In Germany, animal welfare during preslaughter handling and slaughter is governed by national laws and European regulations. However, animal welfare violations still occur at abattoirs, which is an ethical and economic problem. This article describes how our group of research associates investigated which specific measures and penalties responsible authorities consider appropriate for 40 different animal welfare violations at German abattoirs. Past measures and fines are described for some violations. The aim is to provide insight into the status quo, so that flaws in law enforcement can be identified. An online survey, semi-structured interviews, and a virtual colloquium with official veterinarians were conducted. Additionally, relevant judicial decisions from Germany were collected and summarised. Legal professionals were consulted for assistance. Our findings were compiled into a list of measures and penalties. This project is a first step towards achieving a more consistent and standardised enforcement of the German Animal Welfare Act, and thus towards reducing the occurrence of animal welfare violations at German abattoirs.Protecting animal welfare during preslaughter handling and slaughter is an important ethical concern with growing importance to consumers. However, animal welfare violations in abattoirs remain a serious problem, and the enforcement of relevant laws and regulations is often inadequate. This study investigated and compiled the measures and penalties which official veterinarians (OVs) consider appropriate for different animal welfare violations at German abattoirs, including ranges for fines. Additionally, information regarding which measures were taken in past cases, including past procedural outcomes , were gathered and summarised. The aim is to provide insights into the status quo, so that flaws in law enforcement  can be identified in a future study. To achieve this, the following five steps were utilised: acquiring relevant judicial decisions; conducting an anonymous online survey among German OVs; conducting semi-structured interviews with OVs; conducting a virtual colloquium with OVs; and consulting lawyers. Measures and penalties for violations of 40 relevant and frequent different provisions of the German Ordinance on the Protection of Animals in connection with Slaughter or Killing (TierSchlV), in conjunction with the Regulation (EC) No 1099/2009 and the German Animal Welfare Act, were gathered. The findings were compiled into a list of measures and penalties, which contains a separate table for all 40 violations, entailing an overview of the following information: citation(s) of legal/regulatory requirements to protect animals at the time of killing/slaughter; citation(s) of relevant regulatory and criminal penalties; special measures/penalties considered appropriate for the first and repeated offence by OVs; and information on penalties listed in judicial decisions of past similar cases. This initiative is a step towards achieving a reduction of animal welfare violations at German abattoirs. In Germany, the treatment of animals in abattoirs is regulated by Regulation (EC) No 1099/2009 . AccordiAnimal welfare violations present an important ethical issue, seeing as they inflict avoidable pain, distress, and suffering upon slaughter animals . FurtherIn total, this project developed a list of the measures and penalties that participating OVs evaluated as appropriate for relevant and frequent violations of 40 provisions of the German Ordinance on the Protection of Animals in connection with Slaughter or Killing, in conjunction with the Council Regulation (EC) No 1099/2009 and the German Animal Welfare Act. This information is crucial for identifying shortcomings in law enforcement and exploring ways to improve animal protection at abattoirs. This paper will describe the methodology and outcome of the different steps used to complete this research project. It is important to note that the measures and penalties collected here represent recommendations given by responsible authorities, which demonstrate the status quo of how violations are handled by responsible authorities, rather than presenting how such violations should and must be handled. In the future, experienced legal professionals specialised in administrative, regulatory, and criminal law at abattoirs should thoroughly review and refine these measures and penalties to establish helpful guidelines. This initiative shows great potential in promoting a more consistent and standardised enforcement of the German Animal Welfare Act, thereby potentially reducing the occurrence of animal welfare violations at German abattoirs.The list of measures and penalties (including those which OVs recommended and those implemented in the past) was compiled using the following five “steps”: acquiring all obtainable relevant judicial decisions from Germany; conducting an anonymous online survey among German OVs; conducting semi-structured interviews with OVs; conducting a virtual colloquium with OVs; and consulting lawyers throughout the course of the project. This project focused on violations at the abattoir, meaning that violations which can occur during transport to the abattoir were not addressed. Measures and penalties considered appropriate by OVs, in addition to current practices in animal welfare law enforcement , were compiled for 40 relevant and frequent possible animal welfare violations. The violations addressed were ones which occur during preslaughter handling, stunning, and exsanguination. The violations present regulatory offences according to Section 16 of the German Ordinance on the Protection of Animals in connection with Slaughter or Killing. A framework for a regulatory fine  was included for regulatory offences, which represents the median values of the ranges for fines considered appropriate by our survey participants. For the definition of a regulatory offence and a crime, please see Judicial decisions regarding animal welfare violations during preslaughter handling, stunning, and exsanguination were gathered in order to collect information on how courts handled past violations at abattoirs. In the beginning, legal databases . The target audience for the survey were OVs and others entrusted with enforcing animal welfare laws and regulations in German abattoirs . Participation in the survey was voluntary and there were no mandatory questions. The survey included questions regarding frequent cases of violations of Article 16 of the German Ordinance on the Protection of Animals in connection with Slaughter or Killing, which were identified through available literature, judicial decisions, media reports, and interviews with OVs. Altogether, 22 persons tested a mock-up to validate the survey. Suggestions for improvement were incorporated, leading to the final survey. The final survey included 22 constructed but realistic cases of animal welfare violations. In this section, these will be referred to as “cases”. Most cases (18/22) were at least regulatory offences according to Article 16 of the German Ordinance on the Protection of Animals in connection with Slaughter or Killing and the Regulation (EC) No 1099/2009, but in some cases could also be considered crimes due to the prolonged pain and/or suffering inflicted on the animal. The remaining cases also described situations which inflicted considerable pain and/or suffering on the animals, but a description that these specific violations constitute a regulatory offence cannot be found in German or European regulations. The survey was programmed using the online survey tool LimeSurvey, Version 3.28.21. In the beginning, the survey contained questions about the participants’ profession in order to get an impression of their experience and competence. This later allowed screening in terms of a participants’ eligibility for being included in the analysis. Suggestions for improvement were incorporated, leading to the final survey. Overall, the final questions and cases fell into one of seven question