data. All students received the survey link via campus e-mail. The link was distributed within each respective discipline for four consecutive weeks during the 2011 fall semester. The survey was created by an interprofessional group of health care professionals (J.B., J.G., S.M., R.E.) from the divisions of medicine and pharmacy. The anonymous survey contained 23 unique items and incorporated mostly closed-ended, multiple-choice questions (see Appendix 1 for examples). We used question logic to minimize length of the survey and ask specific questions of nonmedical users. For example, only those who admitted to using prescription stimulants without a prescription were asked about side effects of stimulant use. Demographic information requested included student GPA, health care discipline, year(s) in professional program, and whether the participant had been diagnosed with a disorder requiring a prescription stimulant medication (i.e., attention deficit hyperactivity disorder [ADHD]). Anticipating a high degree of participant sensitivity to the topic and data collection process, we felt the study process should include inherent characteristics which promote data integrity and participant anonymity. To ensure anonymity, demographic data such as age, gender, and race were not collected. Additionally, the IP (internet protocol) address collection capability of SurveyMonkey was disabled. Prescription stimulants available in the United States are • amphetamine/dextroamphetamine (Adderall), • dexmethylphenidate (Focalin), • dextroamphetamine (Dexedrine), • methamphetamine (Desoxyn), • methylphenidate (Ritalin, Methylin, Metadate, Concerta), and • modafinil (Provigil). Survey sequencing addressed prescription stimulant misuse of these six drugs, motivation for nonmedical use, deceptive practices, frequency of use, consequences of nonmedical use, and peer group nonmedical use. Survey takers could select multiple answers and use a comment box where appropriate. We defined nonmedical misuse as using a prescription stimulant without a prescription or using more often than prescribed. Responders who noted that they had ADHD or narcolepsy were only considered misusers if they admitted to using prescription stimulants without a prescription or more often than prescribed. We also presented responders with a prescription stimulant use scenario and asked whether the scenario constituted academic dishonesty. They were also asked whether faculty knowledge of prescription stimulant use would adversely affect their education. Survey development included an expert panel review and beta testing. Beta testing was initiated during the spring semester of 2011 in health care students prior to graduation. We did not include beta test results in the final analysis. In an effort to encourage student participation, we offered the opportunity to win one of ten $50 gift certificates as an incentive. Once a participant completed the survey, she or he was directed to an independent and secure Web site for submission of contact information which could not be connected to a responder's survey results. We analyzed all statistics using PASW Statistics 18 (SPSS, Chicago, Illinois). Student t test and chi-square test were used to analyze continuous and categorical data. Because of large amount of data collected, we only present results related to the primary objective of this study, which was to describe the misuse of prescription stimulants by medical, pharmacy, and respiratory therapy students. Further data analysis is ongoing. IRB approval was granted by East Tennessee State University prior to study initiation. Results A total of 372 out of 621 (59.9%) students responded to the survey from the medicine, pharmacy, and respiratory therapy (RT) disciplines, which comprises the set of data analyzed. Because only 10% of advancing nursing students responded and no faculty leader from physical therapy agreed to distribute the survey, these data (the remaining 165 of the 786 students) were not analyzed. The response rates were 47.6% (128/269) for medical students, 70.5% (225/319) for pharmacy students, and 57.6% (19/33) for respiratory therapy students. Forty-four (11.3%) of responders admitted to nonmedical prescription stimulant misuse. Seven of these students reported having been diagnosed with ADHD in the past, yet admitted to misuse (use without a prescription). Additionally, 22 students with an ADHD diagnosis did not admit to misuse. Thirty-three (75%) responding nonmedical users obtained prescription Copyright © by the Association of American Medical Colleges. Unauthorized reproduction of this article is prohibited. Research Report Academic Medicine, Vol. 88, No. 7 / July 2013 3 stimulants from a friend or acquaintance, whereas 13 (29.5%) had obtained prescription stimulants from classmates. Fourteen (10.9%) medical students, 22 (9.7%) pharmacy students, and 5 (26.3%) RT students admitted to using a prescription stimulant not prescribed to them. Three responders did not specify their discipline. Despite the numerically higher rate of nonmedical use in RT students, there was no statistical difference (P = .087) between the medicine, pharmacy, and respiratory therapy disciplines. A total of 329 (52.9%) responders provided their GPA. The average GPA was similar (P = .18) between those who misused prescription stimulants (3.42) and those who did not (3.51). Among the 44 nonmedical users, the reasons for misusing prescription stimulants included to improve academic performance (56.7%), to enhance alertness or energy (65.9%), to get high or to use recreationally (4.5%), and to experiment (18.2%). Because the survey did not address legitimate use of prescription stimulants (such as use with a valid prescription), a description of the frequency of prescription stimulant use cannot be made. The most commonly reported adverse reactions by nonmedical users included lack of appetite (68.2%) and difficulty sleeping (54.5%). Additional adverse drug reactions experienced by >5% of the 44 prescription nonmedical users were • racing heart, 15 (34.1%), • anxiety, 7 (15.9%), • tremor, 7 (15.9%), • headache, 6 (13.6%), and • dependence on prescription stimulants for concentration, 4 (9.1%). Additional negative consequences of prescription stimulant misuse included criticism from others (6.8%), increased physician visits (2.3%), excessive tobacco use (2.3%), and an inability to take some over-the-counter medications due to a drug–drug interaction causing the "heart to skip beats." No responders had sought treatment for substance abuse, experienced legal consequences, or needed urgent/emergent medical treatment secondary to prescription stimulant abuse. Twenty-seven (61.4%) of the nonmedical users agreed with the statement "If I used prescription stimulants and faculty found out, it would adversely affect my education." Only 12 (27%) responders disagreed with this statement. Five responders declined to comment. Responders were presented with the following ethics-probing vignette and asked to answer "Yes" or "No" if the scenario constitutes academic dishonesty: "'Annie' is in her first year of a professional health program and is having trouble staying awake to do the required reading. She has a 4.0 GPA and experiences a lot of pressure from family to maintain this excellent performance. She decides to start using a prescription stimulant medication, Ritalin, during finals week so she can stay awake longer and concentrate better, despite having never been diagnosed with an illness requiring Ritalin." A slight majority (55.8%) of the 372 responders believed this constituted academic dishonesty, and 59.9% agreed that using prescription stimulants provides an unfair advantage to health professions students. Discussion To our knowledge, this is the first multidisciplinary study of prescription stimulant misuse. One in 10 future health care professionals from the medical, pharmacy, and respiratory therapy disciplines completing our survey acknowledged their nonmedical misuse of prescription stimulants, or cognitive enhancers. The majority of responders (61.4%) perceived that if faculty found out about their nonmedical use, it would negatively affect their education. Therefore, it is reasonable to suggest that the true incidence of prescription stimulant misuse may be higher than reported in our voluntary survey. Given student concern over negative perceptions of misuse, it is very possible that many nonmedical users did not respond to the survey because of this fear. Therefore, one must consider this limitation when considering the validity of our data. Prescription stimulants carry a risk for abuse and dependence.9,10 The incidence of psychosis or withdrawal-associated depression is not known for prescription stimulants when used as cognitive enhancers. Moreover, the high rates of insomnia and lack of appetite in our study raise concerns for student wellness as the shortand long-term effects of cognitive enhancement with stimulants are not known. Most of the health science students who responded used cognitive enhancers to enhance alertness or energy (65.9%) or to improve academic performance (56.7%). It is unknown whether the misuse of prescription stimulants as cognitive enhancers leads to improved academic performance; this is an area for future research. In this study, we used GPA as a surrogate marker for academic performance. Although readily available, GPA may not be a good marker. Future placement in competitive residency programs, performance on standardized tests, or performance on clerkship rotations may be better markers of academic performance. Students appear divided on this issue, with 55.8% of responders believing the "Annie" vignette portrayed academic dishonesty and 59.9% believing that using cognitive enhancers provides an unfair academic advantage. This is likely to become a divisive and controversial subject within colleges of health sciences for three reasons. First, the use of prescription stimulants as cognitive enhancers may challenge perceptions of academic dishonesty. One may argue that because cognitive enhancers improve energy and concentration for studying above and beyond what would