Recent advances in analytical chemistry have allowed a greater possibility of using quantitative approaches for measuring human exposure to chemicals. One of these approaches is biomonitoring (BM), which provides unequivocal evidence that both exposure and uptake of a chemical have taken place. BM has been a longstanding practice in occupational health for several reasons. BM integrates exposure from all routes. It can help identify unintentional and unexpected exposures and assess the effectiveness of existing risk-management measures. BM also provides relevant information to support policy development by delivering better evidence of workers’ exposure to chemical substances, even within the framework of the present regulations. Thus, BM can allow for both the evaluation of the impact of regulation and identification of further needs for new or improved regulation. However, despite all these well-recognized advantages, BM is currently an underused exposure assessment tool. This paper provides an overview of the key aspects to be considered when using BM in the context of occupational health interventions. Additionally, this paper describes the potential of BM as an exposure assessment tool, distinguishing the role of BM in exposure assessment and health surveillance and clarifies ethical and communication aspects to guarantee that general data protection regulations are followed. In addition, actions and research needs are identified (particularly with reference to the European situation), which aim to encourage the increased use of BM as an exposure assessment tool. Exposure assessment is defined as the process of estimating or measuring the magnitude, frequency and duration of exposure to an agent and the size and characteristics of the population exposed . BiomoniBM measures chemicals and their metabolites in the human body, usually through analyses of blood, urine, hair, breast milk or exhaled breath condensate. It provides an aggregated measure of the level of exposure to chemicals through different exposure routes. BM is an important tool for assessing exposures to chemicals among the general population and workers ,3.Whilst occupational exposure to chemicals continues to be an important cause of mortality and health burden worldwide, the need for ongoing prevention and control initiatives is justified ,5,6. In Moreover, one of the main advantages of BM data is that they offer potential communication advantages over ambient monitoring data. For instance, Sexton et al. stated that they yield “unequivocal evidence that both exposure and uptake have taken place” ( p.41). ABM of occupational exposure to chemicals is a longstanding practice in occupational health risk assessment and occupational health practice . HoweverIn the occupational health context and according to Manno et al., BM could have three main goals: 1. individual or collective exposure assessment; 2. health protection; but also 3. occupational health risk assessment . The WHOIn 1998, the International Labour Office defined occupational health surveillance as “the ongoing systematic collection, analysis, interpretation and dissemination of data for the purpose of prevention” . AccordiBM is a potentially important tool for OSH research, practice, and policy but the full extent of these tools has yet to be realized. Therefore, the focus of this paper is to describe the potential of BM as an exposure assessment tool in the context of occupational health, distinguish the role of BM in exposure assessment and health surveillance and, finally, to clarify ethical and communication aspects to increase the use of BM as an exposure assessment tool in the occupational health context. Although the paper covers these aspects in a generalised way, we also review the place of biomonitoring in European occupational safety and health regulations, in particular.Considering the relevance of the subject for the exposure science community, this paper was developed with the support of the International Society for Exposure Science–European Chapter (ISES Europe), an interdisciplinary network of experts in different regulatory and research roles to further improve and reinforce the use of biomonitoring in occupational health.The most common reasons to perform occupational exposure assessments are to evaluate compliance with legal requirements, to measure workers’ exposure and to evaluate the effectiveness of risk management measures (RMM). In all of these, BM can play a relevant role when, besides inhalation, other exposure routes  have an important contribution to the workers’ overall exposures. Thus, BM may be particularly useful for those chemicals that are easily absorbed through the skin or taken in by ingestion due to their permanence on workplace surfaces, or where exposure is controlled only by personal protective equipment (PPE) and thus is dependent on the PPE quality, correct selection and use .BM therefore has advantages compared to the common practice of ambient monitoring because it provides additional data . In the case of substances with long half-lives that accumulate , cadmium, lead) BM reflects total exposure over time. In addition, BM aggregates exposures from all sources; at work, at home and from other environments  ,19. ThusBM may also give insight into time-trends of total internal exposure of a substance where BM is performed on a regular basis. In this way it will give information on the effectiveness of RMMs already taken. In addition, stratification of similar exposure groups of workers can be based on BM data. This information can help occupational hygienists to determine whether and where exposure reduction measures would be most effective. For substances with high background levels of exposure  BM data might need additional analysis for interpretation. Thus, with adequate contextual data  BM data are able to answer several questions related with risk assessment and management such as: What is the total exposure? What is the contribution of other exposure routes besides inhalation ? Are there opportunities to reduce exposures? These questions involve interpretation of BM data but also relate with risk management issues, implying the collection of contextual data . As with ambient monitoring data, BM will also make workers aware of the need to work safely since they will be given individual BM values compared to the limit or guidance value in place .Furthermore, BM can play a role in two different strategies: in a collective approach, to support the need to improve working conditions and to provide information for an exposure management strategy and, in a more individual approach, to give an answer related to health risk of each worker. At the individual level, monitoring results can lead to the introduction or improvement of exposure control measures and individual working practices within the working environment and, in some special circumstances (like in the case of high lead accumulation in the body), job replacement. In the collective method, monitoring data can be used to assess the effectiveness of exposure controls, develop risk communications, and identify failures of prevention and control. Therefore, it should be possible to analyse BM data on both levels to protect workers . HoweverIn epidemiological studies, exposure information is essential in investigating dose-response relationships between a chemical and a certain health effect. Indeed, the lack of insight in quantitative total exposure information of a substance is often a problem in epidemiological studies. When standardized guidelines become available for measuring many chemicals simultaneously and high throughput analyses exist, then identification of new dose-response relationships may be possible, linking chemical body burden to specific health outcomes. Certainly, BM may contribute significantly to further developing quantitative risk assessments .To summarise, BM data can support several occupational health information needs, namely: providing knowledge of exposure by all exposure routes, determine if a specific exposure has occurred and if it implies a risk to health; helping practitioners to understand the results from clinical testing in some circumstances; recognising the adequacy of the RMMs in place; demonstrating the link between an occupational exposure and a health effect. Finally, BM data can support health monitoring and surveillance programmes, and identify possible trends in exposure ,24,25,26There are several types of biological value in occupational settings (regulated or advised) with the following terminology in the European Union: biological limit values (BLVs) which are health-based, or biological guidance values  which are not health-based but are statistically derived. BGVs indicate background internal exposures in the general population . Specifically, a BGV represents the concentration of the substance (or a metabolite) in any appropriate biological medium corresponding to a certain percentile  in a defined reference population ,27. If bAn overview of different types of occupational limit values is presented in In order to permit an easier interpretation of BM results, biological limit or guidance values are required. A distinction is also made here between not necessarily health-based Binding Biological Limit Values (BBLVs), BLVs which are health-based threshold values, and BGV. At this moment there is only one BBLV in the European Union published in the CAD; which is set for lead and its ionic compounds. Member states are obliged to set a national BBLV that is lower or equal to the value published in the CAD. BLVs are currently only given as SCOEL  or RAC  recommendations and, with the exception of lead and its compounds, none of them has been included in CAD since only BBLVs are recognised in this directive. The same applies to BGVs.Therefore, member states are not obliged to set national BLVs/BGVs for the substances not included in CAD, Göen et al.  present Additionally, REACH regulation  also allows the setting of derived no-effect levels (DNELs) for biomarkers, although it does not give much advice for their derivation. In contrast, the REACH IT-system (IUCLID) does not include a placeholder for internal biomarker-DNELs ,33,34. Aa skin notation assigned to the OEL identifies the possibility of significant uptake through the skin. This definition is also mentioned in the Methodology for the Derivation of Occupational Exposure Limits defined by SCOEL [Skin absorption, an exposure route common for substances used in occupational settings, has been highlighted in the OSH regulation through the use of the “skin notation” term. As mentioned in the Directive 2000/39/EC of 8 June 2000, by SCOEL . In thesby SCOEL . In 2017by SCOEL . Additioby SCOEL .The American Conference of Governmental Industrial Hygienists also define ‘skin notation’ as “the potential significant contribution to the overall exposure by the cutaneous route” and state that “biological monitoring should be considered to determine the relative contribution to the total dose from exposure via the dermal route” and “the use of a skin notation is intended to alert the reader that air sampling alone is insufficient to quantify exposure accurately” .as a possible exposure route to chemicals in the workplace or uses the skin notation for specific chemicals. In this last case, the most common