groups: questions regarding the participant’s professional experiences (10 questions); violence against slaughter animals and/or the use of prohibited driving aids (8 cases); inadequate housing/husbandry of animals in lairage (3 cases); restraining, stunning, and bleeding animals in a manner which violates animal welfare standards (8 cases); inappropriate handling of ill/injured animals (2 cases); employees performing tasks without an appropriate certificate of competence (1 case); and one question regarding the most common violations in the participant’s work environment . Appropriate measures could be selected for the first and repeated violation, and a range for an appropriate regulatory fine (in EUR) could be provided for the first and repeated violation, if the participant selected that a regulatory fine is an appropriate penalty . The second question inquired about the occurrence of a similar case in the participant’s work environment in the past three years. Information could be given as to whether a similar case to the violation described had occurred and what measures/penalties were taken or ordered. The question inquired about the past three years, because the measures taken  should be relatively recent, in order to reflect the current status quo. The case and/or the measures taken could be described in as much detail as desired using the comment box. Quantitative information on the regulatory fine, the warning fee, or a criminal fine (after a court case) was requested explicitly. The third question dealt with whether, from the point of view of the participant, other/additional measures would be necessary in this case. Other comments on the case could also be made here . The survey was available online for two months from 1 March 2022 to 30 April 2022. Participants were recruited by sending an e-mail including the link for the survey to all veterinary authorities in Germany (N = 431). Calls for participation were also printed in two articles in specialised journals for German-speaking veterinarians and participants in an online conference (attended by approximately 450 people) were informed about the survey before it started. ® SPSS Statistics Version 27  and Microsoft Excel 2019 ©. A measure/penalty was included in the respective table for the individual violation . A full set of the survey questions can be found in In July 2022, a virtual colloquium was conducted on two consecutive days via Cisco Webex to discuss how the measures and penalties proposed by participants of the online-survey should be improved, so that they truly represented the actions which the OV would consider appropriate for different violations. This gave OVs in all German federal states an opportunity to participate. Four hours per day were allocated to ensure enough time for all possible animal welfare violations. An invitation to participate was sent to all veterinary authorities in Germany via e-mail. People who registered to participate received the draft before the colloquium by e-mail, in order to incorporate suggestions for improvements/alterations in advance. During the event, the draft of the Word© document was shared on screen, allowing all attendees to see what changes were made to the document in real time. The aim of this event was to allow participants to discuss suggestions for changes to the measures and penalties proposed, and find a consensus in the end. Requests for modifications could either be provided verbally or in the chat. Overall, 40 possible violations of animal welfare regulations were discussed individually, corresponding to each violation, which can now be found in the list of measures and penalties in . A vote Throughout the course of the project, two lawyers were available to provide guidance. They were involved in order to ensure the correct use of legal terminology when designing the online survey. Additionally, the two lawyers supported the acquisition and analysis of judicial decisions. The lawyers involved were research associates. One is a Visiting Professor who works inter alia on questions of German Public Law and European Law. The other lawyer is a research associate in criminal law, who has published articles on animal welfare violations at the abattoir. The measures and penalties proposed were revised from a legal perspective by both lawyers. This project compiled a list of recommendations for measures and penalties for the 40 violations which can be found in By the end of the project, a total of 16 German judicial decisions from the years between 2015 and 2022 were obtained, which provided valuable insights into the enforcement of animal welfare laws and regulations at abattoirs. There were 20 negative responses to the requests for specific judicial decisions, citing various reasons such as the absence of a reference number, concerns regarding data privacy, or because the court case was still pending. Violations included in the judicial decisions were one or more of the following (listed in descending order of frequency): exceeding the maximum time allowed to pass between stunning and bleeding without a certificate of exemption ; an illegal use of devices which administer electric shocks, kicking/beating animals, exsanguination of ineffectively stunned animals , dragging animals which are too weak or injured to walk on their own with painful driving aids , a failure to supply drinking water to animals as required by relevant regulations, and not milking lactating dairy cattle every twelve hours. Several violations of the German Ordinance on the Protection of Animals in connection with Slaughter or Killing and the German Animal Welfare Act are often judged at the same time. Therefore, it was not always possible to provide information regarding which violations lead to which procedural outcomes. However, in many cases, information could be summarised and incorporated into the overview of measures and penalties for different violations, citing a specific fine for a specific violation. These summaries can be found in the respective table for a specific violation in In total, 312 persons started the survey. Most participants left a varying number of questions unanswered. Altogether, 204 participants (65.4%) ended the survey after the section regarding their profession (questions designed to screen participants). For a participant’s responses to be included in the analysis, questions corresponding to at least one case had to be answered. The entire survey was completed by 66 OVs, meaning that N varied between 66 and 108 participants. Information about the participants in the online-survey can be found in The survey gathered assessments on appropriate measures and penalties for hypothetical violations, as well as information on measures taken if violations occurred in the participants’ work environment in the past. The responses corresponding to appropriate measures in the event of a first and repeated violation showed that participants were in favour of intervening on-site (if possible) rather than punishing violations. Measures with greater financial or work-related consequences, e.g., losing the certificate of competence, tended to be evaluated as appropriate more frequently in the event of a repeated violation. The most common actions identified as appropriate were the following: Intervening and immediately correcting the incorrect working method/technique; informing the supervisor and/or animal welfare officer; conducting an oral briefing with the responsible person(s)  to conduct an oral briefing); and more frequent inspections in the affected area of the abattoir. These measures were identified as “basic measures” for an animal welfare violation at the abattoir, seeing as they were favoured by over 50.0% of participants in most cases, as demonstrated in the case shown in The data for all cases (N = 22) were analysed separately. Fines were calculated for violations which either constitute a regulatory offense according to Section 16 of the German Ordinance on the Protection of Animals in connection with