normally be physiologically possible, this unfair academic advantage is tantamount to cheating.11,12 Yet cheating has been defined as "the intentional violation of a rule in order to gain an unfair advantage over others."13 Because our institution does not explicitly forbid the use of cognitive enhancers, one could also argue that the use of cognitive enhancers (e.g., misuse of prescription stimulants) is not cheating. Institutions wishing to limit the nonmedical use of cognitive enhancers should consider explicit policies. Also, specifying exactly what constitutes an unfair advantage is often difficult to Copyright © by the Association of American Medical Colleges. Unauthorized reproduction of this article is prohibited. Research Report Academic Medicine, Vol. 88, No. 7 / July 20134 do: Does a student who lives in quiet apartment or who can afford a better computer have an unfair academic advantage? Does a caffeine drinker have an academic advantage over a non– caffeine drinker? Although caffeine is also a stimulant, it is not illegal to use without a prescription. Generally, health habits such as exercise, a nutritious diet, and adequate sleep have been suggested to be cognitive enhancing.2 Therefore, questions about the use or misuse of cognitive enhancers pose a significant challenge to colleges of health sciences. The second reason that the misuse of prescription stimulants as cognitive enhancers is a divisive and controversial subject for colleges of health sciences concerns the guidelines for prescribing these medications, schedule II controlled substances. Seven of the 44 (16%) responders who misused prescription stimulants claimed to have been diagnosed with ADHD. Although misuse of prescription stimulants by professional students with ADHD may be seen as no problem by some, such a diagnosis does not excuse using controlled substances without a prescription. Similar misuse has contributed to an epidemic of accidental and fatal overdoses from opioids, also controlled substances.14 Moreover, such illegal activity may prohibit or delay licensure for health care professionals. None of the misusers in our study experienced any legal consequences, but it is unknown from our survey whether our participants considered such legal consequences when deciding to misuse controlled substances. Colleges and universities should consider educating students on the potential legal and career ramifications of prescription stimulant misuse. The guidelines and standards for physicians to prescribe these cognitive enhancers are also unclear. For example, the American Academy of Neurology15 recently published a guide for neurologists that concludes that the decision to prescribe cognitive enhancers to healthy patients ultimately rests on a neurologist's discretion. Therefore, physicians who have used such medications "off-label" or without a prescription may not be discriminating in prescribing cognitive enhancers for those with and without legitimate medical purposes. Last, and perhaps most important, the effects of the practice of "cognitive enhancing" on patient care are not known. Students misusing prescription stimulants as cognitive enhancers may begin to rely on them as a crutch to study and perform well on exams. The question of whether removal of this crutch affects clinical performance needs to be addressed by future research. Conceivably, patients could argue that if a physician used cognitive enhancers during medical school, that physician should continue using them in the clinic or hospital. Further investigation of this would likely require longitudinal study of prescription stimulant users throughout school, postgraduate training, and professional practice. One could argue that increased concentration ("enhanced" or otherwise) is more important in a clinical setting than the relatively safe environment of higher education. The same holds true for other health professions. This study did not differentiate between classroom misuse and clinic misuse. Further research is needed to better elucidate any differences. Colleges of health sciences must address these issues because they have a direct impact on the ethics of professionalism.16 Indeed, the results of this study suggest that some future health care professionals may start caring for patients with a "Do as I say, not as I do" mentality. Implicit in health care are ethical principles of patient welfare and safety. How can we justify selfprescribing cognitive-enhancing drugs, regardless of the profession, when we are reluctant to recommend these drugs to our patients for nonmedical use, especially because the long-term side effects are unknown? In conclusion, our findings show the nonmedical misuse of prescription stimulants as cognitive enhancers by 1 in 10 students within three professions on the same campus. The generalizability of these data to other health sciences colleges requires validation. The nonmedical misuse of cognitive enhancers by future health care professionals is an issue that demands further open discussion and investigation. The implications for student learning and advancement, student wellness, and the quality of patient care provided by nonmedical prescription stimulant users require further research as well. Acknowledgments: The authors express their gratitude toward Robert Pack, PhD, for his review of the survey; Nicholas Hagemeier, PharmD, PhD, for his review of this manuscript; Michael Polson for assistance in statistical analysis; and Shane Keene, DHSc, Kathleen Rayman, PhD, and Ken Olive, MD, for their support. Funding/support: This research was supported with a grant from the East Tennessee State University Research Development Committee. Other disclosures: None. Ethical approval: IRB approval was granted by the East Tennessee State University prior to study initiation. References 1 Schwarz A. Risky rise of the good-grade pill. NY Times. June 9, 2012. http://www. nytimes.com/2012/06/10/education/seekingacademic-edge-teenagers-abuse-stimulants. html?pagewanted=all&_r=0. Accessed March 18, 2013. 2 Greely H, Sahakian B, Harris J, et al. Towards responsible use of cognitive-enhancing drugs by the healthy. Nature. 2008;456:702–705. 3 Substance Abuse and Mental Health Services Administration. Results From the 2010 National Survey on Drug Use and Health (NSDUH): Summary of National Findings, NSDUH Series H-41; HHS Publication No. (SMA) 11-4658. Rockville, Md: Substance Abuse and Mental Health Services Administration; 2011. 4 Smith ME, Farah MJ. Are prescription stimulants "smart pills"? The epidemiology and cognitive neuroscience of prescription stimulant use by normal healthy individuals. Psychol Bull. 2011;137:717–741. 5 Kadison R. Getting an edge-Use of stimulants and antidepressants in college. N Engl J Med. 2005;353:1089–1091. 6 Tuttle JP, Scheurich NE, Ranseen J. Prevalence of ADHD diagnosis and nonmedical prescription stimulant use in medical students. Acad Psychiatry. 2010;34:220–223. 7 Lord S, Downs G, Furtaw P, et al. Nonmedical use of prescription opioids and stimulants among student pharmacists. J Am Pharm Assoc (2003). 2009;49:519–528. 8 McNiel AD, Muzzin KB, DeWald JP, et al. The nonmedical use of prescription stimulants among dental and dental hygiene students. J Dent Educ. 2011;75:365–376. 9 Methylphenidate [package insert]. Corona, Calif: Watson Pharm, Inc.; January 2009. http://dailymed.nlm.nih.gov/dailymed/ drugInfo.cfm?id=31144. Accessed March 8, 2012. 10 Adderall [package insert]. Wayne, Pa: Shire US Inc.; June 2005. http://www. accessdata.fda.gov/drugsatfda_docs/ Copyright © by the Association of American Medical Colleges. Unauthorized reproduction of this article is prohibited. Research Report Academic Medicine, Vol. 88, No. 7 / July 2013 5 label/2005/011522s032,033lbl.pdf. Accessed March 18, 2013. 11 Goodman R. Cognitive enhancement, cheating, and accomplishment. Kennedy Inst Ethics J. 2010;20:145–160. 12 Schermer M. Enhancements, easy shortcuts, and the richness of human activities. Bioethics. 2008;22:355–363. 13 Green SP. Cheating. Law Philos. March 2004: 137–185. 14 CDC. Vital signs: Overdoses of prescription opioid pain relievers-United States, 1999–2008. MMWR Morb Mortal Wkly Rep. 2011;60:1487–1492. 15 Larriviere D, Williams MA, Rizzo M, Bonnie RJ; AAN Ethics, Law and Humanities Committee. Responding to requests from adult patients for neuroenhancements: Guidance of the Ethics, Law and Humanities Committee. Neurology. 2009;73:1406– 1412. 16 Beauchamp TL, Childress JF. Principles of Biomedical Ethics. 6th ed. Oxford, UK: Oxford University Press; 2009. Appendix 1 Sample of Representative Survey Questions About Student Misuse of Prescription Drugs as Cognitive Enhancers, East Tennessee State University, 2011 Have you ever been diagnosed with attention deficit hyperactivity disorder (ADHD) or another disorder requiring prescription stimulants (ADD, narcolepsy)? Yes No Have you ever used a prescription stimulant medication which was not originally prescribed for you? Yes No What adverse reactions have you experienced from using a prescription stimulant medication? (Check all that apply) Anxiety Racing heart Sleeplessness Headache Tremor None Lack of appetite Other (please specify) _________________ Please check all the reasons you used a prescription stimulant medication that was not prescribed to you. Improved academic performance Enhanced alertness or energy Getting high/recreational purposes To experiment To make money by selling to other people Other (please specify) _________________


Blockchain Ontologies: OCL and REA

Philosophy

Unified Modeling Language (UML) of Object Management Group, along with Object Constraint Language (OCL), are considered as the best fit for blockchain ontology. OCL is a declarative language that describes the rules applicable to UML models and is part of the UML standard. Initially, OCL was just an extension of the formal specification language for UML. Now, OCL can be used with any meta-model. Enterprise ontology is combined with the business ontology of Resources, Events, Agents (REA) to be used for the content of the change. REA was originally proposed in 1982 by William E. McCarthy as generalized accounting model.