text associated in the legal text is “A skin notation assigned to the occupational exposure limit value indicates the possibility of significant uptake through the skin”. In the Directive 2004/37/EC, the text is different but the meaning is similar “Substantial contribution to the total body burden via dermal exposure possible”.The high number of substances having a skin notation emphasises BM as a relevant exposure assessment tool. For substances that have the capacity to be absorbed by the skin special attention should be given to identify RMMs that can prevent skin contact, in addition to any inhalation RMMs.The time sequence of steps towards an official implementation of BM in Europe is presented in BM in the occupational health setting was developed early in the 20th century with the determination of B-Pb  and benzTherefore, better clarification of all the possible BM applications in the context of OSH such as: a relevant tool for exposure and risk assessment, to provide the information needed to define the RMMs to be implemented or to improve the ones already in place is of utmost importance.https://www.hbm4eu.eu/about-hbm4eu/) research project a BM study was developed aiming to collect new data on current occupational exposure to hexavalent chromium (Cr(VI)) in Europe, engaging eight countries. In this study it was decided that the study participants would receive their personal urinary chromium results from the research team or an occupational physician, depending on the country specific regulations  [Effective communication is essential for proper collection, interpretation and use of BM data in the occupational exposure assessment process. Without an effective communication process, undesirable outcomes may result. These can include anxiety, conflict, lack of confidence and trust, resulting in the study being viewed as a poor use of time and resources and the study outcomes being questioned. In the case of a BM research project being developed or a BM campaign taking place in a company site, communication between the field work team and the target worker population is key for the success of a BM campaign. The field work team need to have a clear understanding of the worker and company involvement required for the proposed study/BM campaign  and clearly communicate to the workers what is requested of them, the reasons why and the wider benefits of the study/BM campaign ,42. Thiseedback) .Comparable to the general population, communication and dialogue with workers needs to be established in the initial phases of an exposure assessment process. This approach will result in several benefits, such as increased trust in those responsible for the BM campaign, increased use and relevance of the data obtained and improved dissemination ,46.In fact, one of the first steps that will influence the definition of to whom  the BM results will be communicated is to recognize the purpose of the BM campaign. If the main purpose is workers health surveillance, the data communication should be made by the occupational physician directly to each worker involved or, by someone that has been identified as the point of contact and dissemination during the informed consent, guaranteeing always the confidentially aspects; if the purpose is exposure and risk assessment, then the individual worker data should be aggregated in a way that guarantees data confidentiality and anonymization and, only in this case, can be communicated to the employer and, for instance, contribute to a more accurate exposure and risk assessment and, if needed, support the improvement of the risk management measures in place. However, in cases of individual results exceeding binding biological limit values , the occupational health team should analyze those individual results, considering the contextual data available, and act accordingly to prevent continued exposure and thereby ensuring the workers’ health protection. Actions can be focused on improving the risk-management measurements in place or, as a last resort or when other measures cannot be applied in a short period of time, to remove the worker from a specific workplace/task until exposure can be avoided or minimized. Nevertheless, the same BM data can be used for both purposes  resulting in a better use of resources. Additionally, they can also be used to inform and change workers’ behaviours to reduce or eliminate exposure. Bearing in mind all these possible applications of BM data, it is important to recognise the need for BM limit or guidance values. These provide, besides an easier interpretation of BM results, a clearer communication of results to workers and employers.Considering ethical issues, Manno and co-authors summarized very well the main ethical aspects that should be considered when communicating BM data to workers . They stConsidering all of the above issues, it is possible to recognize why it is important to define a communication plan, with enough resources and time, in the beginning of an occupational exposure assessment process that might implicate a BM campaign. This communication plan should present the details of what information to provide, to whom, when and in what format. For example, the communication plan should address the timely reporting of results to the workers that participated in the BM campaign and this is indeed a very relevant aspect to guarantee their willingness to participate in future BM campaigns. The communication plan should also explain how (and if) information will be given to the employers, community, the media, stakeholders, the scientific community and other groups as considered suitable by the research team and ethically appropriate by ethics committees . HoweverAt a company level, the purpose of the BM campaign should be identified in advance and a strategy to communicate individually the results to each worker should be decided . This should convey a clear message not only about the worker’s result but also the meaning of that result for their health risk. Moreover, the moment to communicate and explain the results should be used also for increasing workers’ health literacy through simple messages to allow workers to be more prepared to take the correct decisions to avoid exposure and to protect their own health. This communication should be performed by someone of the occupational health team since all these professionals observe the rules of confidentiality in the execution of their functions, as previously reported .Bearing in mind what makes effective communication and given the central role of communication in the success of interpretation and use of BM data, developing an infrastructure should be of high priority for BM supporters and researchers. These efforts will also guarantee that the ethical aspects will be taken on board from the beginning of any BM campaign. In summary, to ensure success, effective communication and increase the success of interpretation and use of BM data, a number of actions should be developed. (1) Good and clear communication between the field work team and the target worker population from the beginning. (2) Explain the purpose and the wider benefits of the study/BM campaign to all concerned stakeholders. (3) Make efforts to adapt to workers communication style preferences whenever possible. (4) Ensure results confidentiality is at all times upheld. (5) Results need to be consistent, with clear actions being identified that workers and/or their company can undertake if remedial actions are considered necessary.Without proper communication, the efforts of undertaking a BM campaign could be unsuccessful in fulfilling the most pertinent of all goals: protecting workers’ health.https://ipchem.jrc.ec.europa.eu/) is starting to host BM data for the general population; this is mostly datasets uploaded from the HBM4EU project (https://www.hbm4eu.eu/).Various research institutes and commercial laboratories involved in BM are likely to maintain their own databases of BM data but these are generally not openly accessible as they will likely contain personal data. Some data have been published from these databases on an ad-hoc basis . The IPCThese databases and others that should become available will facilitate standardisation and harmonisation of rapidly evolving components of exposure science and, consequently, risk assessment will benefit from well-designed BM studies. Furthermore, these databases will assist a national health authorities, EU agencies, policy-makers, the scientific community and civil society in coordinating research efforts and facilitating new approaches to communication on health risk assessment. Additionally, BM databases can be a good support for defining regulation needs and priorities as well as to evaluating the impact on exposure of regulatory actions already in place. This implies their continued updating with new data.BM can also have an important role in the identification of new and emerging risks of chemicals. Although more commonly applied in general population studies, non-targeted analysis of chemicals can be  used for detection of both known and unknown chemicals in biological samples and is necessary to support further exploration of the total of occupational exposures . It may The generally accepted classification of biomarkers divides them into three main categories: biomarkers of exposure, effect, and susceptibility ,54,55. ABiomarkers of effect are (reversible) biochemical and functional alterations en route to the ultimate health effect. These biomarkers may be an endogenous component, or a measure of the functional capacity, or some other marker of the state or balance of the body or organ system, as affected by the exposure. Commonly it is a pre-clinical marker of pathology and can be specific or non-specific ,58. SpecTherefore, effect biomarkers used as early predictors of clinical disease can advance occupational health risk assessment and trigger new effective disease prevention actions in occupational settings, but further validation is needed ,26. The Tiered approaches need to be set up for establishing useful effect biomarkers, generating data on effect biomarkers and use of the results in (mixtures) exposure assessment. In the future, it may also be possible to perform preliminary mixtures exposure assessment by using multifactorial statistical correlation analyses to establish relationships between levels of various analytes and one effect biomarker. Another important research need is whether the presumed specific occupational biomarker can be raised by exposures outside the work e.g., by specific foodstuffs, hobbies, use of consumer products or environmental pollution. To address this it might be relevant to include a control group of the general population in order to exclude the possibility of the observed effect being related to other exposure sources and not the workplace environment. This is a common approach followed even when studying exposure (and not effect of) to contaminants that, besides being present in the workplace, are also food contaminants ,63.Concerning biomarkers of susceptibility, data may be useful in risk assessment and risk management efforts. However, it is important to mention that these are still very much under scientific development and they should be carefully evaluated in terms of sensitivity and specificity, interpretation, communication