Slaughter or Killing, or when more than 50.0% of the participants agreed that a regulatory fine was appropriate. All the ranges calculated can be found in the list of measures and fines in the As can be seen in Interviews revealed frequent animal welfare violations in the working environment of the individual interviewee. The most commonly mentioned violations were similar to the violations mentioned in literature . In some cases, OVs described systemic problems. Several OVs reported that, despite consistently documenting and reporting violations to veterinary authorities, measures were not taken and penalties not imposed in many cases. In addition, several interviewees described that, in many cases, they did not receive feedback on the course or the outcome of the procedure. This is coupled with a lack of support from management in veterinary authorities when violations were reported. Rather, abattoirs faced no consequences following violations. Furthermore, OVs described that they receive threats in abattoirs in response to informing them that they will report a deficit/an animal welfare violation. As a result, official staff may deliberately choose not to report a violation, because they fear personal and/or professional consequences. It is also important to note that OVs may issue measures directed to the future, such as an administrative proceeding based on Section 16a of the German Animal Welfare Act. A virtual colloquium was conducted with OVs to discuss the measures and penalties deemed appropriate in the online survey. The aim of this discussion was to evaluate the survey results in a practical context, and to improve them, so that they could be used as guidelines. Between 20 and 30 well-experienced OVs participated in the virtual colloquium on both days. The number varied because some participants entered the conference late and/or left early on both days. Participants of the virtual colloquium agreed that the median values should be used for the framework of fines because the median values do not consider the extreme values recommended by few participants. Furthermore, it was agreed that the values for the ranges of the regulatory fines for repeated violations should correlate to double the median values of the first violation. In some cases, the values were not just considered appropriate for a regulatory fine, but also for a periodic penalty payment. Additionally, the virtual colloquium clarified that animal welfare orders based on Section 16a of the German Animal Welfare Act (TierSchG) are frequently issued in order to tackle deficiencies in the abattoir in order to prevent them in the future. “Animal welfare orders” are administrative orders by the competent authority that can legally oblige an addressee to take certain actions/measures. This particular measure was assessed as appropriate rather infrequently in the online survey (on average by 10.7% of participants in the case of the first violation and by 29.3% of participants in the case of a repeated violation). Specific measures for each violation were added to the document which provides an overview of measures and penalties considered appropriate, if a participant described a measure to be appropriate based on legal norms and/or relevant professional experience. As previously mentioned, there were 22 cases in the survey and 40 cases intended for the final overview of measures and penalties deemed appropriate. Various cases are comparable in terms of severity and which measures/penalties are appropriate when they occur. We discussed which cases were comparable to the cases in our survey and then adapted the appropriate measures and penalties to the individual case throughout the course of the virtual colloquium. Thereby, a list of measures and penalties deemed appropriate was completed for 40 violations of animal welfare laws. The 40 violations discussed can be found in The measures and penalties considered appropriate by OVs were revised by two lawyers, who provided essential feedback in terms of correct legal terminology. There were no contradictory suggestions between the two lawyers. The objective of this research project was to compile a list of measures and penalties considered appropriate by veterinary and legal authorities for 40 animal welfare violations at German abattoirs. When applicable, past sanctions were summarized, including a description of the violation and the number of daily rates. The results were achieved by carrying out five important steps. The steps which included OVs were the following: An online survey, semi-structured interviews, and a virtual colloquium. Additionally, legal professionals were consulted, and relevant judicial decisions from Germany were collected and summarised. This project is the first step towards providing guidelines for OVs responsible for overseeing animal welfare in abattoirs. Providing such guidelines is important, because OVs bear a great responsibility to ensure that animal welfare offenses are appropriately punished . In GermAs for interpreting the results of this study, lawyers and OVs with experience in law enforcement in abattoirs should carefully review the list and identify whether the measures and fines suggested are adequate. However, the results of the online survey on their own suggest that animal protection laws at the abattoir are under-enforced by some OVs. Most of the violations were at least regulatory offences, but the average amount of participants suggesting an initiation of a regulatory offence proceeding after the first violation was only 18.9%. For the repeated violation, this percentage increased to 45.5%. We would have expected the percentage of participants recommending regulatory fines in the violations depicted in To our knowledge, this project is the first to present insights into which measures and penalties authorities deem(ed) to be appropriate for animal welfare violations in German abattoirs. Given the unprecedented methodology which was applied, the findings can hardly be compared to other publications. Some fines for regulatory offences from the year 2019 were published by the Administrative District of Kassel . For exaThe limitations of this study are the following: The data collected as part of the voluntary survey, interviews, and participation in the virtual colloquium cannot be validated. This is a common issue with these methods, which needs to be considered in relation to the benefits gained by using them. Participants were able to share their professional assessments and experiences in an anonymous and/or confidential setting. This is important considering this specific topic, seeing as there is media coverage on the particular subject of OVs not responding to animal welfare violations adequately ,42. It iAs for future directions, the next step in this project is revising these measures and penalties proposed by OVs into guidelines which clearly state which measures must be taken in the event of specific animal welfare violations. These findings can be used to develop clear guidelines regarding which actions can and must be taken for animal welfare violations, and the approach can be adapted and applied in other countries. This project compiled which measures and penalties responsible authorities would consider appropriate and which measures were taken in the past. Legal citations and summaries of relevant court decisions were included as well. This presents the status quo of law enforcement for different cases of animal welfare violations at the abattoir. The online-survey suggested that some OVs do not initiate the measures and penalties which the law requires them to, and thus may need further training in identifying animal welfare violations and taking the necessary actions. Revising these measures and penalties into a list of guidelines and recommendations  has the potential to promote a more consistent and standardised enforcement of the German Animal Welfare Act. This can contribute to the reduction of the number of animal welfare violations at German abattoirs.