          DOI: 10.13140/RG.2.2.14744.14083

Blockchain Ontologies: OCL and REA Nicolae Sfetcu April 13, 2019 Sfetcu, Nicolae, "Blockchain Ontologies: OCL and REA", SetThings (April 13, 2019), MultiMedia Publishing (ed.), DOI: 10.13140/RG.2.2.14744.14083, URL = https://www.telework.ro/en/blockchain-ontologies-ocl-and-rea/ Email: firstname.lastname@example.com This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International. To view a copy of this license, visit http://creativecommons.org/licenses/by-nd/4.0/. This is a partial translation of Sfetcu, Nicolae, " Filosofie tehnologiei blockchain Ontologii", SetThings (1 februarie 2019), MultiMedia Publishing (ed.), DOI: 10.13140/RG.2.2.25492.35204, ISBN 978-606-033-154-4, URL = https://www.telework.ro/ro/e-books/filosofia-tehnologiei-blockchain-ontologii/ Blockchain Ontologies: OCL and REA Unified Modeling Language (UML) of Object Management Group, along with Object Constraint Language (OCL), (Purvis and Cranefield 1999) are considered by Joost de Kruijff and Hans Weigand as the best fit for blockchain ontology. (de Kruijff and Weigand 2017) OCL is a declarative language that describes the rules applicable to UML models and is part of the UML standard. Initially, OCL was just an extension of the formal specification language for UML. (Object Management Group 2000) Now, OCL can be used with any meta-model. (Object Management Group 2006) OCL is a precise text language that provides constraint expressions and query objects on any specific model or meta-model that cannot be expressed otherwise by schematic notation. OCL is a major component within the new standard recommendation for model transformation. OCL is an analysis and design method resulted from a previous second-generation objectoriented method. OCL includes: 1. a context defining the condition when the statement is valid 2. a property of context features (for example an attribute if the context is a class) 3. an operation handling or qualifying a property; and 4. keywords for conditional expressions. Joost de Kruijff uses OCL for datalogical blockchain ontology (de Kruijff and Weigand 2017) where wallet (that initiates transactions on the blockchain and receives the result), transaction (a request to nodes containing an entry, an amount and an exit, or custom data), and node (an entity to prove or validates and adds transactions to a block using a unique hash, being rewarded for each successful transaction) are most important components of this ontology. In the taxonomy for the ontological structure of a chain, Joost de Kruijff and Hans Weigand distinguish several objects. Langefors distinguished between information (as well as knowledge) and data (as representation), (Goldkuhl 1995) creating a new field in knowledge engineering called infology, for the administration of complicated structures. (Dietz 2006) Blockchain as a "distributed register" is an infological characterization. A registry consists of accounts. Transactions must comply with trading rules. For each transaction the entry is equal to the output. (de Kruijff and Weigand 2017) Essential or business level refers to what is created directly or indirectly through communication. In the Language/Action Perspective, (Narayanan et al. 2016) the key notion in communication is commitment as a social relationship based on the common understanding of what is right and true, a change of social reality. (de Kruijff and Weigand 2017) Enterprise ontology is combined with the business ontology of Resources, Events, Agents (REA) to be used for the content of the change. (Huňka and Zacek 2015) REA was originally proposed in 1982 by William E. McCarthy as generalized accounting model. (McCarthy 1982) REA can add value when shaping current ERP business processes, providing an useful tool for understanding. (Fallon and Polovina 1982) REA sees the accounting system as a virtual representation of the real business. REA is an ontology, with the following real objects included in the model: • Resources: goods, services, or money • Events: business transactions or agreements affecting resources • Agents: people or other human agents (companies, etc.) REA philosophy is based on the idea of reusable design patterns, although REA models are used to describe databases. In ontology-based modeling, conceptualization is the set of ontologies needed to ensure common interpretation of data from one or more databases. If blockchain modeling is a specialized form of inter-enterprise modeling based on ontology results in a blockchain with improved interpretability. (Kim and Laskowski 2016) • For informal or semi-formal ontologies, can lead to improvement of database standards as well as business practices and processes. • For formal ontologies, can help with the formal specification of automatic inference and verification. In the formal ontology approach, the description is very similar to the definition of smart contracts as "pieces of software that represent a business arrangement and execute themselves automatically under pre-determined circumstances." (The Economist 2016) The REA model developed of Bill McCarthy (McCarthy 1982) can be used in accounting. The accounting perspective is appropriate in the context of the blockchain. (de Kruijff and Weigand 2017) Since the blockchain transaction refers to events that trigger changes in economic reality, REA is perceived as more suitable for the baseline analysis than DEMO, which is more appropriate for coordinating these events, which is less important for the blockchain. REA includes the concept of a contract as a package of mutual commitments, through transactions and commitments, where some of the engagements are executed automatically. (Szabo 1997) Referring to REA processes, it can be said that planning (creating a smart contract) and allocation of input resources (output events) materialize on the blockchain, while executing the contract by exchange and conversion (in a smaller extension) triggers output resources (input events) that could go beyond the blockchain, even for physical entities, such as, for example, IoT devices. (de Kruijff and Weigand 2017) Bibliography Dietz, Jan L. G. 2006. Enterprise Ontology: Theory and Methodology. Berlin Heidelberg: Springer-Verlag. //www.springer.com/la/book/9783540291695. Fallon, Richard, and Simon Polovina. 1982. "REA Analysis of SAP HCM; Some Initial Findings." http://ceur-ws.org/Vol-1040/paper4.pdf. Goldkuhl, Goran. 1995. "Information as Action and Communication, The Infological Equation." http://www.vits.org/publikationer/dokument/145.pdf. Huňka, František, and Jaroslav Zacek. 2015. "A New View of REA State Machine." ResearchGate. 2015. https://www.researchgate.net/publication/277942361_A_new_view_of_REA_state_mach ine. Kim, Henry M., and Marek Laskowski. 2016. "Towards an Ontology-Driven Blockchain Design for Supply Chain Provenance." ArXiv:1610.02922 [Cs]. http://arxiv.org/abs/1610.02922. Kruijff, Joost de, and Hans Weigand. 2017. "Understanding the Blockchain Using Enterprise Ontology." Springerprofessional.De. 2017. https://www.springerprofessional.de/en/understanding-the-blockchain-using-enterpriseontology/12328482. McCarthy, William E. 1982. "The REA Accounting Model: A Generalized Framework for Accounting Systems in a Shared Data Environment." The Accounting Review 57 (3): 554–78. https://www.jstor.org/stable/246878. Narayanan, Arvind, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven Goldfeder. 2016. Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press. Object Management Group. 2000. "ObjectConstraintLanguage Specification." http://homepage.divms.uiowa.edu/~tinelli/classes/5810/Spring08/Papers/OCL_1.5.pdf. ---. 2006. "Object Constraint Language OMG Available Specification Version 2.0." http://www2.imm.dtu.dk/courses/02291/files/OCL2.0_06_05_01.pdf. Szabo, Nick. 1997. "Formalizing and Securing Relationships on Public Networks." First Monday 2 (9). https://doi.org/10.5210/fm.v2i9.548. The Economist. 2016. "Not-so-Clever Contracts." Internet Archive. 2016. https://archive.org/details/perma_cc_QNH7-5BJZ.