and management of the results, as well as from an ethical perspective to not discriminate or reduce job opportunities and the quality of life for involved workers ,64.Phenotyping regarding xenobiotic metabolising enzymes is possible, albeit not common. Rapid or slow metabolizers might be at increased health risks, dependent on whether metabolism exhibiting genetic polymorphism is activating or deactivating the parent substance. Phenotyping is not frequent and mostly used in hospitals for optimizing dose levels of human pharmaceuticals. If one wants to take this into account in occupational settings as well, transformation of current clinical tests is probably necessary . Moreover, these biomarkers need to be specific for the chemical substance the worker is exposed to. Additionally, the co-exposure with medicinal drugs might be relevant as well because some of them can induce or inhibit xenobiotic biotransformation , thereby increasing the susceptibility to some occupational exposures .https://echa.europa.eu/oel). This is the main reason for the variable use of BM in different member states. According to the paper by Louro et al., [Under EU OSH legislation, the EU SCOEL has recommended biological limit or biological guidance values for more than 20 substances. These have not, however, been incorporated into the Chemical Agents Directive or Carcinogens and Mutagens Directive (CMD) except for B-Pb for which a BOELV has been established at EU level in the CAD. Therefore, except for B-Pb it is currently up to the member states how they apply SCOEL—recommended BLVs and BGVs in the exposure and health risk assessment of workers , BM is noted only as a health surveillance tool and not as an exposure assessment tool. As a consequence, BM is considered as medical data limiting the use of BM data in risk management at the workplace because of the privacy issues. This clearly needs regulatory clarification, since in most cases in practice, BM is measuring only exposure biomarkers and thus can inform only on exposure, and not on health . Thus, under the OSH framework, there is a clear need to explain the role of BM as an exposure assessment tool versus health surveillance tool.https://echa.europa.eu/documents/10162/22979809/tmpl_reporting_occupational_exp_data_du_en.xlsx/84ef3203-4294-75c8-3b79-9c024abc2bcd).Another important regulatory use of occupational BM data is in the context of REACH regulation. REACH includes a possibility to set DNELs for exposure biomarkers  in the chemical safety reports as prepared by industry or by RAC in their opinions. But in practice, these include limited methodological information on how they are derived and how they should be used in practice, and what the underlying uncertainties have been. In addition, as noted by Louro et al. , the REAAlso in the case of pesticides (plant protection products) regulation, BM could have a more visible role along the external exposure monitoring as a tool to characterize exposure. According to a recent publication of EFSA (European Food Safety Authority) , availabThere is a need for guidance and education of risk assessment and OSH experts at different levels. First of all, there is a need for clear guidance and best practices for regulators for the setting of biological limit and guidance values. Under OSH, SCOEL had guidelines for this but RAC has taken over SCOEL’s tasks, and RAC refers to existing practices of SCOEL and e.g., the French Agency ANSES  and the German Commission for the Investigation of Health Hazards of Chemical Compounds in the Work Area (MAK Commission) OSH agencies but does not have any detailed guidance of their own . Also deImportantly, the mere setting of biological limit/guidance values or DNELs is not enough in order to ensure the correct use of BM. This needs both general guidance on the use of BM in occupational settings as well as substance specific guidance.legal status of the biological limit value (binding or indicative);substance specific sampling strategy (including specific sampling time considering toxicokinetic data);possibilities for pre-analytical errors , other exposures which might have an impact on the results ;analytical requirements, quality assurance;interpretation of results; what does it mean if the measured value exceeds BGV/BLV, what actions should/should not be taken;General guidance should include guidance on the setting-up of the BM campaign at the workplace and recruitment of workers, collaboration between the occupational physician and industrial hygienist to set up the study and to interpret the results at the group level. Furthermore, it might be that the use purpose  necessitates some specificity in the generation and interpretation of the BM data. Therefore, clarification of the purpose of the actual BM should be included, i.e., as an exposure assessment tool versus a health surveillance tool. Guidelines for dealing with confidentiality and General Data Protection Regulation issues in the case of medical data are also needed. The legal basis of BLVs and BGVs needs to be explained as well as the difference between BBLVs in the CAD (and maybe in future in the CMD) and (possible) indicative biological values that have been published only in SCOEL Opinions see also . GeneralMoreover, a separate guidance is probably needed for the use of BM in REACH/plant protection products contexts, explaining how BM is used in post-market surveillance, what the biomarker-DNEL means, what are the  consequences if it is exceeded etc.To achieve wider acceptance of BM as an exposure assessment tool, greater obligation to perform post-marketing surveillance occupational BM is needed. With this obligation, there are also research needs to make the regulatory obligation work and for it to be meaningful.Regulatory obligations in the near future will likely trigger further research. Early research results will enable more concrete law-making.As health risk is driven by exposure, the exposure–effect relationship and susceptibility, there is a need to address all three related types of biomarkers in research needs, at least for part of the tens of thousands of chemical substances that are commercially available. For all three, regulatory needs already exist or are expected in the future. Right now, there are only obligations  for exposure biomarkers, and none for effect and susceptibility biomarkers. However, with increased knowledge and use cases as well as regulatory need, such as e.g., for combined exposure to substances with similar modes of action, this is expected to change. Exposure to mixtures, the most common scenario concerning exposure in the workplaces, can likely be assessed and any identified risks effectively controlled with sufficient knowledge and use of effect biomarkers.One central need, without which sensible use of BM data in an occupational health and safety context is not possible, is a thorough understanding of the relationship between external and internal exposure. Internal exposure is the best exposure measure, but risk mitigation measures can only be taken at the external exposure level. And as the relationship between external exposure  and internal exposure (exposure biomarker) is time-dependent, it is obvious that knowledge of the kinetics of a substance following exposure via inhalation or dermal exposure is of utmost importance, i.e., in order to know what to measure with the BM tool, one needs to know the most suitable exposure biomarker, be it the parent compound, a metabolite or the sum of both. For this, thorough knowledge on the parent, its metabolites and their appearance over time is needed.Therefore, and focusing on exposure biomarkers, there is a need for more and better human data regarding absorption, distribution, metabolism and excretion. Also needed are blood concentration-time curves and urinary (and sometimes respiratory) excretion profiles (which is toxicokinetics) for each occupational exposure relevant chemical substance. Crucial is knowledge on absorption rates, distribution (including whole blood vs serum vs plasma), metabolism  and excretion (routes and rates) of workplace-relevant substances and under worker-relevant circumstances.2), surface exposed (cm2), partial occlusion by personal protection equipment such as protective clothing or respiratory protective equipment. This could make it imperative to use human volunteer studies, e.g., mimicking actual daily occupational exposure under laboratory conditions, as long as they are performed by strictly following ethical guidelines.Sometimes the above needs to obtain sufficient knowledge on the toxicokinetics of a substance can be addressed by carefully studying actual occupational exposure scenarios. However, there may be limitations to what is possible in order to delineate toxicokinetic information from real-life situations when accurate external exposure levels are needed. This might refer e.g., to substances for which peak exposures via inhalation are relevant for possible effects and where the relevant half-life is short and non-frequent sampling will not sufficiently reflect exposure. It might also refer to actual dermal exposure in terms of duration (hours or minutes), surface area concentration  and the suitability of personal protective equipment together with an individual worker exposure assessment. Detailed BM data can also be used in conjunction with health data in occupational epidemiological studies. Furthermore, BM can provide relevant information to support policy making by delivering better evidence of the exposure of workers to chemical substances, allowing for the evaluation of the impact of regulations and the need for new or improved OSH regulation.However, despite these benefits, BM is currently an underused exposure assessment tool. Several actions have been proposed to maximise the benefits and opportunities that this exposure assessment tool presents. Firstly, efforts for the legal enforcement of biological limit values in EU should be advanced. Secondly, clarity should be given (through regulation and guidance) on the valuable role that BM can have in workplace exposure assessment and risk assessment/management, as well as workers’ health surveillance. Thirdly, clear guidance should also be created for supporting regulators that have the responsibilities of setting biological limit and guidance values. In addition, regulators should communicate to the research community where biological limit and guidance values for a substance were not possible due to lack of data. General and substance specific guidance is also needed for occupational physicians and industrial hygienists on the management of BM campaigns and how to deal with ethical and General Data Protection Regulation issues.Finally, it will be important to develop guidance to help employers set up a BM programme as part of their overall approach to chemical risk management at the workplace. This should include information for workers who participate in such programmes and should explain the purpose of BM, how it will be carried out, and how the results will be used.These actions will help understanding the approach used to define limit and guidance values, to understand their utility for exposure assessment and health surveillance, to identify the needs for further research and to increase the use of BM by occupational health professionals.


