BACKGROUND:The most common treatment for major depressive disorder (MDD) is antidepressant medication (ADM). Results are reported on frequency of ADM use, reasons for use, and perceived effectiveness of use in general population surveys across 20 countries. METHODS:Face-to-face interviews with community samples totaling n = 49 919 respondents in the World Health Organization (WHO) World Mental Health (WMH) Surveys asked about ADM use anytime in the prior 12 months in conjunction with validated fully structured diagnostic interviews. Treatment questions were administered independently of diagnoses and asked of all respondents. RESULTS:3.1% of respondents reported ADM use within the past 12 months. In high-income countries (HICs), depression (49.2%) and anxiety (36.4%) were the most common reasons for use. In low- and middle-income countries (LMICs), depression (38.4%) and sleep problems (31.9%) were the most common reasons for use. Prevalence of use was 2-4 times as high in HICs as LMICs across all examined diagnoses. Newer ADMs were proportionally used more often in HICs than LMICs. Across all conditions, ADMs were reported as very effective by 58.8% of users and somewhat effective by an additional 28.3% of users, with both proportions higher in LMICs than HICs. Neither ADM class nor reason for use was a significant predictor of perceived effectiveness. CONCLUSION:ADMs are in widespread use and for a variety of conditions including but going beyond depression and anxiety. In a general population sample from multiple LMICs and HICs, ADMs were widely perceived to be either very or somewhat effective by the people who use them.

Objective This paper introduces a novel method to evaluate the local impact of behavioral scenarios on disease prevalence and burden with representative individual level data while ensuring that the model is in agreement with the qualitative patterns of global relative risk (RR) estimates. The method is used to estimate the impact of behavioral scenarios on the burden of disease due to ischemic heart disease (IHD) and diabetes in the Turkish adult population. Methods Disease specific Hierarchical Bayes (HB) models estimate the individual disease probability as a function of behaviors, demographics, socio-economics and other controls, where constraints are specified based on the global RR estimates. The simulator combines the counterfactual disease probability estimates with disability adjusted life year (DALY)-perprevalent-case estimates and rolls up to the targeted population level, thus reflecting the local joint distribution of exposures. The Global Burden of Disease (GBD) 2016 study meta-analysis results guide the analysis of the Turkish National Health Surveys (2008 to 2016) that contain more than 90 thousand observations. Findings The proposed Qualitative Informative HB models do not sacrifice predictive accuracy versus benchmarks (logistic regression and HB models with non-informative and numerical informative priors) while agreeing with the global patterns. In the Turkish adult population, Increasing Physical Activity reduces the DALYs substantially for both IHD by 8.6% (6.4% 11.2%), and Diabetes by 8.1% (5.8% 10.6%), (90% uncertainty intervals). Eliminating Smoking and Second-hand Smoke predominantly decreases the IHD burden 13.1% (10.4% 15.8%) versus Diabetes 2.8% (1.1% 4.6%). Increasing Fruit and Vegetable Consumption, on the other hand, reduces IHD DALYs by 4.1% (2.8% 5.4%) while not improving the Diabetes burden 0.1% (0% 0.1%). Conclusion While the national RR estimates are in qualitative agreement with the global patterns, the scenario impact estimates are markedly different than the attributable risk estimates from the GBD analysis and allow evaluation of practical scenarios with multiple behaviors.

The fifth edition of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the eleventh edition of the World Health Organization’s International Classification of Diseases (ICD-11) chapter on mental and behavioural disorders represent interrelated milestones in the understanding of psychiatric (mental) disorders. Following a brief history of the scientific and professional forces that contributed to their rise as international standards, there is a succinct summary of their characteristics, similarities, and differences. Their advantages and limitations for research, specialty clinical, and primary care clinical use are identified, as is the need for a briefly described ICD-11 primary care version (ICD-11-PC). Likewise, the need for, and development of, a separate International Classification of Functioning, Disability, and Health (ICF) is described that is now more closely linked to these mental disorder classifications. These interim classifications now await further understanding of the aetiology and pathophysiology for iterative advances.