Incidence, Prevalence, and Hybrid Approaches to Calculating DALYs

Philosophy

When disability-adjusted life years are used to measure the burden of disease on a population in a time interval, they can be calculated in several different ways: from an incidence, pure prevalence, or hybrid perspective. I show that these calculation methods are not equivalent and discuss some of the formal difficulties each method faces. I show that if we donât discount the value of future health, there is a sense in which the choice of calculation method is a mere question of accounting. Such questions can be important, but they donât raise deep theoretical concerns. If we do discount, however, choice of calculation method can change the relative burden attributed to different conditions over time. I conclude by recommending that studies involving disability-adjusted life years be explicit in noting what calculation method is being employed and in explaining why that calculation method has been chosen.

Schroeder Population Health Metrics 2012, 10:19 http://www.pophealthmetrics.com/content/10/1/19COMMENTARY Open AccessIncidence, prevalence, and hybrid approaches to calculating disability-adjusted life years S Andrew Schroeder*Abstract When disability-adjusted life years are used to measure the burden of disease on a population in a time interval, they can be calculated in several different ways: from an incidence, pure prevalence, or hybrid perspective. I show that these calculation methods are not equivalent and discuss some of the formal difficulties each method faces. I show that if we don't discount the value of future health, there is a sense in which the choice of calculation method is a mere question of accounting. Such questions can be important, but they don't raise deep theoretical concerns. If we do discount, however, choice of calculation method can change the relative burden attributed to different conditions over time. I conclude by recommending that studies involving disability-adjusted life years be explicit in noting what calculation method is being employed and in explaining why that calculation method has been chosen. Keywords: Disability-adjusted life years, Incidence perspective, Prevalence perspective, Burden of disease, DiscountingBackground When used to measure the burden of disease on a population during a time interval, disability-adjusted life years (DALYs) can be calculated in different ways – or, as it is usually put, from different perspectives. Studies sometimes choose one method of calculation over the others for reasons of convenience (based, say, on what type of data they have access to) [1], and other times don't even acknowledge the issue, never explicitly saying what calculation method is being used [2-7].a This is, I believe, a serious mistake. Although the point isn't frequently recognized, the different ways of calculating DALYs yield measurements of different quantities; they are not alternative approaches to quantifying the same thing [8]. It is important that a calculation method be chosen with this in mind. In what follows, I'll first explain the three main ways in which DALYs can be calculated, from an incidence, pure prevalence, or hybrid perspective, and I'll show that each method produces a measurement of a different quantity. I'll then discuss the formal features of each perspective and explain the apparent difficulties that eachCorrespondence: firstname.lastname@example.com Department of Philosophy, Claremont McKenna College, 850 Columbia Avenue, Claremont, CA 91711-6420, USA © 2012 Schroeder; licensee BioMed Central Lt Commons Attribution License (http://creativec reproduction in any medium, provided the orfaces. I'll show how this issue interacts with another important question in health measurement: whether the value of future health states should be discounted. I'll show that if we don't discount, there is a sense in which the perspective issue is a "mere" question of accounting which raises no deep conceptual concerns. On the other hand, if we do discount, then the choice of perspective is crucial. Different choices will result in changes to the relative burden attributed to different diseases or risk factors. I'll conclude with a few recommendations.DALYs and time All DALYs are calculated by adding YLLs (years of life lost to premature mortality) to YLDs (years of life lived with disability, multiplied by a disability weight [DW] representing the severity of the condition).b This calculation is relatively simple when DALYs are not linked to a time interval but instead are linked to a well-defined population. If we want, for example, to know how many DALYs were lost by the 1890 birth cohort, we can simply count how many years prematurely the members of that cohort died and then determine the duration of disability they experienced, multiplied by severity. Many DALY measurements, however, are linked to time. Instead of looking at how many DALYs have been lost to lungd. This is an Open Access article distributed under the terms of the Creative ommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and iginal work is properly cited. Alice Bob Carol David Edith Frank 0 1 2 3 4 5 6 7 8 X X X X Figure 1 Incidence DALYs. To calculate I-DALYs for year #2, look for events – color changes – in year #2, and then add up the future stream of ill health connected to those events. A sequela-based measurement (see below) will count only Frank's two years of disability; a pathology-based measurement will also count his 2+ years of premature mortality, but will not count Carol's four years of premature mortality (unless her death was unrelated to her disability). Schroeder Population Health Metrics 2012, 10:19 Page 2 of 7 http://www.pophealthmetrics.com/content/10/1/19cancer, we often want to know how many DALYs were lost to lung cancer in 2004. This kind of measurement is potentially very useful, since it allows us to track changes over time, monitor the impact of policy interventions, and the like. But it also raises a number of problems.c Suppose, then, that we want to determine the number of DALYs lost in 2004 to lung cancer. We know that all DALYs are the sum of YLLs and YLDs. To determine the number of YLLs due to lung cancer in 2004, it seems reasonable to look at all the lung cancer deaths in 2004 and then to count how many years premature each death was. So, if Sally dies from lung cancer in 2004 at age 50, then (assuming a life expectancy of 80 years) that means 30 years of life were lost.d To determine the number of YLDs due to lung cancer in 2004, it seems sensible to look at how many people suffered as a result of lung cancer that year, and then to multiply those person-years by the appropriate DW. So, if Jim experiences mild (DW=0.2) disability throughout 2004 on account of his lung cancer, then 0.2 DALYs were lost on account of his lung cancer in 2004. This is, I think, the most intuitive way to think about YLLs and YLDs in 2004, but note that the method – the perspective – adopted for each component was quite different. We calculated YLLs by identifying a collection of events (deaths) that took place in our time period and determining the stream of future health losses associated with those events. We calculated YLDs, on the other hand, by looking at the states experienced in our time period and adding that time up. Theoretically, it doesn't seem to make much sense to just add these results together to get the total DALYs lost to lung cancer in 2004, since they are based on very different methods [8,9]. The resulting output wouldn't measure anything easily describable. It could best be summarized as the future years of life lost to premature mortality due to deaths in 2004 plus the amount of disability experienced in 2004. What, then, can we do to avoid that kind of chimeric measure? The most common strategy is to change the YLD calculation method to bring it in line with that used to count YLLs. So, instead of looking at the amount of disability experienced in 2004, we instead pick some type of event and look at the future stream of disability connected with events of that type occurring in 2004. We might, for example, look at all disabling events, like we looked at deaths. If I go permanently blind (DW= 0.6) in 2004 at age 30, we could attribute 50 years x 0.6 = 30 DALYs to 2004's YLD count. Your blindness, however, which struck in 2003, wouldn't be counted at all; its full impact would have been registered in 2003's YLD count. This approach is called an incidence perspective, and it is the method most commonly used to calculate DALYs. With a few qualifications to be discussed below,I-DALYs (incidence DALYs) for period T measure the stream of lost health connected with events in T. The second apparent solution to our original puzzle is to take the opposite route and to change the method for calculating YLLs to bring it in line with that used when calculate YLDs [10]. Rather than looking at events that happened in 2004, this would involve counting ill health experienced in 2004. It's very natural, as we saw, to do this for disability, but it doesn't make sense in the case of death. No one "experiences" being