The COVID-19 pandemic took high-income countries entirely by surprise. Despite funding pandemic preparedness programs in Asia for more than 20 years, donor countries had not experienced an uncontrolled pandemic since HIV in the 1980s. When Ebola, Zika, SARS, and MERS threatened, countries outside the immediate geographic neighborhood or income level of those diseases’ places of origin were left largely untouched. In contrast to the swift, comprehensive response of South-East Asian countries, authorities in Europe and the United States assumed this coronavirus would behave like its predecessors SARS and MERS.What happened next around the world was both harrowing and illuminating. Lacking protective material resources, the human capacity for contact tracing or understanding of the disease, policymakers in higher-income countries turned to technology for a miracle. The technology sector responded with history’s most extensive hackathon, illuminating the mutual shaping of technology and public health policy. The most striking feature of the technological response to the pandemic has been the degree of what Morozov has called solutionism driving it—the belief that complex problems can be solved by technological intervention alone. The COVID-19 pandemic took high-income countries entirely by surprise. Despite funding pandemic preparedness programs in Asia for more than 20 years, donor countries had not experienced an uncontrolled pandemic since HIV in the 1980s. When Ebola, Zika, SARS, and MERS threatened, countries outside the immediate geographic neighborhood or income level of those diseases’ places of origin were left largely untouched. This led many northern countries not to take seriously the emergence of the novel coronavirus in 2019 as the existential threat it turned out to be. In contrast to the swift, comprehensive response of South-East Asian countries, authorities in Europe and the United States assumed this coronavirus would behave like its predecessors SARS and MERS, which made their hosts seriously ill and sent them home, reducing their ability to infect others. Instead, the new virus presented itself with a very different epidemiology, hiding mostly unseen amongst the young and active while they infected those around them and ravaging the elderly and sick wherever it spread.What happened next around the world was both harrowing and illuminating. Lacking protective material resources, the human capacity for contact tracing, or understanding of the disease, policymakers in higher-income countries turned to technology for a miracle. The technology sector responded with history’s most extensive hackathon, illuminating the mutual shaping of technology and public health policy. The most striking feature of the technological response to the pandemic has been the degree of solutionism Morozov  driving The driving example of solutionism during the first wave of the pandemic was the claim by a group of public health information specialists  toward the simpler problem of repurposing existing population surveillance infrastructures. Along with the apps, over the first months of the pandemic, many solutionist projects were proposed including systems to detect coronavirus infection from voices Futurism , detectiThere are higher-level lessons to be drawn from these proposals for pandemic technologies, perhaps most importantly that allowing the market to be the sole driver of technology development will result in technology that primarily serves the market rather than the needs of the public. However, the most relevant lessons for the ongoing effort to combat the pandemic are more immediate. In parallel with the European and US responses to the pandemic, a different set of strategies adopted by South-East Asian countries exists. These, ironically, were based substantially on guidance from institutions founded and funded in the Global North such as the WHO, philanthropies, and universities. They involved early response, immediate travel restrictions, intensive testing and contact tracing, and enforced quarantine where cases were found. Where these measures were taken, both infections and deaths remained strikingly low BBC . DespitePerhaps the most important lesson we can take with us into the “new normal” of the pandemic from observing technological solutionism in the Global North is this: technology is important to pandemic response, but it must support rather than lead. The practical lessons of the pandemic are ones the North already knew, because it was busy teaching them in the South: the state of the art on how to combat a pandemic is to test, trace, and isolate, and to do this exhaustively and accurately. Yet rather than apply this knowledge, governments turned out to be more ready to listen to Northern innovators than to the public health experts, often internationally funded, in Southern pandemic-affected countries. The response shows that the North has not acquired the necessary capacity for pandemic response, namely flexibility, a focus on the most vulnerable, and human capacity supported by the digital rather than the other way around. Instead, we seem to have trained hard in believing our own rhetoric on technological innovation and the power of the market as solutions to truly existential challenges.