The inherent stochasticity in transmission of hospital-acquired infections (HAIs) has complicated our understanding of transmission pathways. It is particularly difficult to detect the impact of changes in the environment on acquisition rate due to stochasticity. In this study, we investigated the impact of uncertainty  on nosocomial transmission of HAIs by evaluating the effects of stochasticity on the detectability of seasonality of admission prevalence. For doing so, we developed an agent-based model of an ICU and simulated the acquisition of HAIs considering the uncertainties in the behaviour of the healthcare workers (HCWs) and transmission of pathogens between patients, HCWs, and the environment. Our results show that stochasticity in HAI transmission weakens our ability to detect the effects of a change, such as seasonality patterns, on acquisition rate, particularly when transmission is a low-probability event. In addition, our findings demonstrate that data compilation can address this issue, while the amount of required data depends on the size of the said change and the degree of uncertainty. Our methodology can be used as a framework to assess the impact of interventions and provide decision-makers with insight about the minimum required size and target of interventions in a healthcare facility. Approximately 4% of hospitalized patients in the United States acquire an HAI during their stay, resulting in more than 1.7 million HAIs annually in the USA . The emeepistemic uncertainty, which refers to the uncertainty due to lack of knowledge or noise in data, and (2) aleatory uncertainty, which refers to the inherent variability in certain parameters. Epistemic uncertainty can potentially be reduced by collecting more data and improving the accuracy of data collection, e.g. improving estimates of prevalence of colonization on admission. However, aleatory uncertainty is intrinsic to the nature of events, e.g. length of stay or HCW hygiene compliance. Aleatory uncertainty is more commonly referred to as stochasticity, and increased data collection cannot reduce the inherent uncertainty in these variables. For clarity, hereinafter, we refer to aleatory uncertainty as ‘stochasticity’, and we use ‘uncertainty’ when referring to randomness, in general.The uncertainty regarding transmission of HAI-causing organisms can be categorized into two types: (1) et al. [Uncertainty analysis techniques have been developed to analyse the impact of uncertainties on system outcomes in parameters for which the true values are not known (i.e. epistemic uncertainty) ,5, whileet al.  showed tIn addition, simulations have shown that inherent stochasticity can cause dynamics similar to what external deterministic forces can cause. For example, when considering spatial correlations in epidemics, demographic stochasticity has been shown to be able to cause both small post-outbreak waves and major secondary outbreaks. This implies that in highly connected contact networks, recurrent epidemics can be due to endogenous stochasticity, rather than exogenous factors, such as seasonality or introduction of new variants ,9.One area that needs further investigation, particularly in the context of HAI transmission, is the effect of stochasticity on the detectability  of underlying deterministic trends (e.g. long-term continuous increase in antibiotic consumption leading to an increase in colonization susceptibility) and epistemic variabilities, such as seasonality in admission prevalence or the long-term impact of infection prevention and control interventions. Here, admission prevalence refers to the proportion of patients who are already colonized when admitted to ICUs (i.e. pre-admission colonization). For example, the impact of seasonality on the outcome may diminish when the amplitude of stochasticity increases, such that for a highly stochastic process, it may become difficult to detect the seasonal component . These issues are compounded when the event is a relatively low-probability event, such as an HAI.Data compilation (i.e. compiling data from multiple sources) is a common approach when dealing with highly stochastic events. While collecting more and better data generally does not reduce aleatory uncertainty, with highly stochastic events, such as acquisition of HAIs, compiling data from multiple sources can help to detect underlying deterministic patterns, such as seasonality, that may be concealed by strong stochasticity . Although data compilation can be a solution, given such data are available, certain precautions about spatio-temporal heterogeneities in the data have to be considered, particularly when studying the dynamics of disease transmission .One of the main principles of systems engineering is the necessity of understanding the underlying structure and dynamics of a process if one wants to implement effective changes. When it comes to complex processes such as nosocomial transmission of HAIs, this means we need to understand the underlying deterministic and stochastic drivers of pathogen transmission, and their interconnections, before we can decide what intervention would be effective and estimate or measure its impact after implementation. We need to quantify the effects of seasonality and possible trends in admission prevalence, which is arguably one of the main drivers of nosocomial transmission of HAIs. In simple words, how can we measure the impact of interventions if we do not know how many (or what proportion of) patients are already colonized when they are admitted to ICUs, and how this may change through time  and affect the effectiveness of interventions. If we see a decrease in infection rate, we need to know whether it has been due to an intervention, the effects of seasonality, or just randomness. Accordingly, investigation of the seasonality patterns in the drivers of nosocomial transmission of HAIs precedes the analysis of intervention impacts.In this study, we investigated the impact of sources of uncertainty  on nosocomial transmission of HAIs. Specifically, we evaluated the effect of stochasticity on the propagation of seasonality from admission to acquisition. We demonstrate how varying levels of stochasticity and seasonality could collectively affect the acquisition rate. Additionally, we demonstrate the amount of data needed to overcome the stochasticity effects and detect underlying deterministic patterns, which can improve our understanding of transmission pathways and would provide insight on targeted and more effective interventions to mitigate acquisition.. 2We developed an agent-based model of an ICU to study the effects of uncertainties on the dynamics of transmission of HAIs. We simulated the transmission of HAI-causing pathogens under different seasonality and admission prevalence scenarios, considering the uncertainties in the behaviour of the healthcare workers (HCWs) and stochasticity of the transmission of pathogens between patients, HCWs, and the environment. Finally, we trained a logistic regression to infer the parameter space in which seasonality effects is likely to be observed.. 2.1. 