dead. What we can do, though, is add up the health that would have been experienced in 2004, but for injury, disease, and so forth. That is, we can calculate YLLs for 2004 by counting the number of people who died in the past, whose natural lifespan included 2004. So, the lung cancer death of a 50 year-old in 2000 would count as one YLL for each year from 2001 through 2030, since the deceased would have been alive in each of those years had she not contracted lung cancer and instead lived out her natural lifespan. This approach to calculating DALYs doesn't have a name, since (to my knowledge) it has never been used. I'll call it a pure prevalence perspective and call DALYs calculated this way PP-DALYs. Roughly, PPDALYs for period T measure the amount of additional health that would have been experienced in T, but for injury, disease, and so forth. Finally, the third response to our original puzzle has been to ignore it – when calculate YLLs from an incidence perspective and YLDs from a prevalence perspective and then to simply add the results.e This approach, which is less common than the incidence approach, is usually described in the literature as taking a prevalence perspective, but for reasons which I hope are clear, I will call it a hybrid perspective and call the resultant DALYs H-DALYs. As mentioned earlier, H-DALYs don't seem to measure any nondisjunctively describable quantity. Visually, all of this is represented in Figures 1, 2 and 3, where green represents time lived in good health, yellow Alice Bob Carol David Edith Frank 0 1 2 3 4 5 6 7 8 Figure 2 Pure prevalence DALYs. To calculate PP-DALYs for year #2, add up all the yellow (disability) and red (premature mortality) that occur in year #2 on the chart. Schroeder Population Health Metrics 2012, 10:19 Page 3 of 7 http://www.pophealthmetrics.com/content/10/1/19represents time lived in a particular disabled state, and red represents time lost to premature mortality. Conceptual choices and problems Having briefly described the three main approaches to calculating DALYs, I'll now discuss each in more detail, identifying some of the choices and problems each perspective faces. I-DALYs DALYs are most commonly calculated from an incidence perspective. This involves selecting some type of event, identifying the events of that type that occur in the target time period, and then adding up the DALYs that are connected to those events. Calculating I-DALYs therefore requires (1) choosing a type of event and then (2) specifying how lost health is connected to events. (These choices are rarely made explicit in the literature.) In order not to undercount lost health, all lost health should be connected to an appropriate event; but in order not to overcount lost health, all lost health should be connected to only one event. Here are four possibilities: 1. Individuation by sequelae and death: "events" are the onset of a sequela or a death; we connect all health lost to a sequela or to death to its onset.f 2. Individuation by pathology: "events" are the onset of a pathology; we connect all health lost to a pathology to its onset.Alice Bob Carol David Edith Frank 0 1 2 3 4 5 6 7 8 X X Figure 3 Hybrid DALYs. To calculate H-DALYs for year #2, add up all the yellow (disability) that occurs in year #2, and then look for any deaths in year #2, adding up the (future) years of life lost to those deaths.3. Individuation by pathology and death: "events" are the onset of a pathology or a death; we connect all health lost to disability to the onset of its underlying pathology, and we connect all health lost to death to death. 4. Individuation by death: "events" are the death of an individual; we connect all health lost by an individual to her death.g Suppose that Jane begins smoking in 1950. She contracts chronic obstructive pulmonary disease in 1980, which gives her six years of mild disability (DW=0.2), followed by two years of serious disability (DW=0.5), followed by death in 1988 at age 60. If we individuate by sequelae and death and assume a life expectancy of 80 years, we'll assign 6 × 0.2 = 1.2 DALYs to 1980; 2 × 0.5 = 1 DALY to 1986; and 20 × 1= 20 DALYs to 1988. If we individuate by pathology, we'll assign all 22.2 DALYs to 1980. If we individuate by pathology and death, we'll assign 2.2 DALYs to 1980 and 20 DALYs to 1988. If we individuate by death, all 22.2 DALYs would be assigned to 1988. The first option is the method officially used in the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study [11]. Many studies claim to be using the third option – while at the same time saying they are following GBD methodology [12].h I mention the second option because it seems more internally consistent than the third and is used for calculating Healthy Life Years (HeaLYs), a measure related to the DALY [13]. The fourth method has never been used and probably never will be. I mention it simply to show that there are many possible ways of calculating I-DALYs. Some less obvious ways may prove to be useful. In the above scenario, for example, we might try to individuate by primary cause or risk factor, assigning the 22.2 DALYs to Jane's decision to smoke in 1950. Given, then, the many different possible versions of an incidence approach, it is important to be clear on the method of calculating I-DALYs being used. It is also important to resist the temptation to describe I-DALYs as measuring the amount of ill health lost due to causes of a particular type in a time period. This may be true for some methods of event individuation, but it is certainly not true for all of them. (Individuation by pathology comes the closest.) It is therefore more accurate to describe I-DALYs as measuring the amount of lost health connected with events in a given time period and then to explicitly define the connection relation. Finally, note that in no intuitive sense are I-DALYs (of whatever type) a measure of the health of a population at a time or of the burden of disease on a population at a time. Suppose, for example, that last year we were struck by a massive epidemic that caused permanent Schroeder Population Health Metrics 2012, 10:19 Page 4 of 7 http://www.pophealthmetrics.com/content/10/1/19blindness, paraplegia, and chronic pain in the entire population. If there are no new health problems (incident pathologies, sequelae, or deaths) this year, then zero I-DALYs would be attributed to this year. But if someone asked how healthy we were this year or whether we experienced any burden resulting from disease, we would surely answer that we were not at all healthy and experienced a huge burden, since we're blind, paralyzed, and in chronic pain. Call this the polio problem, since for many years in many countries polio would have registered zero I-DALYs, despite having left many people with serious disabilities. PP-DALYs Pure prevalence DALYs calculate YLDs by looking at the gap between health actually experienced in some time period and full health, and they calculate YLLs by counting the number of people who died in the past but who would have been alive in the time period had they lived a normal lifespan. PP-DALYs are in a sense simpler than I-DALYs, in that they don't require defining things like "events" and the connection relation. It is tempting to say that they measure the amount of additional health that would have been experienced in the time period but for pathology, but there are two reasons why that isn't quite right. First, YLLs are calculated based on a somewhat arbitrary choice of target life expectancy. If we truly had no ill health, people would live much longer than 80 years. (In fact, if a fatal condition is considered ill health, then absent ill health no one would ever die.) Second, PP-DALYs are not designed to make this kind of counterfactual assessment. If I become infertile, that infertility will register a (small) number of DALYs each year I experience it. But the health that would have been experienced by the children I would have had, had I been fertile, will not register on a PP-DALY measure. So, it is more accurate to say that PP-DALYs measure the amount of additional health that would have been experienced in a given time period by past or present members of some population, had they lived to an arbitrary age in good health. As with I-DALYs, this is not an intuitive measure of the health of a population at a time. Elvis Presley died in 1977 at age 42. He, therefore, would still count for one YLL this year. But, intuitively, it does not seem that we as a population are in poorer health now because Elvis died 35 years ago. Call this the Elvis problem. H-DALYs Hybrid DALYs are calculated by adding I-YLLs to PYLDs. As such, H-DALYs avoid both the polio and Elvis problems. (The polio problem arises due to the use of IYLDs and the Elvis problem due to the use of P-YLLs.) They also largely sidestep the difficulty facing I-DALYsin choosing "events." Since only YLLs are calculated from an incidence perspective, it seems natural to select death as the event in question and then to connect all years of life lost to the moment of death. Finally, HDALYs are a true period measure: all the data needed to calculate H-DALYs