Background: In Germany, people suffering from severe mental illness who have committed serious offenses and have considerably reduced or suspended criminal responsibility can be detained and treated in forensic psychiatric hospitals. In the German federal state of Baden-Wuerttemberg, all psychiatric hospitals including forensic psychiatric hospitals are obliged to record data on every coercive intervention and to submit them to a central registry. The objective of this study was to determine key measures for the use of seclusion and restraint and to compare them with data from the same registry on the use of coercion in general inpatient mental health care.Methods: Data on the main psychiatric diagnosis according to ICD-10, type and duration of each coercive measure and number of treated cases according to diagnoses, and cumulated number of days of treatment from all 8 forensic facilities in the state of Baden-Wuerttemberg covering a catchment area with about 11 million inhabitants were collected at the treated-case-level for 3 years.Results: 22.6% of the cases treated in 2017 in forensic psychiatric hospitals were subjected to seclusion, and 3.8% were subjected to mechanical restraint. The mean cumulated duration of seclusion episodes per affected case was 343.9 h and the mean cumulated duration of restraint episodes was 261.7 h. 13.2% of the treated cases were subjected to room confinement with a mean cumulated duration of 539.1 h per affected case. Involuntary medication was applied in 1.9% of the cases. In general psychiatry, 2.9% of the treated cases were subjected to seclusion, and 4.7% were subjected to mechanical restraint. The mean cumulated duration per affected case amounted to 32.2 h for seclusion episodes and to 37.6 h for restraint episodes. Involuntary medication was applied in 0.6% of cases.Conclusion: Compared to general psychiatry, mechanical restraint is used in forensic psychiatry substantially less frequently and seclusion substantially more frequently. Room confinement is used only in forensic psychiatric hospitals. Use of involuntary medication is rare. On the one hand, recorded involuntary medication comprises only clear actions against the patient's expressed will as defined by law. Psychological pressure to take medication to avoid other forms of coercion and to achieve higher levels of freedom within the facility is not recorded. On the other hand, the low numbers of clear involuntary medication probably reflect the high legal threshold for such interventions, and, consequently, efforts by staff to motivate voluntary acceptance. The long duration of freedom-restricting coercive measures in forensic psychiatry probably reflects the selection of patients at high risk of violence. In Germany, people suffering from severe mental illness who have committed offenses and are deemed to have considerably reduced or suspended criminal responsibility at the time of the offense can be detained in forensic psychiatric hospitals according to penal law . A criminal court can order the detention of an individual in a forensic psychiatric hospital, if due to his or her condition, the individual presents a significant risk of committing harmful or dangerous acts, and the detention will be suspended only when the court is of the opinion that the individual presents no future danger to the public or that ongoing detention is no longer proportionate. A similar rule applies to offenders with addictive disorders who can be detained in specialized forensic units ; however, in these cases, the maximum duration is limited to 2 years plus two-thirds of a parallel prison sentence.The conditions of forensic psychiatric treatment in the respective facilities are regulated by the federal law of the 16 federal states, with certain differences between them. This applies also to the use of freedom-restrictive coercive interventions. The law explicitly mentions seclusion and mechanical restraint, as well as being confined in one's own room (room confinement). With regard to involuntary medication, the threshold is high since a seminal decision by the Federal Constitutional Court (Bundesverfassungsgericht) in 2011 , emergency medication, and involuntary medication following a judge's decision in all psychiatric hospitals, including forensic psychiatry. It is mandatory for all psychiatric hospitals to supply this data to acentral register.The central recording of coercive measures, on the one hand, makes special demands on data protection and data security in view of the highly sensitive personal data. On the other hand, the simplest possible transmission of the data to be delivered is desirable. Therefore, an online platform was set up after detailed consultation with and approval of the State Data Privacy and Data Security Officer. The platform serves both for uploading data by the institutions and for downloading data by the evaluation office. Thus, the register offers the unique possibility to analyze the use of coercive interventions in all eight forensic psychiatric hospitals in a Federal State with 11 million inhabitants. The patient population of these hospitals is very well-characterized by other data available from the Forensic Base Documentation (FoDoBa)  which haThe objective of this study was to determine key measures for the use of seclusion, restraint, and involuntary medication as defined earlier in non-forensic patients  and to cWe obtained data on patients and on coercive measures from two sources: the central register (CR)  and the The FoDoBa was introduced by all forensic hospitals in Baden-Wuerttemberg in 2009 and contains extensive data on age, gender, socioeconomic status, number and kind of offenses, personal history, living conditions, family situation, and medical and psychiatric history of all detained patients. In 2009 and 2010, data had been provided twice a year, since 2011 data is provided on a yearly basis.The CR contains data on coercive interventions from both forensic psychiatry and general psychiatry. Each facility with an obligation to report data has its own protected upload area on the online platform of the CR. When uploading data, a comparison between the user name and mandatory fields for hospital identification is made. In the case of contradictions, the data upload is blocked with a simultaneous warning to the user. There are three datasets to be uploaded. Dataset 1 contains all the coercive measures to be reported, together with the hospital identification code, pseudonymized case number, postal code of residence, gender, main diagnosis, legal basis for hospital stay at the beginning of the coercive measure, type, and duration of coercive measure. The other two datasets contain aggregated data on the number of treated cases and days of treatment. According to the requirements set by the State Data Privacy and Data Security Officer, the data of the CR is structured in such a way that the identification of specific persons is not possible, i.e., the data is anonymized. This especially applies to Dataset 1. Evaluations based on the central register are provided regularly to the psychiatric and forensic hospitals and to the parliament of Baden-Wurttemberg.Seclusion is defined as separation into a specially secured room which can be locked from the outside. The affected patient is brought into a separate room and locked up there or prevented from leaving the room.Restraint is defined as physical restriction of movement by belts .Room confinement means securing a patient in his or her own room. In contrast to seclusion, it is not a special, safe (and mostly empty) room and in contrast to time-out, the room is locked from the outside.In the CR for non-forensic patients in general psychiatric hospitals, each complete patient treatment episode within a given reporting year is defined as a treatment case. If, for example, a patient had been admitted on December 15, 2016 and was discharged on January 10, 2017, he or she is counted in the reporting year of 2017 with all 26 days of treatment in 2016 and 2017. If a patient had been admitted on December 20, 2017 and was discharged on January 5, 2018, she is not counted in 2017.For forensic patients each newly started, ongoing, or completed patient's treatment episode within a given reporting year is defined as a treatment case and is tallied in this reporting year. Different to non-forensic patients, in forensic patients only those days of treatment which accrued within a given reporting year are considered. This difference arises because the majority of forensic patients are not discharged within a given year and their total length of stay is still unknown.As no patient identification is given and case identification numbers are pseudonymized, it is not possible to check whether two or more cases relate to the same patient. This applies to both non-forensic patients and forensic patients.The FoDoBa defines treated cases as the number of patients being treated as inpatients on a given day .The data analysis was carried out at case level. Therefore, a treatment case may have several different coercive measures and may be tallied in all categories, a case with seclusion episode, a case with restraint episode, and a case with room confinement. Due to the structure of the data, the identification of specific persons was not possible.The Ethics Committee of Ulm University waived the requirement for ethics approval as approval is not required for studies analyzing anonymized data, in accordance with national legislation and institutional requirements.In 2017, eight forensic hospitals and 32 non-forensic hospitals reported data to the central register. In the eight forensic hospitals, on December 31, 2017, the number of treated patients amounted to 1,049. Of these, 131 patients were preliminarily committed following a crime and awaiting trial, and 918 had been given a hospital order by court decision. Most of the patients suffered from psychotic disorders or severe personality disorders , and 362In the eight forensic hospitals 1,431 patients were treated in 2017  with a tIn the 32 general psychiatric hospitals, 115,011 patients were treated in 2017  with a tTo our knowledge, this is the first publication reporting the use of coercive interventions in forensic psychiatric services for a politically defined catchment area over several years. Moreover, the case registry offers the possibility to compare the obtained results with data on all general psychiatric hospitals in the same Federal State of 11 million inhabitants, recorded by the same methods, and under identical definitions of coercive interventions. Due to strict legal requirements, repeated conferences on data quality among all hospitals run by the Ministry of Social Welfare and Integration, and the necessity to submit raw data collected in electronic charts to the registry, the validity of the data is probably good to very good . The resThe result of the higher use of coercive interventions in forensic psychiatric facilities is not unexpected and indicates a higher degree of risk of violence toward others as all patients in forensic hospitals have been detained by criminal courts following significant offenses. Yet our data do not allow to draw conclusions on whether these differences can be wholly explained by the different patient characteristics or whether different institutional practices and cultures also play a role.An interesting topic of discussion between forensic psychiatrists and