2.1.1We explicitly defined two types of human agents: patients and HCWs. HCW agents are further divided into two categories of nurses and physicians. Other HCWs, such as health technicians and nonclinical staff, were not included in the model as nurses and physicians constitute the majority of patient visits . PatientS), highly susceptible to colonization due to antibiotic use (X), colonized (C) and infected (I). Upon admission, each patient's disease state is assigned using a multinomial trial with defined probabilities for admission status distribution. To address the role of infection prevention, infected patients and those identified as colonized are put on contact precautions, which affects the interaction with HCWs and transmission potential (see Process overview).In the model, patients can be in one of four disease states: susceptible to colonization  has the same number of patients, the new patient is randomly assigned to one of the nurses (or physicians). The patient is then randomly assigned to an empty room, and its length of stay is randomly determined from a lognormal distribution. At admission patients are randomly selected for pathogen testing, and given the test accuracy (sensitivity), colonized patients may be identified and put under contact precautions. Contact precautions, as a type of transmission-based precautions, are a set of second tier infection control measures for patients with known (or suspected) infectious agents. Patients under contact precautions are placed in single-patient rooms, have limited movements and visits for medically necessary purposes only, and must use disposable or dedicated patient-care equipment. For all interactions that may involve contact with the patient or the patient's environment, personal protective equipment (PPE), such as gloves and gown, must be used .. 2.1.2.2At the start of every day, patients that have completed their stay are discharged. After a patient is discharged, the terminal disinfection protocol is executed in the room, which can clear the room contamination based on the room disinfection probability. Second, based on the bed utility rate, sampled for each empty bed (i.e. room), the number of admitted patients for the day is calculated, and new patients are initialized and assigned to their primary HCWs and rooms, as explained in setup. Afterward, the nurses' (and physicians’) daily visit schedules are determined by generating a random number of visits that each patient receives from nurses (and physicians) during a day (24 h). Patients under contact precautions receive fewer visits from the HCWs . While e. 2.1.2.3The simulation is run for 360 days with a one-hour time step. The simulation is segmented into four 90-day quarters where the third quarter is the high season during which admission prevalence is higher . Every hDuring contacts with the patient, if the HCW or their PPE is contaminated, the pathogens may transmit and colonize the patient. Similarly, a colonized patient can contaminate the HCW or their PPE. It is assumed that after contacting the patient, the HCW contacts the environment again, which may contaminate the environment itself. However, if the PPE becomes contaminated before the HCW leaves the room, this will not increase the risk of transmission as the PPE will be discarded. The HCW may comply with hand washing when exiting the room, based on the hygiene compliance probability on exit. Environmental contamination and patient colonization occur based on probability of environmental contamination by a contaminated HCW βph=2βhp . To accosmission .. 2.1.2.4A patient's colonization status may deteriorate into an infection based on a probability of infection from colonization. This is implemented using a Bernoulli trial every day with a success probability equal to the probability of infection. When a patient becomes infected, they will be put under contact precautions and a 7-day course of antibiotic treatment, which may extend the patient's length of stay. The antibiotic treatment period may extend over seven days until the patient clears the infection with a negative culture test. After clearing the infection, the patient will be removed from contact precautions.. 2.1.2.5Active surveillance is conducted once every 7 days on selected patients starting from day 1 in every simulation. Given the test accuracy (sensitivity), if a patient's test is positive, the patient will be put under contact precautions for the rest of their stay.. 2.1.3The model includes HCW-mediated and environmental transmission during HCW visits. HCW-to-HCW transmission was not included. Patient-to-patient transmission was not considered as patients are highly unlikely to have direct interactions with other patients in ICUs. We assumed that it is more likely for a HCW to become contaminated from a colonized patient than for a susceptible patient to become colonized from a contaminated HCW, as it is easier for pathogens to transfer to hands (of a HCW) than to establish within the microbiome of a patient and cause colonization . We assuPatients that have been or are receiving antibiotics have been shown to be more susceptible to colonization, particularly with MRSA ,17. The . 2.2We defined the constant parameters and the statistics of random variables of the model based on the findings from the literature. While the mean probability of transmission and seasonality are assumed not to vary across ICUs, other parameters can vary for different ICUs, the probabilities of which are specified in XP) and colonization importation (CP) ratios. After sampling CP and XP from their respective distributions, the probability of admission as susceptible, assuming no infection importation S, PX, PC, PI})Multinomial  during a HCW–patient contact are the probability of environmental contamination by a colonized/infected patient, the probability of transmission from a contaminated HCW to a susceptible patient, the probability of transmission from a colonized/infected patient to an uncontaminated HCW, the probability of HCW contamination from the environment, and the probability of environmental contamination from a contaminated HCW. Other parameters, such as hand hygiene, PPE compliance, and number of visits per day, affect the overall probability of transmission through time. In total, the random variables of the model, represented using distributions listed in ct is the peak day of the high season , and We modelled the seasonality effect as an increase in admission prevalence of colonization during the high-season period using a Gaussian-modulated sinusoidal function to generate a Gaussian pulse:U-test [U-test provides a more robust assessment than the Student's t-test typically used for hypothesis testing.The seasonality impact on quarterly acquisition rate  is assessed using the Mann–Whitney U-test . Since tU-test may or may not show seasonality in acquisition rate across simulations with similar parameters. As the amplitude of the seasonal signal in admission prevalence and the baseline acquisition rate increase, seasonality effect in acquisition rate is magnified and is more likely to be detected. We used a logistic regression predictor to define the parameter space for which the seasonality effect is most likely to be detected. The logistic regression predictor is trained with seasonality strength of admission prevalence and baseline acquisition rate as the predictor variables and the binary outcome of the U-test as the response variable.There is a potential relationship between the detectability of seasonality in acquisition rate, the seasonality strength of admission prevalence, and the baseline acquisition rate. However, because of the uncertainties in transmission, this relationship is not deterministic, so the . 2.4The main outcome of interest is quarterly acquisition rates, in terms of number of acquisitions per 1000 patient-days. To evaluate the combined effect of uncertainty in transmission and seasonality on acquisition rate, 1000 transmission-seasonality scenarios were created by sampling the mean probability of transmission and seasonality strength from their respective distributions using Latin hypercube sampling (LHS), assuming that mean probability of transmission and seasonality strength do not vary across ICUs. For each scenario, a single ICU was simulated, and the simulation was repeated 300 times using Monte Carlo methods. The number of simulation repetitions (i.e. 300) was determined by sampling error minimization, which showed that the distribution statistics (sample mean and standard deviation) converged when sample size was 300 or larger . In other words, repeating ICU simulation for each of the transmission-seasonality scenarios for more than 300 times does not add more information to the mean and variance of model outputs, i.e. acquisition and infection rates. Each ICU was simulated for 360 days after a 60-day burn-in period. An overview of the methodology is illustrated in . 