can be measured in the time period in question. (I-DALYs require a projection of the future duration of disability, and PP-DALYs require knowledge of deaths that occurred prior to the time period in question.) These are the virtues of H-DALYs, and they are considerable. Unfortunately, there are also serious problems. As we've seen, H-DALYs don't measure any intuitively describable quantity, and they therefore appear to lack theoretical justification. I believe, however, that H-DALYs may be justifiable as our elusive measure – or, more accurately, index – of population health at a time, a role we saw that neither Inor PP-DALYs could plausibly fill. Some educational assessment indices for schools include both a component connected to the academic performance of students (e.g., standardized test scores) and a component that reflects the drop-out rate [14].i The thought is that a school has in some sense failed in its mission if its students perform badly on (well-designed) tests or if they drop out. A school's job is both to increase test scores and to keep students in the system. Similarly, we might think that we, as a population, are doing poorly with respect to health if our members are living with disabilities or if we are allowing our members to "drop out" of the population by dying. The job of our health system is both to decrease disability and to keep people alive. The former component, I suggest, may plausibly be represented with P-YLDs, and the latter with I-YLLs. (I argue for this claim more extensively in an unpublished manuscript, "Measuring Health and the Problem of Changing Populations.") DALYs, accounting, and discounting Given, then, that we have (at least) three different ways to calculate the DALYs lost for a given time period, which one should we use? There is one sense in which the decision might not seem like a theoretically significant one. As we saw, these issues only arise when we try to assign DALYs to time periods. If we ask, for example, how many DALYs John lost to ill health, there is a simple answer. Similarly, if we ask how many DALYs members of the Boston Red Sox lost to ill health, there is a simple answer. Things only become complicated when we ask how many DALYs members of the Boston Red Sox lost to ill health in 2010. Our problem, then, is really just one of accounting. We have a set number of DALYs that have been lost, and we want to assign those DALYs to different time periods. Each of the three methods we've looked at provides an internally consistent way of Schroeder Population Health Metrics 2012, 10:19 Page 5 of 7 http://www.pophealthmetrics.com/content/10/1/19doing that. There aren't any deep metaphysical issues raised when an accountant decides whether to calculate asset depreciation annually versus allocating the entire loss to the year of sale. Similarly, there is no principled reason we can't allocate DALYs to time periods in any number of ways, so long as we do so consistently. That said, there are two reasons why the choice of accounting has at least practical importance. First, the choice of perspective – the accounting method – may make a difference in how certain diseases "look" over time. After a successful vaccine has been developed, certain illnesses will no longer show up in I-DALY measurements but will continue to register in Hand PPDALY measurements. Certain cyclical diseases with lasting effects may appear only to be sporadic problems from an incidence perspective while appearing to be chronic problems from a pure prevalence or hybrid perspective. Hand PP-DALYs may appear to underestimate the importance of a new, chronic health problem, if most of its effects will occur in the future. Of course, each of these problems can be corrected for by taking a more comprehensive view. (We can correct for the last problem, for example, by also looking at predicted future Hand PP-DALYs.) But in the real world we know that such care won't always be taken, and so there is potentially some practical importance in the choice of perspective, because of the different "shape" or trajectory it can give to different health problems. Second, choice of perspective interacts with discounting in a crucial way. The practical matters I mentioned above concern the shape of different DALY measurements. Calculated one way, a disease may show huge periodic "spikes" of DALYs lost; calculated another, it may register a relatively constant drain on the population. But in the end, the total number of DALYs lost over time will be the same, which is why the issues can easily be corrected for by more carefully examining the measurements and taking a broader view. If, however, we discount the value of future health, this need not be the case. Suppose condition A affects 100 people per year and gives each person exactly one year of serious disability (DW=0.5). Condition B, which has two incident cases each year, causes 50 years of equally serious disability. If B's incidence rate has been constant, it will eventually have the same prevalence as A – 100 people will have each condition at any given time. From a hybrid or pure prevalence perspective, both A and B will register 100 ×Table 1 The impact of discounting and perspective choice on Condition Incidence Duration Prevalence Hor PP-DALYs (w/or w/ou A 100 1 100 50 B 2 50 100 500.5 = 50 DALYs per year, every year. This will be true whether or not we discount the value of future health, since both Hand PP-DALYs calculate YLDs by looking at the health loss experienced this year. When we calculate I-DALYs, however, things change. Whether or not we discount, condition A will register 100 cases × 1 year × 0.5 = 50 DALYs each year. If we don't discount, condition B will register 2 cases × 50 years × 0.5 = 50 DALYs each year. If we do discount, however, we'll get a much lower number for B, since the heath loss in the future will count for less. (Using a low 3% discount rate, B will register 25.9 I-DALYs each year.) This means that if we don't discount, then H-, PP-, and I-DALYs will all measure A and B as equally serious health problems – 50 DALYs per year, every year. If we do discount, Hand PPDALYs will still count A and B equally, but I-DALYs will register A as a much more serious health problem than B (50 vs. 25.9 DALYs). (See Table 1). So, if we discount the value of future health, then the choice of perspective becomes crucial – it can have a huge effect on the relative burden assigned to different health problems, and this difference will not wash out over time. The choice of perspective here no longer seems like mere accounting.j Conclusions I've explained the many different ways to calculate DALYs – from a pure prevalence, hybrid, or incidence perspective (the last of which has several possible forms) – and I've explained some of the conceptual choices and problems that arise for each method. Returning to the question that opened the last section: which is the right one to use? Which perspective should we take when measuring health? I think the answer, unsurprisingly, is that since each measures a different quantity, each is useful in different situations. If we are interested, for example, in estimating the impact of lung cancer on this year's economic productivity, PP-DALYs are likely a useful data point. H-DALYs may be a good choice when evaluating the performance of a health care system, and I-DALYs might be the best option if we're deciding how much money to allocate to different vaccine programs. The important point to keep in mind is that PP-, H-, and I-DALYs measure different things. That is, it is not the case that we have a single quantity to measure, population health in 2004 or the burden of disease in 2004, for which we have three candidate calculation methods. Rather, we have three candidate quantities thatthe measured burden for two sample conditions t discounting) I-DALYs (w/out discounting) I-DALYs (3% discount) 50 50 50 25.9 Schroeder Population Health Metrics 2012, 10:19 Page 6 of 7 http://www.pophealthmetrics.com/content/10/1/19we could measure. When undertaking a measurement of health, the first question needs to be: what, exactly, do I want to measure? A careful answer to this question will much of the time suggest the proper calculation method. Two further recommendations are in order. First, those measuring health need to clearly describe their calculation method. As has been noted, many studies do not explain (much less justify) what calculation method has been chosen, and those that do frequently relegate the explanation to a footnote or appendix. For studies adopting an incidence approach, it is very rare to find an explicit mention of what "events" are being counted and of how lost health is tied to those events. These choices matter. It is important for them to be made clear so that results can be properly interpreted and to ensure that only properly comparable results are compared. Second, I think it is important to speak more clearly about DALYs and what they do and don't measure. As we noted above, neither I-DALYs nor PP-DALYs are plausible candidates for measuring population health at a time – recall the polio and Elvis problems – and so we shouldn't