Background: Non-communicable diseases account for the majority of the global burden of disease. Interventions, such as exercise and healthy diet, have the potential to decrease the prevalence and burden of diseases. While global academic studies represent the domain knowledge and provide the marginal effect of risk factors, individual level observational data reflects the joint distribution of local behaviors and socioeconomics, and their local impact on disease prevalence. Methods: We developed a method to evaluate the potential impact of a public health intervention by guiding individual level disease prevalence models of local observational data with the published meta-analysis results of Global Burden of Disease (GBD) studies. We use a Hierarchical Bayes framework to ensure that the model parameters are in agreement with the qualitative patterns in the meta-analysis results. The method provides impact estimates with uncertainty intervals at the population or any targetable subpopulation level. The intervention impact accounts for the burden due to multiple diseases without double counting and controls for demographics, socioeconomics and behaviors. We evaluate the potential impact of behavioral interventions involving smoking, physical exercise and diet on prevalence and disability adjusted life years (DALY) due to Ischemic Heart Disease (IHD) and Diabetes in the Turkish adult population.Findings: As expected, eliminating smoking and secondhand smoking and encouraging exercise have the potential to reduce DALYs associated with IHD and Diabetes by 10% (8% to 12%) and 8% (7% to 10%), respectively in the overall population. Somewhat surprisingly, increasing fruit and vegetable consumption would increase the expected DALYs for about one in five individuals due to its adverse effect on Diabetes, while bringing about a 2% (1% to 3%) reduction at the population level DALYs. Interpretation: The findings are in line with the qualitative patterns of GBD meta-analysis results, but differ quantitatively for some risk factors and demographic groups. Beyond evaluating the potential impact at the national level, the method allows the decision maker to see the heterogeneity of the intervention impact due to joint distribution of behaviors and other socioeconomic/ lifestyle factors in addition to different relative risk estimates by age and gender. Funding Statement: The authors are employees of Koç University and there was no additional funding from other sources for this research.Declaration of Interests: The authors do not have any competing interest.Ethics Approval Statement: We received a waiver from the Koc University Ethics Committee. We also got approval from the Turkish Statistical Institute for the study to be able to use the micro-level data.