3. 3.1At baseline, with no seasonality effect, the mean acquisition rate was 45.55  cases per 1000 patient-days. The 95% highest density interval (HDI) of acquisition rate extends from 30.55 to 62.05 cases, which agrees with the literature ,36,37. T. 3.2The effect of importation seasonality on the acquisition rate depends on the likelihood of transmission. Importation seasonality may not have a significant effect on incidence when transmission is a low-probability event .Figure a). For admission prevalence between 5 and 10%, seasonality effects could only be detected when the seasonality strength was more than 40% (figure 4b). Similar patterns were observed for higher admission prevalence values (10 to 15%). Seasonality effects were detectable when the seasonality strength was more than 25% and acquisition rate was more than 30 cases per 1000 patient-days. As acquisition rate decreased, the minimum detectable seasonality strength increased such that for acquisition rates below 5 cases per 1000 patient-days, the minimum detectable seasonality strength was 40% (figure 4c). The accuracy of the logistic regression predictor of the detectable seasonality region  to evaluate the contribution of variability of input parameters to the variability of outcome variables. The results show that the stochasticity in acquisition rate is mainly due to the stochasticity in admission prevalence, probability of transmission between HCWs and patients, and probability of environmental contamination or colonization by HCWs and patients. While for infection rate, admission prevalence and the probability of progression (from colonization to infection) mainly contributed to stochasticity. Refer to the supplementary materials for details.. 4The distinction between epistemic and aleatory (stochasticity) uncertainties has important implications in understanding the transmission of HAIs, as it helps to identify the sources of uncertainty which are potentially reducible. Our simulations demonstrate that the inherent stochasticity in HAI transmission weakens our ability to detect the effects of deterministic forces, such as seasonality, on the acquisition rate. From a broader perspective, the amount of stochasticity is a critical driver in our ability to detect the impact of an intervention on reducing the acquisition rate from baseline. Here, we used seasonality in admission prevalence as an example of a (short-term) deterministic change to the dynamics; however, our methodology and findings are intended to represent a framework for evaluating the ability to detect the impact of interventions intended to alter the acquisition rate of HAIs.The core message of our findings is that due to the highly stochastic nature of HAI transmission, the impact of changes in the environment  may not be readily detectable unless the size of the change is larger than a certain threshold (which depends on the likelihood of acquisition) or more longitudinal or cross-facility data are compiled to enable a more statistically reliable estimation of the impact. The fact that stochasticity is such an important feature of transmission of HAI-causing pathogens helps to explain why interventions may have different results through time and location. The underlying stochasticity in transmission of HAI-causing pathogens also makes the impact of a change stochastic. In other words, it is difficult to understand the characteristics of a random event with a highly irregular probability distribution by taking only a limited number of observations. For example, for an infection control intervention that is implemented in one or more ICUs, the corresponding impact on acquisition rate may vary across the ICUs or for any single ICU through time. For evaluation purposes, longitudinal data from all the ICUs should be analysed using uncertainty analysis techniques, such as our proposed framework, to understand the probabilistic impact of the implemented intervention.As our simulations suggest, to overcome the seasonality detection problem, we need to collect a greater amount of data. Our framework presents a mechanism to assess the amount of required data to detect the impact of changes in the environment on the acquisition rates in ICUs. For example, our simulations show when the baseline acquisition rate is lower than 20 cases per 1000 patient-days, the impact of seasonality in admission prevalence on the acquisition rate is more likely to be detected when the seasonality strength is at least 30% . More accurately, our framework can be used to estimate the probability distribution of the potential impact of environmental changes  increased, the impact of the interventions became more statistically detectable . As an eUnderstanding the amount of data needed to overcome the stochasticity effect and detect the impact of interventions on lowering the acquisition rate is crucial for deciding what types of interventions to invest in. This is not hospital or ICU independent but depends crucially on the epistemic uncertainty in operations at the healthcare facility. Greater data capture of facility operations, including items such as HCW–patient contact rates, colonization prevalence, and handwashing adherence and effectiveness, can reduce the epistemic uncertainty, which may reduce the data compilation needs. For example, based on the uncertainty in the model and the inherent stochasticity in the system, our simulations suggest that data from at least 50 ICUs would be needed to detect seasonality effects with statistical robustness. However, the ICUs in this case were fairly generic representations of actual ICUs. Though based on data from actual hospitals, there was still a significant amount of uncertainty in many parameters, such as the probability of room and hand colonization and even the rate that nurses visit patients. Development of a more accurate synthetic population would reduce the epistemic uncertainty, and studies that can provide greater understanding of the actual colonization prevalence would likely reduce the required number of ICUs needed to detect a change in the underlying dynamics. Future work will assess the potential of utilizing hospital specific data to better understand the requirements for detecting the impact of interventions.In conclusion, our findings help explain why certain interventions show inconsistent results through time or across different ICUs. In addition, our framework can be used to provide decision-makers with insights about the minimum required size of interventions given the estimated rate of acquisition in a healthcare facility. The framework can also be used to understand which data elements have the greatest impact on epistemic uncertainty and can be leveraged to reduce the threshold for intervention detection. Furthermore, it can help justify  the application of simpler deterministic macro-scale models, such as compartmental models, to study transmission at the community–hospital interface and evaluate the long-term impact of interventions, given the level of stochasticity in the system. The compartmental susceptible–infected–recovered (SIR) models have been extensively used to study the transmission of infectious diseases. While these models are great tools that are simple to understand and use, they disregard the effects of stochasticity and heterogeneity by assuming deterministic parameters and homogeneous populations. Therefore, they are not suitable for the study of HAI transmission at the facility level due to the underlying high degrees of stochasticity. Our framework can be used to inform the scale (number of ICUs) at which the collective patterns in HAI data converge , hence, potentially enabling the application of simpler macro-scale deterministic SIR models.