describe them as such. Rather, we should explicitly describe I-DALYs for time period T as measuring the health loss connected with events (of whatever type) occurring in T – or, somewhat more loosely, as measuring the total health loss tied to new health problems in T. And we should describe PP-DALYs for T as, loosely, measuring the amount of additional health that would have been experienced in T but for injury, disease, and so forth. What about H-DALYs? I suggested that they can be described as an index representing overall population health for the period. This clarity of description has a cost, of course. It is much easier, and more likely to resonate with the public and with policymakers, to simply describe DALYs as measuring population health or the burden of disease for some period of time. But if that simplicity comes at the price of misunderstanding, I question whether it is worth it. Endnotes aThough none of these articles explicitly indicate which calculation method is being used, in some (but not all) cases it can be inferred from the paper. bDisability weights raise a number of important issues which I will set aside here. In particular, there is a question about what a disability weight is supposed to represent – a quantity of health, the value of health states, the well-being associated with health states, etc. The resolution of this issue will affect much of what I say below, about what each type of DALY seeks to measure, in ways which should be straightforward. cFor the remainder of the paper, whenever I speak of DALYs, assume that I mean DALYs-for-time-period-T. In particular, many of the recommendations I make atthe end of the paper do not necessarily apply to DALY measurements that are not restricted to a time period. dThe choice of life expectancy is another important, difficult, and potentially problematic issue. But I will ignore it here. eThere is, logically, a fourth possibility: adding P-YLLs to I-YLDs. I know of no one who has even mentioned the possibility, though, and I can't think of any context in which it would be useful. fThere is a sense in which we could simply call this "individuation by sequelae," where we consider death to be a sequela. Since it is standard practice, however, to reserve "sequela" for nonfatal conditions, I have chosen the slightly longer formulation here. gIn each of these cases, further work needs to be done properly operationalize it. What distinguishes one sequela from another? (Is moderate back pain a different sequela than severe back pain?) What counts as the onset of a pathology? (Is it the initially asymptomatic cancerous mutation or the appearance of symptoms?) What counts as health lost to a pathology? (If I accidentally drink from your glass because I'm blind and as a result catch a cold, should the resultant health loss be attributed to my blindness?) These are important questions, but I'll set them aside. hEven the GBD Study itself makes this mistake. The 2004 update describes YLD as "years lost due to disability. . .for incident cases of the disease or injury" [12]. Theo Vos (personal communication) has explained to me that in the GBD Study, as well as in some national burden of disease studies, an inconsistent mix of individuation by sequelae and death and individuation by pathology and death has been used. For example, in past GBD studies foot amputation from diabetes was treated as a separate sequela, with YLD assigned to the time of amputation. But YLD owing to different sequelae arising from cancer were assigned to the original onset of cancer. iCalifornia's Academic Performance Index (API) originally included only components relating to student achievement, creating perverse incentives for schools to encourage poor performers to drop out or transfer. Recent legislation has mandated that the API to be revised to include a component reflecting a school's retention rate [14]. jDan Brock (personal communication) has raised the question of whether these observations are relevant to the much-debated issue of whether or not we should discount the value of future health. There certainly is a sense in which the perspective issue would be simpler if we did not discount. Then, the choice of perspective would really be one of accounting – it wouldn't affect the relative burden over time attributed to different conditions. So, dispensing with discounting would be a way Schroeder Population Health Metrics 2012, 10:19 Page 7 of 7 http://www.pophealthmetrics.com/content/10/1/19of making the perspective issue much less pressing, and that might be thought to count in favor of doing so. I suspect, however, that this consideration by itself wouldn't sway many advocates of discounting, since it is already well known that discounting will shift the relative burden attributed to different conditions. (The cost effectiveness of vaccination programs and programs to alleviate the effects of climate change, for example, are notoriously sensitive to the choice of discount rate.) I think the relevance of the perspective issue to the question of discounting, though, is worthy of further investigation. Abbreviations DALY, Disability-adjusted life year; DW, Disability weight; GBD, Global Burden of Disease; H-DALY, Disability-adjusted life years calculated from a hybrid perspective; I-DALY, Disability-adjusted life years calculated from an incidence perspective; PP-DALY, Disability-adjusted life years calculated from a pure prevalence perspective; YLD, Years of life lived with disability (multiplied by disability weight); YLL, Years of life lost. Competing interests The author declares that they have no competing interests. Author's information The author is a philosopher who works on issues in ethics and the philosophy of science connected to health measurement. Acknowledgments I thank Dan Brock, Daniel Hausman, Christopher Hitchcock, Kristi Olson, Joshua Salomon, Theo Vos, and the participants at the Critical Ethical Choices for DALYs meeting for helpful discussion on these issues and for comments on an earlier draft of this paper. Received: 30 July 2012 Accepted: 6 September 2012 Published: 12 September 2012 References 1. Ding D, Hong Z, Wang W, Wu J, De Boer H, Prilipko L, Sander J: Assessing the disease burden due to epilepsy by disability adjusted life year in rural China. Epilepsia 2006, 47(12):2032–2037. 2. Budke C, Jiamin Q, Zinsstag J, Qian W, Torgerson P: Use of disability adjusted life years in the estimation of the disease burden of echinococcosis for a high endemic region of the Tibetan plateau. Am J Trop Med Hyg 2004, 71(1):56–64. 3. Chapman G, Hansen K, Jelsma J, Ndhlovu C, Piotti B, Byskov J, Vos T: The burden of disease in Zimbabwe in 1997 as measured by disabilityadjusted life years lost. Trop Med Int Health 2006, 11(5):660–671. 4. Ditsuwan V, Veerman L, Barendregt J, Bertram M, Vos T: The national burden of road traffic injuries in Thailand. Population Health Metrics 2011, 9(2):1–9. 5. Dodhia H, Phillips K: Measuring burden of disease in two inner London boroughs using Disability Adjusted Life Years. J Public Health 2008, 30(3):313–321. 6. Fèvre E, Wissman B, Welburn S, Lutumba P: The Burden of Human African Trypanosomiasis. PLoS Negl Trop Dis 2008, 2(12):1–7. 7. Johnell O, Kanis J: An estimate of the worldwide prevalence, mortality and disability associated with hip fracture. Osteoporosis Int 2004, 15:897–902. 8. Murray C: Rethinking DALYs. In The Global Burden of Disease. Edited by Murray C, Lopez A. Geneva: WHO Press; 1996:1–98. 9. Begg S, Vos T, Barker B, Stevenson C, Stanley L, Lopez A: The burden of disease and injury in Australia 2003. Canberra: AIHW; 2007. Cat. no. PHE 82. 10. Murray C, Salomon J, Mathers C: A critical examination of summary measures of population health. In Summary Measures of Population Health. Edited by Murray C, Salomon J, Mathers C, Lopez A. Geneva: WHO Press; 2002:13–40.11. Murray C, Acharya A: Understanding DALYs. J Health Econ 1997, 16:703–730. 12. World Health Organization: The Global Burden of Disease: 2004 Update. Geneva: WHO Press; 2008. 13. Hyder A, Rotllant G, Morrow R: Measuring the Burden of Disease: Healthy Life-Years. Am J Public Health 1998, 88(2):196–202. 14. Editorial: How to keep kids in school. San Francisco Chronicle 2007, Sep 17. doi:10.1186/1478-7954-10-19 Cite this article as: Schroeder: Incidence, prevalence, and hybrid approaches to calculating disability-adjusted life years. Population Health Metrics 2012 10:19.Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit


Review - Bare Facts And Naked Truths

Philosophy