general psychiatrists is the different use of seclusion and mechanical restraint. In forensic psychiatry, mechanical restraint is used very rarely, which is not the case in general psychiatry. The reason, according to discussions among clinicians, is that the duration of these interventions is time limited in most cases in general psychiatry, with a mean duration of a single measure of about 8 h , indicatIn comparison with general psychiatry, it is noticeable that relatively few cases are treated in forensic psychiatry . However, these cases account to 11.5% of the number of treatment days in general psychiatry, reflecting the selection of clinically severe cases in forensic psychiatry.A comparison of the eight different forensic psychiatric hospitals, as we did for the general psychiatric hospitals , makes lOur study has some limitations. First, there exists no external validation in the strict sense.So even if very unlikely, given the background of legal obligations, underreporting cannot completely be excluded. This refers particularly to the approval of involuntary medication by a judge, which could be omitted from the recording process on the form sheet for coercive measures after the long run of legal procedures. But there is a degree of control, after all, as in the case of eye-catching values, inquiries are made by the commissioned office and the validity of the data is thoroughly checked by the respective hospitals.Second, there is no data available on the reasons why coercive measures were carried out.As data privacy has a high priority in Germany, only limited data may be gathered on the central register. Yet the hospitals themselves gather vast amounts of information on the reasons and consequences of coercive interventions. The data from the central register is discussed annually among representatives of the hospitals at a specialist conference which frequently results in close examinations of attendant circumstances of coercive interventions.Third, is the decision to refer an individual to forensic psychiatry and to discharge someone later on, depends on local courts' decisions and not on clinical judgment. Therefore, forensic psychiatric populations in other Federal states of Germany might be somewhat different.Fourth, our study is based on retrospectively collected routine data and uses only aggregated data for secondary analyses. However, retrospective data collection is used in all studies on coercive measures where a researcher is not present fulltime to observe what happens. Real prospective studies in a strict sense are extremely rare in the field.Due to the wide lack of comparable data from other countries, it is difficult to interpret the results in terms of appropriateness or quality. Some comparable material can be found in two publications from one of the two forensic psychiatric hospitals in Finland. Putkonen et al.  evaluateHowever, the low prevalence of involuntary medication, compared to the high prevalence of freedom-restrictive measures needs in-depth discussion. Involuntary medication, if applied as a rapid tranquilizing injection, may often be connected with feelings of shame and humiliation , all theNevertheless, in our opinion, adequate medication treatment of acute crises could reduce restraint and seclusion and the total amount of coercion. The major reason for the low prevalence of involuntary medication is the high threshold of legal requirements for treatment against a patient's will to be approved by a judge. Though over 40% of the cases had a diagnosis of a psychotic disorder (schizophrenia or mania), only in about 2% of cases involuntary medication was approved. It is improbable and in contrast to findings from patient interviews  that allIn the Netherlands, formerly very high rates of seclusion dropped considerably after increased use of medication , 16. RegThe datasets generated for this study will not be made publicly available. The data stored in the register is classified as confidential by the data protection officer of Baden-Wuerttemberg and is not publicly available due to data privacy.The Ethics Committee of Ulm University waived the requirement for ethics approval as approval is not required for studies analyzing anonymized data, in accordance with national legislation and institutional requirements.EF made the evaluations and calculations and contributed to introduction, methods, and discussion. TS and UF contributed substantially to introduction and discussion.TS and EF are commissioned with the operation of the central register for coercive measures. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The possible association of patient safety events (PSEs) with the costs and utilization remains a concern. In this retrospective analysis, we investigated adult hospitalizations at a medical center between 2010 and 2015 with or without reported PSEs. Administrative and claims data were analyzed to compare the costs and length of stay (LOS) between cases with and without PSEs of the three most common categories during the first 14 days of hospitalization. Two models, including linear regression and propensity score-matched comparison, were performed for each reference day group of hospitalizations. Of 14,181 PSEs from 424,635 hospitalizations, 69.8% were near miss or no-harm events. Costs and LOS were similar between fall cases and controls in all of the 14 reference days. In contrast, for cases of tube and line events and controls, there were consistent differences in costs and LOS in the majority of the reference days . Consistent differences were less frequently seen for medication events and control events . Our study approach of comparing cases with PSEs and those without any PSE showed significant differences in costs and LOS for tube and line events, and medication events. No difference in cost or LOS was found regarding fall events. Further studies exploring adjustments for event risks and harm-oriented analysis are warranted. The context and practice of clinical care have improved in recent years in terms of patient safety through advances in medicine and technology. However, the occurrence of adverse events (AEs), defined as an injury caused during the health care process, rather than by the underlying disease or condition of the patient, remains a significant issue ,2,3,4. TPromoting patient safety usually involves a common strategy to detect patient safety events (PSEs), defined as an unexpected or unintended event that could have led or did lead to harm to the involved patient during the care process. Typical events might include patient falls; medication events such as prescription errors, dispensing errors, and drug administration errors; tube and line events such as inadvertent removal of an indwelling tube or line, etc. With this approach, studies have estimated an AE rate of 2.9–16.6% . TypicalBesides, the association between PSEs and healthcare utilization and costs has also been increasingly studied. A study in the period 2003–2004 reported the AE-related costs of over two billion Australian dollars annually . As therAmong the methods used to analyze PSE-related costs , linear The remainder of the paper is organized as follows. The next section outlines the methodology of this study. We subsequently present and discuss our findings and provide concluding remarks.This retrospective study was conducted at National Taiwan University Hospital to analyze the cost and healthcare utilization associated with PSEs between 1 January 2010, and 31 December 2015. The Institutional Research Ethics Committee of the National Taiwan University Hospital approved this study (#201709046RINA) and waived the need for informed consent from the patients. The hospital is a medical center, with about 2300 beds and approximately 6800 workers, including 460 attending physicians, 650 residents, 2800 nurses, and other workers. The hospital’s Incident Reporting System (IRS) was established in 2002; all of the PSE reports in this study were provided electronically. Staff from the Center for Quality Management of the hospital validated the content, patient harm, and severity of each PSE after receiving the report.Patients admitted to the hospital during the study period were included. Patients with at least one reported PSE were labeled as PSE cases; the remaining hospitalizations without any reported PSE were classified as controls. This labeling was used to retrieve relevant clinical, administrative and claims data upon request from the Integrated Medical Database (NTUH-iMD) of the Department of Research of the National Taiwan University Hospital, who provided de-identified data for research purposes. Patients were excluded if they had at least one of the following conditions: (1) admitted for end-of-life management; (2) admission not paid by the National Health Insurance program.patient safety event (PSE) was defined as an unexpected or unintended event, which could have led to or did lead to harm to the involved patient. These events consisted of the following types: an adverse event (AE) was defined as an injury caused during the health care process rather than by the underlying disease or condition of the patient; a no-harm event was defined as an event which resulted in no harm to the patient, or the effect was so minor that the patient could not even feel it; a near miss event was defined as an event that may have caused an accident, injury or illness, but did not due to an unintentional or timely intervention [A rvention .To analyze the association of PSEs with costs and healthcare utilization, the investigators acquired the following variable: demographic data, admitting department, category of event, the severity of harm to the patient, claimed medical expenses of the hospitalization, and length of stay.The following data were collected for each PSE from the IRS database: age of the patient, department, and ward for admission, category of events resulting in the PSE, severity of harm to the patient. Furthermore, after the approval by the Research Ethics Committee, the study investigators requested the Integrated Medical Database (NTUH-iMD) of the Department of Research of this hospital to providing de-identified clinical and administrative data. The database provided cases with de-identification data for research purposes. Patient-level costs were obtained from the claims for each admission for the reimbursement by the National Health Insurance of the Taiwan, which provided universal healthcare coverage of the patients and set the prices for hospital care. Costs were converted into US dollars based on the exchange rate toward the end of 2015. The inflation rate in Taiwan has been maintained at a steady low level, about 1.09% annually; therefore, the authors decided that the inflation rate would not be included in the adjustment. The primary outcome measures were total financial costs based on the claims and length of stay.selection bias to assigning patients with PSE to the control group because of the non-reporting of events. This situation could also result in information bias, in that the control group could also suffer from unreported PSEs. These types of bias could result in underestimation of the impact of costs and LOS caused by the PSEs.Since the under-reporting of PSEs remains a commonly understood issue across the institution, the study might encounter As the investigators were not able to have a reference cost estimate from a pilot analysis in the