We thank Stuckler and Reeves for their commentary1 on our re-evaluation3 of claims made by Banks et al.4 and others that the English are healthier than the US Americans. Living in England, Stuckler and Reeves may be forgiven for concluding their commentary by saying ‘So should you live in the US or England? Judging on the health data alone, we find the weight of evidence still (slightly) favours—England’. The point of our article was not to suggest that people move countries, but rather to propose a better methodology for tackling the difficult public health challenge of comparing health across populations. They1 begin by claiming that our aim was to operationalize the well-known World Health Organization (WHO) 1948 definition of health2. Our aim was actually the very different one of arguing that it is a mistake to adopt the Banks et al.4 understanding of health merely as the absence of disease. We rather claim that health needs to be measured as a vector of functioning in a parsimonious set of domains that matches the intuitive notion of health such that one can compare the health of people with (for example) diabetes and those with depression, an approach proposed by Salomon et al.5 We read with pleasure when Stuckler and Reeves1 point out the convergence of inferences obtained by examining recent Global Burden Disease (GBD) 2010 efforts6 and our own. We agree on this. The difference between us is that whereas the GBD says that the health differences between the USA and the UK are trivial, we say they are really, really trivial! To see this, consider their Table 1,1 in which healthy life expectancy (HALE) at age 50 is reported to be about 25 years for both the UK and the USA, with a 0.4-year advantage for the UK. If healthy survival after age 50 has a Poisson distribution, this difference amounts to 8% of a standard deviation. Life expectancy (LE), by contrast, at age 50 is about 31–32 years for the UK and the USA, with a 0.8-year advantage for the UK. If survival after age 50 has a Poisson distribution, this difference amounts to 14% of a standard deviation. But these differences are indeed very, very small. Imagine a sample of 1000 persons aged 50 years and older from the UK and a matching number from the USA. Taking all possible pairs, selecting one person from the UK and one from the USA, if we predicted a longer life expectancy for the person from the UK we would be right 52% of the time, instead of the 50% of the time that might be expected if the life expectancy distributions were identical in the USA and UK or by chance. Our Rasch-based health metric,3 on the other hand, suggests a smaller difference (about 2% of a standard deviation) and implies a correct guess about health in 51% of pairs of USA/UK elders instead of the 50% expected if the distributions were identical. A related feature of our approach,3 which Stuckler and Reeves1 call ‘a major limitation’, is our reliance on self-report data, and the fact that culture is an important driver of how respondents answer questions about their health. In fact, our results argue for diminishing differences between the USA and the UK after these cross-national differences in self-reporting of health are adjusted. In other words, our results are based on the Rasch model, in which the health score is estimated after correcting for differential item functioning (DIF) and therefore accounting for reporting biases and hence population invariant. We think that culture and, more broadly, all social and environmental differences between the USA and the UK, influence self-reported health. The Rasch and DIF scoring procedure—with different thresholds for different sex, age and national groups—is an attempt to address this. Although we do not report it, had we done our Rasch scoring of health without correction for DIF by national group, we might have had larger USA/UK difference in health. This means that it is possible that all of the (neglible) health differences between the USA and the UK may be due to measurement error caused by cultural differences in the self-reporting of health. Regarding the ‘curious reporting conventions’, at least one is the convention of this journal, namely that of reporting 90% rather than 95% confidence intervals for the main result, following Sterne and Davey Smith.7 As for the suggestion that the goodness-of-fit tests we used would not permit our conclusion, this would be quite correct if we had based our conclusion on these tests. In fact, we only used these tests as further supporting evidence for our main conclusions, which were derived from the regression coefficients resulting from the linear additive model as reported in Table 4 of our paper.3 Another concern of theirs1 is that although we object to Banks et al.4 looking only at a few health conditions, we, it would seem, reduce all health conditions to a single unidimensional scale. But this is to misconstrue the fundamental difference between ‘counting diseases’ as a measure of severity (like counting apples and oranges and then deciding which of these two groups are sweeter overall) and constructing a metric of health based on the functioning domains that are constitutive of the essence of health. Finally, we are told that we have constructed a ‘straw man’1 by citing examples of where the Banks et al.4 conclusions about UK health advantage have been relied on. It suffices to invite readers to peruse the Institute of Medicine Report U.S. Health in International Perspective: Shorter Lives, Poorer Health (2013)8 which cites Banks et al., and similar studies, extensively. Finally, although we are reluctant to recommend that US Americans immigrate to the UK to improve their health, we definitely would recommend, when comparing the health of populations, to supplement comparing prevalence of diseases with a more nuanced and rich analysis based on a fuller conception of health, since after all, health is more than the absence of disease… Conflict of interest: None.

The World Health Organization (WHO) plans to submit the 11th revision of the International Classification of Diseases (ICD) to the World Health Assembly in 2018. The WHO is working toward a revised classification system that has an enhanced ability to capture health concepts in a manner that reflects current scientific evidence and that is compatiblewith contemporary information systems. In this paper, we present recommendations made to the WHO by the ICD revision’s Quality and Safety Topic Advisory Group (Q&S TAG) for a new conceptual approach to capturing healthcare-related harms and injuries in ICD-coded data. The Q&S TAG has grouped causes of healthcare-related harm and injuries into four categories that relate to the source of the event: (a) medications and substances, (b) procedures, (c) devices and (d) other aspects of care. Under the proposed multiple coding approach, one of these sources of harm must be coded as part of a cluster of three codes to depict, respectively, a healthcare activity as a ‘source’ of harm, a ‘mode ormechanism’ of harm and a International Journal for Quality in Health Care, 2016, 28(1), 136–142 doi: 10.1093/intqhc/mzv099 Advance Access Publication Date: 11 December 2015 Perspectives on Quality © The Author 2015. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved 136 by gest on M arch 1, 2016 D ow nladed fom consequence of the event summarized by these codes (i.e. injury or harm). Use of this framework depends on the implementation of a new and potentially powerful code-clustering mechanism in ICD-11. This new framework for coding healthcare-related harm has great potential to improve the clinical detail of adverse event descriptions, and the overall quality of coded health data.