Bare Facts and Naked Truths: A New Correspondence Theory of Truth, by George Englebretsen, Ashgate Publishing, 2006, 194 pp. Reviewed by Bo R. Meinertsen, PhD Published in Metapsychology Online Reviews, Aug 1st 2006 (Volume 10, Issue 31) 'To say of what is that it is not, or of what is not that it is, is false, while to say of what is that it is, and of what is not that it is not, is true'. This famous definition of truth by Aristotle captures the essence of what is known as the 'correspondence theory of truth'. Truth consists in correspondence with the world; something-a belief, sentence, statement or 'proposition' (what a sentence 'expresses')-is true if, and only if, it corresponds with how the world is. This is our pre-theoretical notion of truth. In analytic philosophy, however, where theories of truth are one of the major (and often quite technical) issues, it remains very controversial how to formulate a viable correspondence theory of truth. Indeed, it is often considered so fraught with difficulties as to be untenable. This has serious consequences: for instance, the correspondence theory is considered to be essential to realist views of a discourse, such that if 'rumor' spreads that no such theory is forthcoming, the popularity of various forms of non-realism and relativism in certain important discourses, e.g. politics and the humanities, will probably increase. However, in Bare Facts and Naked Truths, George Englebretsen sets out to present a new version of the correspondence theory that avoids these difficulties. Such a theory requires accounts of three elements: what are the things that are true, the 'truthbearers'; the relation of correspondence; and the entities in the world that the truth-bearers correspond to, the 'truth-makers'. On the correspondence theory, truth-makers are usually called 'facts'. A truth-bearer is true if and only if it corresponds to a fact. Now, there is a trivial sense of 'fact' in which this is obviously correct: the statement that John is in love with Mary, for instance, is true if and only if it is a fact that John is in love with Mary. A fact in this sense goes hand in hand with the thatclause: 'it is a fact that...' However, it is generally agreed among philosophers that the correspondence theory needs a substantial notion of facts, a substantial view of the objective and non-linguistic correlate of truths, the truth-makers. What are facts in this sense? (Note that the word 'fact' is thus profoundly ambiguous). That is, what are facts, metaphysically speaking? Answering this is normally considered the biggest challenge for the correspondence theory. This, is should be stressed, does not in any way mean that accounting for the two other elements, truth-bearers and the relation of correspondence-and how they link up with other philosophical notions like meaning and knowledge-is not important too. Englebretsen does attempt to account for them-indeed a significant proportion of the book is devoted to it (including attempts at solving highly technical problems in the area, such as the Liar Paradox: the paradox resulting from sentences such as 'this sentence is false'). Having come from a strong background in logic and semantics, he deals with these issues most ably. However, in my view the main sense in which his correspondence theory is new lies in its attempts at providing a novel metaphysical view of facts. The first two chapters of the book make up its Part One. In the first chapter, Englebretsen provides an overview of important views on truth in the history of Western philosophy; in the second, the focus is on theories of truth in analytic philosophy from Frege to Russell to Tarski to Davidson. These chapters manifest most clearly a salient feature of the book as a whole. Englebretsen is very learned and his writing is quite dense. Thus, in little space, he invokes and discusses a vast number of often very different issues and philosophers. This means that, although he strictly speaking does not presuppose that much special knowledge on the side of his readers, only professional philosophers and their graduate students with a fair amount of such knowledge are likely to feel at ease with reading this book. The classic debate on truth in 1950 between Peter Strawson and John Austin is discussed in both of the two first chapters, creating a clear link between them. While Strawson attacked all three elements of a correspondence theory of truth-truth-bearers, the relation of correspondence, as well as facts-it was in particular the last-mentioned that he objected to. Strawson insisted that 'fact' is, as he put it, 'wedded' to 'that'-clauses, and related to this, that facts are what statements, when true, state, not what they are about. If, for instance, 'the cat is on the mat' is a truth (a true sentence, statement or proposition), then there is no fact 'in the world'-no fact in a metaphysical sense-to which the truth corresponds; there is no entity in the world other than the cat and the mat. Austin argued that Strawson's view was, as he put it, 'unfair to facts', though in a way that contemporary philosophers rightly consider very outdated. Englebretsen, by contrast, with his new correspondence theory, is most interesting. This theory is derived from the unique logic and semantics, the so-called 'terminism', due to Englebretsen himself and his long-time collaborator, the logician and philosopher Fred Sommers. Very briefly, on terminism. Sentences are construed as terms (nouns, predicates etc.). Like any term on this theory, a sentence 'denotes' one or more individuals; and a sentence is used relative to a 'domain of discourse'. Any such domain is a 'totality to individuals'. Englebretsen now makes use of this in his theory of truth. Taking the primary truth-bearers to be propositions (what sentences, when used in the right way, express), he maintains that 'any true proposition is expressed by a sentence denoting the domain relative to which it is expressed'. This sounds technical, but it is very simple to state when the primary truth-bearer, a proposition, is true. It is true when, and only when, it corresponds to a fact! It is at this point that Englebretsen believes he possesses the theoretical underpinning to provide a novel definition of 'fact', in the metaphysical sense. However, what is surprising is that while, unlike Strawson, he is a fan of metaphysical facts, he concurs with him that such facts are not in the world. Instead, they are of the world. A fact is, Englebretsen maintains, 'a particular characteristic of the domain denoted by the sentence expressing it'. This characteristic he calls a 'constitutive property'. The world itself is a domain, the most inclusive there is: the totality of all individuals. In a word, a fact is a constitutive property of the world. In short, 'Facts are not constituents of the world, they are not in the world; facts are constitutive properties of the world' (p. 105). If this view is tenable, it accommodates Strawson's sceptical intuitions about metaphysical facts. One of these intuitions was the view that when the cat is on the mat there is in the world the cat and the mat, but no third entity called a 'fact'. The problem with this view for a correspondence theory is that this seems to remove the entire third element of the theory: the truth-makers, the objective, non-linguistic correlates of truths, facts in the metaphysical sense. However, if Englebretsen is right, then Strawson and Austin shared a false presupposition: metaphysical facts do not have to be in the world. Indeed, they are not - so it was little wonder that Strawson did not find any. They are of the world. Unfortunately, however, to most contemporary philosophers working in metaphysics, it makes no literal sense to say that a fact is a 'property of the world' or any other kind of property. Properties (being green, being a desk, etc.) can of course be 'of' something, and they are so when they are instantiated (exemplified, possessed, had) by that thing. But facts cannot. For no fact is a property. Facts and properties are categorially different entities, just like, say, chairs, desks, plants, animals and molecules are. Thus, to identify a fact with a certain kind of property (a 'constitutive property') is as wrong as it would be to identify, say, my desk with the property of being a desk. This notion of property at issue is a 'thick', metaphysical one, underpinned by particular metaphysical views. Someone might therefore object that my criticism of Englebretsen is unfair: is the notion of a property he uses not that of a weaker, more pre-theoretical sense, in which it can be used interchangeably with the notion of a 'feature' or a 'characteristic' (which are notions that do not carry the same kind of ontological commitment as that of 'property', and which you therefore are free to identify with facts, or any other kind of thing)? It is true that there is such a notion of a property. And it may seem that it is the one Englebretsen uses here. But it is not the only one. He makes quite clear that he endorses properties in the thick sense of the contemporary Australian philosopher David Armstrong (p. 89). It will not be of any use to Englebretsen to give up the thick sense of property. While this might make his correspondence theory coherent, it will also make it metaphysically far less interesting. And as we saw, it is precisely an account of metaphysical facts that the correspondence theory so urgently needs. However, this criticism of Englebretsen's metaphysics, or lack of it, does not deduct from the fact that he has provided us with a learned presentation of a new, original correspondence theory of truth, attempting to deal with all three of its elements: truth-bearers, the relation of correspondence and truth-makers.