same hospital or a similar study from another institution, the study size was not estimated, and the investigators decided that all of the reported PSE in the IRS would be analyzed after proper inclusion and exclusion processes were applied.We first performed a descriptive analysis of the included reported PSEs regarding their category, severity, and time of occurrence concerning the admission date to show the general background scenario. Categorical data are expressed as number (%). Continuous variables were expressed as mean ± SD and 95% confidence interval (95% CI). We then performed inference analysis to compare the direct costs and LOS between the cases and controls according to the model used. We then selected the most common categories of PSE, together contributing to at least 50% of the PSEs. In this study, we used two models to estimate differences in the direct costs and LOS between the cases and controls. In both models, we only selected the cases with a reported PSE within the first 14 days of hospitalization as the cases and then stratified these cases into 14 groups based on the day when the PSE was reported. The control cases were those who did not have any reported PSE and were not discharged until at least the stratified day. For example, for the reported PSEs that occurred on the third day after admission, we only included controls with an LOS of at least three days. In the first model, we performed linear regression analysis to estimate direct costs and LOS, including age, gender, diagnosis, Charlson comorbidity index ,22, and p-value < 0.05 was considered to indicate statistical significance.Continuous variables were expressed as mean ± SD. Statistical analyses were performed using SAS software . All statistical comparisons were two-tailed. A During the 6-year study period, there were 424,635 admissions to the hospital. n = 1695), tube and line events , and medication events . Therefore, we decided to use the first 14 days of hospitalization in these three categories as the analysis cohort.A total of 14,181 PSEs related to hospitalized patients during the study period were reported to the IRS, with an incident rate of 3.3 per 100 admissions. We further analyzed differences in the costs and LOS for the three most common PSEs, including falls, tubes and lines, and medication events. We then selected the three most common categories  of PSE that together contributing to at least 50% of the PSEs. As a result, we chose three of the most commonly reported categories of PSEs for inference analysis based on the rationale that the occurrence of surgical and transfusion events should only develop in those patients who had received or at least been arranged for these two categories of treatment. As our study data lacked a detailed daily list of surgical procedures and the amount of transfusion, adjustment on these factors before comparison by each reference day was not possible. To estimate the differences in direct hospital costs and LOS, we selected PSEs within 14 days of hospitalization and control hospitalizations without PSEs. As can be seen in Our results showed that of the hospitalizations with and without PSEs during the first 14 days of admission, the extents of differences in costs and LOS varied across the three PSE categories. Differences in costs and LOS for fall events were not significant by either of the two comparison models; however, the differences were more consistently significant for tube and line events and were less consistently significant for medication events by the two estimation models. A feature of this paper is the novel approach with the reference day-stratified grouping of cases against controls based on the occurrence or absence of patient safety events. We believe that this methodology has not been reported in the literature. We also employed two analysis methods, including linear regression and propensity score-matched comparison, and the results were similar between the two methods. The comparison between these two methods based on reference day stratification approach has not been reported before, either.The comparisons of costs and LOS in this study were based on the presence or absence of reported PSEs through the institutional IRS; thus, in addition to AEs, the analyses also included events without associated patient harm and those reported for identified undesired processes, materials, equipment and facilities related to patient care. Therefore, this study provides a broader approach to the understanding of undesired contexts of care concerning the burden of healthcare systems. A study showed that AE-related additional financial costs were highest for surgery-related events, with an additional 10.9 hospital days and additional costs of USD 57,727, while an event occurring at medical wards led to an additional 9.8 hospital days and an additional cost of USD 38,656 . AnotherOur claims-data-based approach to estimate costs might have ignored other hidden costs such as administration costs in terms of reporting, data collection, meeting and discussion, improvement activities, and maintenance of the facilities and materials related to health care. Although the cost difference was not significant for fall events, the aggregated cost may still have had a large impact on healthcare. However, standardized methods to assess the burden related to PSEs are currently lacking . AlthougGiven the higher financial burden and longer LOS, our study may not imply causality between the occurrence of PSEs and higher costs. Specific categories of PSE, such as tube and line and medication, would not occur if the patients do not receive the specific materials for the intervention. The complexity of a clinical condition might also be associated with the materials used, and intervention received, contributing to a higher cost and carrying a higher risk of PSE. Therefore, the cost and LOS may have the same risk factors as the cases with PSEs. Moreover, as most of the patients with PSEs did not suffer from any harm, a longer LOS is less likely to be caused by the PSE, but may be related to the disease and the care provided. Another possible explanation is that the occurrence of a PSE, even if it did not result in patient harm, may be linked to an undesired quality relating to the care process and undesired context. This situation may also have affected the effectiveness and efficiency of care, as reflected by increased costs and prolonged LOS. Further studies to comprehensively investigate the financial impact associated with these events are needed. There are several limitations to this study. First, reporting PSE was voluntary; therefore, underreporting of PSEs is highly probable, as has been reported previously . HoweverIn conclusion, we aimed to analyze the difference in cost and length of stay between admission with PSE and without one. Our findings showed mixed results for the differences in costs and length of stay between admission with PSE and without. The differences in costs and LOS were not significant in cases with fall event and those without PSE, while the costs and LOS differences were more significantly different in terms of tube and line events and medication events. We employed reference day-based analysis with propensity score-matched comparison to provide a possible method for the estimation of PSE-associated cost and LOS. This method might provide a deeper understanding of the impact of PSEs on the healthcare burden. Further studies exploring adjustments for event risks and harm-oriented analysis are warranted.


Educational indicators are metrics that assist in assessing the quality of the educational system. They are often associated with economic and social factors suggested to contribute to good school performance, however there is no consensus on the impact of these factors. The main objective of this work was to evaluate the factors related to school performance. Using a data set composed by Brazilian schools’ performance (IDEB), socioeconomic and school structure variables, we generated different models. The non-linear model predicted the best performance, measured by the error and determination coefficient metrics. The heterogeneity of the importance of the variable between school cycles and regions of the country was detected, this effect may contribute to the development of public educational policies. Indicators are statistics that reveal the performance of the educational system. Although they are not perfect descriptors, they are critical for evaluating past policies and designing new strategies to improve results –3. In adHistorically, the students’ socioeconomic status is often correlated with their performance –8, and cThe Brazilian educational system is marked by the slow development of formal education, initially restricted to social layers with greater economic power, which has contributed to the economic disparities across the regions of the country , 13 S1 FTherefore, measuring educational performance has become the main point, as the results allow to assess the efficiency of the educational system and constitute a new benchmark for changes in formulation and implementation of educational policies. As resources are limited, the indicators allow the identification of elements to be improved and are also a form of public transparency so society can verify if the resources allocated to Education are producing the expected results.In Brazil, the Basic Education Development Index (IDEB) is the main descriptor of educational quality . This inThe National Institute of Educational Studies and Research Anísio Teixeira (INEP) produces numerous data that could potentially help in the analysis of factors that may influence school performance and, therefore, help to formulate improvements in educational policies. However, those data are not fully explored and only a few studies use sophisticated econometric tools, generating macro insights on the educational level. Most studies apply traditional methods that have important limitations . In general, the most studied factors are those with the greatest impact on academic performance: (i) socioeconomic situation of the family , 9, 16; The study of such factors can benefit from the use of scientific data analysis techniques. Numerous research fields  have benefited from using different analysis techniques such as Machine Learning and Data Science , 22. SevConsidering the low educational performance, the models of multiple linear regression, penalized regression and gradient boosting machine (GBM), we have assessed an IDEB data set of different groups of descriptive variables to evaluate the predictive power of school performance, using variables that describe school structure and organization, in addition to socioeconomic variables. We have applied different analysis, including non-linear models. This study is expected to help in the conception of new perspectives for the design of educational policies.The public data set comes from different sources, all of them available on government websites with free access, partly related to education and partly related to socioeconomic information. The educational variables describe the performance of students in each school (IDEB) , the infIDEB is calculated every two years and is an index that simultaneously lists two fundamental characteristics in school evaluation: school performance and proficiency , 15 The The descriptive variables of the school structure were obtained by