Background: Contemporary casemix systems for health services need to ensure that payment rates adequately account for actual resource consumption based on patients' needs for services. It has been argued that functioning information, as one important determinant of health service provision and resource use, should be taken into account when developing casemix systems. However, there has to date been little systematic collation of the evidence on the extent to which the addition of functioning information into existing casemix systems adds value to those systems with regard to the predictive power and resource variation explained by the groupings of these systems. Thus, the objective of this research was to examine the value of adding functioning information into casemix systems with respect to the prediction of resource use as measured by costs and length of stay.Methods: A systematic literature review was performed. Peer-reviewed studies, published before May 2014 were retrieved from CINAHL, EconLit, Embase, JSTOR, PubMed and Sociological Abstracts using keywords related to functioning ('Functioning', 'Functional status', 'Function*, 'ICF', 'International Classification of Functioning, Disability and Health', 'Activities of Daily Living' or 'ADL') and casemix systems ('Casemix', 'case mix', 'Diagnosis Related Groups', 'Function Related Groups', 'Resource Utilization Groups' or 'AN-SNAP'). In addition, a hand search of reference lists of included articles was conducted. Information about study aims, design, country, setting, methods, outcome variables, study results, and information regarding the authors' discussion of results, study limitations and implications was extracted. Results: Ten included studies provided evidence demonstrating that adding functioning information into casemix systems improves predictive ability and fosters homogeneity in casemix groups with regard to costs and length of stay. Collection and integration of functioning information varied across studies.Results suggest that, in particular, DRG casemix systems can be improved in predicting resource use and capturing outcomes for frail elderly or severely functioning-impaired patients.Conclusion: Further exploration of the value of adding functioning information into casemix systems is one promising approach to improve casemix systems ability to adequately capture the differences in patient's needs for services and to better predict resource use.

Objective Our aim was to specify the requirements of an architecture to serve as the foundation for standardized reporting of health information and to provide an exemplary application of this architecture. Methods The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) served as the conceptual framework. Methods to establish content comparability were the ICF Linking Rules. The Rasch measurement model, as a special case of additive conjoint measurement, which satisfies the required criteria for fundamental measurement, allowed for the development of a common metric foundation for measurement unit conversion. Secondary analysis of data from the North Yorkshire Survey was used to illustrate these methods. Patients completed three instruments and the items were linked to the ICF. The Rasch measurement model was applied, first to each scale, and then to items across scales which were linked to a common domain. Results Based on the linking of items to the ICF, the majority of items were grouped into two domains, Mobility and Self-care. Analysis of the individual scales and of items linked to a common domain across scales satisfied the requirements of the Rasch measurement model. The measurement unit conversion between items from the three instruments linked to the Mobility and Self-care domains, respectively, was demonstrated. Conclusions The realization of an ICF-based architecture for information on patients’ functioning enables harmonization of health information while allowing clinicians and researchers to continue using their existing instruments. This architecture will facilitate access to comprehensive and consistently reported health information to serve as the foundation for informed decision-making.

QUALITY ISSUE:Responding to person's health and related needs requires the availability of health information that reflects relevant aspects of a health condition and how this health condition impacts on a person's daily life. INITIAL ASSESSMENT:Health information is routinely collected at different time points by diverse professionals, in different settings for various purposes with varying methods. Consequently, health information is not always comparable, posing a challenge to the regular monitoring of quality. CHOICE OF SOLUTION:The World Health Organization's (WHO) International Classification of Diseases (ICD), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI; under development) are complementary and serve as meaningful reference classifications for comparing data on persons' health and related interventions across health systems. IMPLEMENTATION:We developed a systematic approach of translating routinely collected information into a standardized report based on the three WHO reference classifications and the Rehab-Cycle®. Subsequently, we have demonstrated its application using five random case records of individuals attending a rehabilitation program. EVALUATION:All identified concepts were able to be linked to WHO's reference classifications. The ICF served as a tool to standardize information on rehabilitation goals and their achievement. The ICHI served as the basis for reporting the interventions that were documented in the case records, including the intervention targets that were derived from the ICF codes. LESSONS LEARNED:Our experience shows that, it is possible to translate routinely collected information into standardized reports by linking existing narrative records with WHO's reference classifications.

Due to fundamental differences in design and editorial policies, semantic interoperability between two de facto standard terminologies in the healthcare domain--the International Classification of Diseases (ICD) and SNOMED CT (SCT), requires combining two different approaches: (i) axiom-based, which states logically what is universally true, using an ontology language such as OWL; (ii) rule-based, expressed as queries on the axiom-based knowledge. We present the ICD-SCT harmonization process including: a) a new architecture for ICD-11, b) a protocol for the semantic alignment of ICD and SCT, and c) preliminary results of the alignment applied to more than half the domain currently covered by the draft ICD-11.

The World Health Organization (WHO) plans to submit the 11th revision of the International Classification of Diseases (ICD) to the World Health Assembly in 2018. The WHO is working toward a revised classification system that has an enhanced ability to capture health concepts in a manner that reflects current scientific evidence and that is compatible with contemporary information systems. In this paper, we present recommendations made to the WHO by the ICD revision's Quality and Safety Topic Advisory Group (Q& S TAG) for a new conceptual approach to capturing healthcare-related harms and injuries in ICD-coded data. The Q& S TAG has grouped causes of healthcare-related harm and injuries into four categories that relate to the source of the event: (a) medications and substances, (b) procedures, (c) devices and (d) other aspects of care. Under the proposed multiple coding approach, one of these sources of harm must be coded as part of a cluster of three codes to depict, respectively, a healthcare activity as a 'source' of harm, a 'mode or mechanism' of harm and a consequence of the event summarized by these codes (i. e. injury or harm). Use of this framework depends on the implementation of a new and potentially powerful code-clustering mechanism in ICD-11. This new framework for coding healthcare-related harm has great potential to improve the clinical detail of adverse event descriptions, and the overall quality of coded health data.

In the beta phase of the 11 revision of International Classification of Diseases (ICD-11), the World Health Organization (WHO) exposes ICD-11 content through a collection of web services. The emerging Common Terminology Services 2 (CTS2) standard provides a common model and semantics for representation, interchange, and federation of terminological resources. We hypothesize that the CTS2 standard can provide service layer of standardization that could potentially aid in the interoperability among authoring applications for the ICD-11 revision. This paper examines the existing WHO ICD-11 content services from the perspective of the CTS2 standard. The content in the WHO ICD-11 content services was mapped to the Code System Catalog, Code System Version Catalog, Entity Description and Association models in the CTS2 specification. It proposes mappings for existing ICD-11 properties and suggests additional CTS2 properties may be important for ICD-11. The mapping effort was used to develop a prototype of the CTS2 Services Wrapper for the ICD-11. In conclusion, the CTS2 standard is useful in exposing ICD-11 content representation through predictable and familiar services. Keywords—ICD-11; CTS2; Medical Classification; Data Standards; Biomedical Ontologies

The upcoming ICD-11 will be harmonized with SNOMED CT via a common ontological layer (CO). We provide evidence for our hypothesis that this cannot be appropriately done by simple ontology alignment, due to diverging ontological commitment between the two terminology systems. Whereas the common ontology describes clinical situations, ICD-11 linearization codes are best to be interpreted as diagnostic statements. For the binding between ICD codes and classes from the ontological layer, a query-based approach is favoured.