the 2017 School Census . The cenEducational indicators are summarized variables generated by INEP using data from the School Census, with longitudinal monitoring and crossing information from other surveys. They are specific, so the perception of school characteristics is immediate. Therefore, these indicators were inserted in the studied models: Average Number of Daily Class Hours, Teaching Effort Index, Teaching Regularity Index, Management Complexity Index, Rate of Teachers with Higher Education, Average Number of Students per Classroom and Adequacy of Teacher Education.Municipal financial statements are declared annually, making it possible to identify expenses incurred in different sectors of Education . In the The value of all elementary and high school teachers’ salaries were selected by the respective Annual List of Social Information microdata occupation codes . The extThe fraction of the municipal GDP destined to Education is part of a category that comprises the following government sectors: administration, defense, education, public health and social security. The level of aggregation disclosed does not allow for a greater degree of refinement, which would make it possible to identify the budget for each school. Educational GDP per capita was calculated using the population estimate by the Brazilian Institute of Geography and Statistics (IBGE).All data sets were loaded and unified according to the school or city code and data were pre-processed. Subsequently, the models were trained, and predictions were made. The error metrics and the determination coefficient were calculated for each of the models in the respective school cycles. With these statistics, the best predictive performance was chosen to study the importance of variables. Finally, IDEB predictions were made according with the five geopolitical regions of Brazil, which constitute well-established spatial groupings , each with specific characteristics . st - 5th grade), Final Years of Elementary School (6th - 9th grade) and High School (10th - 12th grade). Each set was divided according with the municipalities where the schools were located, this strategy aimed to reduce the possible effects of spatial correlation.The public schools were the focus of this analysis , since they concentrate 80% of the students. The data were segmented into three main data sets corresponding to the educational cycles analyzed: Early Years of Elementary School  a loss or cost function, which can be: the mean absolute error; the sum of the quadratic errors or the quadratic error; and (ii) a weak learner optimizer . All mod2) was calculated as the correlation between the values performed and the values predicted squared.The choice of the best model was made in terms of the coefficient of determination and the error metrics calculated for all models. The mean squared error (MSE) calculates the square difference between the observed value and the value predicted by the model, after which the mean of the differences is calculated. The root mean square error (RMSE) consists of the square root of the MSE, which leaves the errors on the same original scale, whereas the mean absolute error (MAE) measures the mean magnitude of errors for the set of predictions, without considering the direction of the differences. The higher the value of any of the errors, the worse the performance of the model. The determination coefficient , to identify whether grades improved, maintained or decreased in their performance, and afterwards the absolute number for each category was computed. In general, there has been little improvement in performance and between 6% to 7% of schools showed results equal to the immediately previous assessment. Regarding the decrease in grades, for the last period (2015–2017), 30.5% of schools had a reduction in grades, while 62.5% of schools managed to improve their exam performance s = 0.5–0.6, p < 0.05) was identified. The regressions obtained similar results, but with results lower than those in These results allowed an initial perspective to the national scenario, in which all schools were analyzed simultaneously, without any distinction of region. The method that obtained the best performance was GBM, with its error metrics lower than the other models and the determination coefficient with the best performance. These findings indicate greater predictive capacity, when compared to other methods, a condition that suggested the absence of linearity for the analyzed data sets. When correlating the data performed with those predicted, a moderate correlation between the data , Final Years x High School (rs = 0.739) Early Years x High School (rs = 0.533).To assess how different the importance of variables was between educational cycles, Spearman correlations were calculated pairwise. All relationships were significant (p < 0.01). The magnitude of the correlations is associated with how close the school cycles are: Early Years x Final Years  were used to analyze a data set based on educational and economic research. After evaluating the models, carried out in terms of error metrics and coefficients of determination, the importance of each variable for school performance was identified for the GBM method, which obtained the best predictive performance, and later IDEB regional predictions were made.The overall performance of schools in IDEB improved since its implementation, for all school cycles, but if individual school follow-up is observed, most schools do not assume a performance improvement path. This way, IDEB performance of approximately 62% of schools, regardless of the educational cycle, remains the same compared with the performance of the previous period. Only 7% shows better performance compared with the previous year. This finding indicates that in-depth investigations must be carried out to better understand the elements that cause the increase in global scores.The regression methods did not achieve the same performance as the GBM. One of the possible reasons for the best performance of the GBM model is the absence of rigid assumptions, among them the assumption of non-linearity between the elements analyzed, or even its high modeling and forecasting accuracy of response variables . One of Regarding the elements associated with IDEB for each educational cycle, we identified that the factors of greatest contribution are different for each cycle. Thus, a possibility for high school, a cycle with lower performance, it is crucial to consider some measures  during the formulation of public policies, that may not have the same effectiveness and efficiency for another school cycle, even if there is a strong correlation between some cycles.The difference between educational cycles and between regions was found, reflecting the historical regional difference well known in Brazil , 34: NorPublic policies for education should consider the effect of variables on school performance between school cycles and regions, so that the implementation of new practices could potentially maximize student’s performance. The results indicate a decrease in the performance of the model in some regions, and the impact of the variables is not the same for all regions. For late years of elementary school, the Midwest region had the worst performance, and as the variable with the greatest effect is GDP per capita, it may be that the increase in this variable has no impact on the quality of education.Educational heterogeneity is found in several countries in Latin American  and the According with the structure of each region, it was already expected that for the same model there would be uneven performances, a condition related to the historical and economic conditions of those places, which currently manifest themselves in terms of human capital. Thus, it was possible to detect that the same model does not have the same predictive capacity for all regions and school cycles, indicating that other factors not incorporated in this analysis may interfere in the predictive capacity of the future IDEB. This limitation can be reduced by adding new variables or by refining the data. As for the variables, it is known that the school failure rate, indicated by countless studies, is one of the main conditions for future school performance. However, at this moment it is relevant to identify the existence of factors for more immediate action, such as increasing the number of classes or reducing the average number of students per classroom so that positive results can come sooner.In addition to deepening in other Machine Learning methods, there are some investigations that were not privileged at this time, as our main objective was to identify the feasibility of using new techniques to assess the factors that contribute to good educational performance. We suggest future work could refine the parameters of the GBM, which obtained the best performance, as well as incorporate other years of evaluation, consolidating a longitudinal approach. Another possibility is to insert other descriptors of the school structure, composition of the classrooms and other economic variables. However, it is noteworthy that it was already possible to identify that school performance is better described by non-linear methods and that heterogeneity is present in the most relevant factors in educational and regional levels.Similar studies already report the efficiency of Machine Learning methods in predicting school performance, however, few studies have used IDEB in more comprehensive analyzes that allow simultaneous comparison between educational cycles and Brazilian geopolitical regions. In general, the most used models are those of classification, in order to detect whether students will pass, whether they will abandon the school year, and other issues, for very specific regions such as cities or even the number of classes. The identification of new elements that help in the prediction of IDEB, can be an alternative for the design of new educational policies. Considering the current scenario of economic restriction, it is possible to prioritize the elements that are not directly linked to educational expenses in a fixed and continuous way, but those of a specific character and temporarily delimited.Non-linear methods have greater predictive capacity for school performance and constitute a methodology to be explored in Brazil, where only few studies are available  opposingThis work brings new aspects about the performance evaluation of public schools, when considering the analyzes of the three educational periods of Brazilian basic education. We detected which are the most relevant factors for each of the educational cycles, and they should be considered for future educational policies. Those factors directly or indirectly related to school performance should be considered by policy makers who should consider the impact of specific conditions on the composition of local policies, given the heterogeneous character of the school performance. In conclusion, we believe that the proposed approach, based on non-linear models, is a promising tool to obtain new perspectives for the association of variables that describe school structure and school performance, in addition to allowing the detection of the elements that have the greatest influence on school performance.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.S5 Fig(DOCX)Click here for additional data file.S6 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.