Semantic Interoperability, i.e., preserving the meaning among health related data, is one of the crucial topics of Health Informatics. The International Classification of Diseases by WHO and SNOMED-CT, by IHTSDO, are the most prominent systems currently available for coding health data. In 2010 a collaboration agreement between the maintainers of ICD and SNOMED-CT has been signed, and gave birth to a Joint Advisory Group (JAG), aimed at defining a common basis between both terminological systems, in the form of a Common Ontology. In fact, in addition to the base task of defining the logical structure of the Common Ontology, JAG also designed a workflow for identifying its content items. As a preliminary step, the investigation of three specific ICD11 chapters has been designed to understand the total workload and calendar of activities. The present paper briefly describes the method and the tool that has been developed to support the latter investigation.

PURPOSE:The Patient Reported Outcomes Measurement Information System (PROMIS (®) ) is a US National Institutes of Health initiative that has produced self-report outcome measures, using a framework of physical, mental, and social health defined by the World Health Organization in 1948 (WHO, in Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, 1948). The World Health Organization's International Classification of Functioning, Disability and Health (ICF) is a comprehensive classification system of health and health-related domains that was put forward in 2001. The purpose of this report is to compare and contrast PROMIS and ICF conceptual frameworks to support mapping of PROMIS instruments to the ICF classification system .METHODS:We assessed the objectives and the classification schema of the PROMIS and ICF frameworks, followed by content analysis to determine whether PROMIS domain and sub-domain level health concepts can be linked to the ICF classification.RESULTS:Both PROMIS and ICF are relevant to all individuals, irrespective of the presence of health conditions, person characteristics, or environmental factors in which persons live. PROMIS measures are intended to assess a person's experiences of his or her health, functional status, and well-being in multiple domains across physical, mental, and social dimensions. The ICF comprehensively describes human functioning from a biological, individual, and social perspective. The ICF supports classification of health and health-related states such as functioning, but is not a specific measure or assessment of health, per se. PROMIS domains and sub-domain concepts can be meaningfully mapped to ICF concepts.CONCLUSIONS:Theoretical and conceptual similarities support the use of PROMIS instruments to operationalize self-reported measurement for many body function, activity and participation ICF concepts, as well as several environmental factor concepts. Differences observed in PROMIS and ICF conceptual frameworks provide a stimulus for future research and development.

To assess the relationship of posttraumatic stress disorder (PTSD) with health functioning and disability in Vietnam-era Veterans.A cross-sectional study of functioning and disability in male Vietnam-era Veteran twins. PTSD was measured by the Composite International Diagnostic Interview; health functioning and disability were assessed using the Veterans RAND 36-Item Health Survey (VR-36) and the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0). All data collection took place between 2010 and 2012.Average age of the 5,574 participating Veterans (2,102 Vietnam theater and 3,472 non-theater) was 61.0 years. Veterans with PTSD had poorer health functioning across all domains of VR-36 and increased disability for all subscales of WHODAS 2.0 (all p < .001) compared with Veterans without PTSD. Veterans with PTSD were in poorer overall health on the VR-36 physical composite summary (PCS) (effect size = 0.31 in theater and 0.47 in non-theater Veterans; p < .001 for both) and mental composite summary (MCS) (effect size = 0.99 in theater and 0.78 in non-theater Veterans; p < .001 for both) and had increased disability on the WHODAS 2.0 summary score (effect size = 1.02 in theater and 0.96 in non-theater Veterans; p < .001 for both). Combat exposure, independent of PTSD status, was associated with lower PCS and MCS scores and increased disability (all p < .05, for trend). Within-pair analyses in twins discordant for PTSD produced consistent findings.Vietnam-era Veterans with PTSD have diminished functioning and increased disability. The poor functional status of aging combat-exposed Veterans is of particular concern.