Unilateral spastic cerebral palsy (USCP) is caused by damage to the developing brain and affects motor function, mainly lateralized to one side of the body. Children with USCP have difficulties grasping objects, which can affect their ability to perform daily activities. Although cerebral palsy is typically classified according to motor function, sensory abnormalities are often present as well and may contribute to motor impairments, including grasping. In this review, we show that the integrity and connectivity pattern of the corticospinal tract (CST) is related to execution and anticipatory control of grasping. However, as this may not explain all the variance of impairments in grasping function, we also describe the potential roles of sensory and sensorimotor integration deficits that contribute to grasp impairments. We highlight studies measuring fingertip forces during object manipulation tasks, as this approach allows for the dissection of the close association of sensory and motor function and can detect the discriminant use of sensory information during a complex, functional task . In addition, we discuss the importance of examining the interactions of the sensory and motor systems together, rather than in isolation. Finally, we suggest future directions for research to understand the underlying mechanisms of grasp impairments. Cerebral palsy (CP) is a neurological disorder caused by a prenatal or perinatal injury. It is the most common pediatric neurological and physical disability ,2. UnilaMost research is focused on the behavioral characteristics of grasping in USCP. However, to understand the mechanisms of the impairment and to improve rehabilitation and upper extremity function, it is important to look at the relationship between brain structure and function. In this review, we aim to summarize our current understanding of the relationship of the development of various tracts in the brain and grasp function. Specifically, we will focus on studies on fingertip force control during precision grip, and the neural correlates once contact with the object is achieved. Precision grip, involving grasping between the thumb and forefinger, is a hallmark of human motor behavior, and is essential to manipulate small objects and skilled tool use. The study of this skill provides a sensitive method to quantify the contribution of the motor and sensory systems to a functional task. The paradigm was first used by Johansson and Westling in a series of studies starting four decades ago to examine cutaneous afferent input during grasp ,8,9,10. We will discuss studies on the integrity of the corticospinal tract (CST) and its relationship with grasp impairments in children with USCP. The CST, which is the primary neural pathway for voluntary digit control, is important for the grasping of objects e.g., ,16). Yet. Yet16])Animal studies have demonstrated that the control of grasping primarily involves the integration of sensory input with motor output, where the process is mediated by the connection of the prefrontal cortex with parietal areas . In the Precision grip lifts in human studies also demonstrate the importance of the motor cortex, as it is shown that the strongest amplitudes of electromyography (EMG) responses are right before the grasp of an object, and on the onset of the manipulation of the object . Damage The study of fingertip forces during precision grip has been instrumental to our understanding of sensorimotor control of prehension. These studies require subjects to lift an instrumented object. Successful manipulation requires the interplay between anticipatory (motor planning) and feedback control. Visual information is used to identify previously manipulated objects or estimate the required fingertip force scaling based on size, shape, and density cues to drive the manipulation of the object ,43,44,45During typical development, the CST approaches the spinal cord by the 20th week of gestation  and contCP is the most common physical and neurological childhood disability, affecting approximately 2 per 1000 births , difficuThe relationship between these motor and sensory upper extremity impairments has been explored as well. For example, better sensory function related to greater unimanual capacity, as measured through the Melbourne Assessment (MA), which includes a grasp component . LikewisPrecision grip impairments are present in children with USCP  found a relationship between poorer sensory function, as measured through stereognosis and less adaptation of static GF during the precision grip task . FurtherChildren with USCP display anticipatory control impairments during grasp  lesions, which occur in the early third trimester of pregnancy and affect the white matter ,80. The Though lesions in the developing brains of children with USCP can cause a disruption of development, the CST is capable of considerable reorganization after damage . There aDamage to the CST in relation to grasp impairments have been measured in a variety of ways. For example, structural damage has been measured by examining the ratio of the size of the asymmetry of the cerebral peduncles (the more affected peduncle area divided by the less affected peduncle area × 100), in which the CST passes through ,16,87,88Diffusion properties of the CST are also related to manual dexterity ,90, and In addition, transcranial magnetic stimulation has been used to examine the overlap of the primary motor cortex motor representations of both hands . ModeratGrasp impairments relate closely to CST damage, as investigated through performance on a precision grip lift task ,16. For Children with PV lesions have better manual dexterity than those with MCA lesions ,94. SimiCST connectivity patterns, determined by transcranial magnetic stimulation and/or functional magnetic resonance imaging, have shown to contribute to grasp function ,69,95. AIn children with USCP, the somatosensory system has a limited potential for reorganization after injury compared to the CST, as reorganization occurs in the ipsilesional hemisphere and does not reorganize to the contralesional hemisphere ,97. In tStructural connectivity of the DCML pathway and thalamocortical pathways are shown to relate to grasp function ,94,98,99Although many studies have examined sensory and motor tracts in isolation, others examined them simultaneously. However, it is important to examine these tracts together, to gain a better understanding of the contributions of each. Overall, there is conflicting evidence on which tract relates more to grasp function. For example, the presence of somatosensory tracts have a stronger relationship with grasp function in comparison to the absence or presence of the CST, as shown by the larger effect sizes . This inChildren with USCP may have an inability to appropriately integrate somatosensory information with the motor output of the same hand. Therefore, while they may be able to form an internal representation of the object, the ability to establish the sensorimotor associations used with the motor output in the more affected hand may be impaired. This in part may be due to that for children with ipsilateral CST connections, their sensory information is from the maintained contralateral sensory tract, but they have ipsilateral CST connections (interhemispheric sensory-motor dissociation) ,20. In tThe interhemispheric sensory-motor dissociation and the CC are shown to relate to grasp function. The dissociation of sensory and motor tracts can impact motor impairments ,19,20,21One limitation of previous research in understanding underlying mechanisms of grasp impairments is that the studies involving the integrity of sensory tracts solely used clinical measures. There may be a dissociation between sensory perception and utilization. Furthermore, these tests require the ability to understand the instructions and sufficient attention to detect subtle discrimination levels, and, thus, may not be appropriate for young children or children with cognitive or attention deficits. No studies to date have examined the relationship between integrity of sensory tracts and modulation of fingertip forces during grasping, which is shown to be a reliable, objective, and sensitive measure to examine utilization of sensory information during grasp. This is important as although it is speculated impairments in adaptation of fingertip forces is due to sensory feedback ,6,13,60,There may also be a differentiation in motor planning versus execution in relation to different brain areas. While the CST is shown to be associated with anticipatory motor control impairments ,16, sensFinally, it has been demonstrated that children with USCP can transfer sensory information signaling object weight from the more affected hand to the less affected hand for use in anticipatory control . TherefoWe described sensory, motor, and sensorimotor integration impairments in children with USCP in relation to grasping, with an emphasis on precision grip studies. We also described developmental differences in the sensory and motor systems. It was demonstrated that the integrity of the CST tract and symmetry of the CST are related to grasp impairments in children with USCP. It was also shown that while there are no studies to date examining the sensory tract and grasp impairments using the precision grip task, children with USCP share many of the same grasp characteristics of adults with experimental suppression of afferent information. Further, we examined the interactions on grasp function between the motor and sensory systems when the tracts are in different hemispheres. The interactions between the motor and sensory systems are complex and it is likely they both play an integrative role in grasp impairments in children with USCP. In addition, it was determined that the CC is important for the transfer of sensory information and that children with USCP can transfer anticipatory control of forces of object weight from the more affected hand to the less affected hand. This implies that anticipatory control impairments in children with USCP are due to sensorimotor integration problems. Overall, it is shown that the development of motor tracts, sensory tracts, and CC play a role in grasp function in children with USCP. Therefore, although children behaviorally display what looks like somatosensory impairments causing insufficient internal models and motor output impairments, the inability to integrate the somatosensory input with the motor output may be what is contributing to anticipatory planning impairments of grasp in children with USCP.


Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest.This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group.In the analysis of the 145 included studies, support vector machines were the most utilized ML technique , with the most studied disease being Parkinson disease . After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network–based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models.This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research. Voice and speech production relies on complex and multiorgan cooperation. The basic mechanics of speech and voice creation is that the airflow obtained by releasing the pressure in the lungs reaches the vocal folds in the larynx and vibrates the vocal cords that result in voice, and by articulating this voice speech is created . The harThe anomalies and the absence of vocal quality in relation to pitch, height, resonance, and duration, which are unexpected for individuals, regardless of their gender and age are characteristics of a disordered voice -5. ThereMeasurable, reliable, and repeatable assets that can be correlated with a clinical outcome are defined as biomarkers. The criteria and context of use describe the category of biomarkers such as diagnostic, monitoring, pharmacodynamic/response, predictive, prognostic, and digital biomarkers . TraditiAs aforementioned, voice and speech can be influenced by several conditions and disorders, which contribute to decreased quality of life. Nevertheless, being so sensitive could open possibilities for earlier diagnosis of disorders that affect the voice through the use of voice as a biomarker . As the Most health problems could benefit from an early diagnosis for better treatment and management of outcomes. However, the growing pressure on health care systems, due to the increased life expectancy and an aging population, may hinder this early detection. Patients are usually referred to specialist care only when apparent signs of disease are present, and thus are at an already moderate advanced state. Fortunately, the existence of digital biomarkers , along with the trend of digitalization in health care, opens the possibility of using technologies such as machine learning (ML) to address these issues. Research on biomarkers extracted from voice and speech with ML techniques for diagnosing, prognosticating, and monitoring disease  has showML techniques are becoming prevalent in health care for aiding decision-making in treatment and diagnosis . These tPrior studies provide comprehensive information on feature extraction and its application -25. EncoClassification Manual for Voice Disorders [This SLR investigates state of the art of clinical applications of voice-based diagnosis that make use of ML algorithms. Adapting voice-based diagnosis and prognosis into clinical practices requires solid evidence and research to clinically validate the usability and reliability of voice biomarkers and the performance of ML classifiers ,15. Thisisorders . These hAn SLR is a summary of the results from research papers focused on a common context or a question. The summary action includes the identification, collection, assessment, and synthesizing of high-quality research evidence within the scope of the research question by following a predefined protocol. The aim of an SLR is to provide perspective on recent research so that decision makers can benefit from up-to-date knowledge and address the gaps that can be used as a basis for new research. The predefined protocol describes the methodology to follow; defines the research question; and contains information about inclusion/exclusion criteria and quality assessment . This seSQ1: What are the aims of pathologic voice evaluation?SQ2: Which ML techniques are being used for the diagnosis and monitoring of voice-affecting disorder through voice and which voice-affecting disorders are being investigated?SQ3: What are the time and geographical trends of publications in the scope of SLR?SQ4: What are the data characteristics of the sound samples for different disorders and types of studies?SQ5: Are the studies cross-sectional or longitudinal?SQ6: How is performance being evaluated in the studies?All the information on the methodological approach that guided the execution of this SLR is based on the prespecified SLR protocol .population, intervention, comparison, and outcome (PICO) framework [comparison due to the nature of this SLR being a characterization:A search string was constructed by applying the ramework -34. The Population: Disorders that affect the voice, given by the Classification Manual for Voice Disorders [isorders , referriIntervention: Use of ML techniques for the diagnosis or monitoring of disorders through voice samples.Outcome: Reported quantities or results such as precision and accuracy.The search string was adapted based on the advanced search requirements of each database. The filter options were tuned to retrieve articles from January 1, 2012 to December 31, 2022. The period was chosen after consulting with experts in the medical field with regard to the development of new technologies for health care. The development of the search string was primarily based on the MeSH  terms, with the help of a librarian, and categories of voice disorders in the classification manual . AND  AND  AND  AND  )The search string was used to perform an automated search on each database. The Zotero  bibliography software was used to collect all relevant articles from all 3 databases and to remove duplicates . First, All articles in the group of full-text readings underwent a quality assessment procedure to assure high-quality evidence , based oInclusion criteriaJournal studyPrimary study written in EnglishResearch published not earlier than 2012Research that uses voice as the input dataResearch that employs at least one machine learning algorithmResearch that aims to diagnose or monitor at least one voice-affecting disorder not related to the systematic conditions affecting voice, nonlaryngeal aerodigestive disorders affecting voice, and neurological disorders affecting voiceExclusion criteriaA nonpeer-reviewed studyResearch written in languages other than EnglishResearch published before 2012 or after 2022Research that does not use voice as a direct input, which means research employing various nonverbal forms of data input, such as written transcriptions, digital images, videos, electroencephalogram, and signals generated during vocalizationResearch that classifies voice-affecting disease without a machine learning approachResearch that classifies voice disorders related to conditions other than systematic conditions affecting voice, nonlaryngeal aerodigestive disorders affecting voice, and neurological disorders affecting voiceGeneral questionsAre the aims clearly stated?Is the targeted population described?Has it discussed the contribution of the study?Are gender and age considered?Is/are the technique(s) being implemented clearly described?Data analysisIs the origin of data given?Is the type of data clearly described?Do the data consist of voice recordings?Is the data validation method given?Is there a discussion on whether the data size can be generalized for the targeted population?ResultIs/are the result(s) clearly discussed?Are all aims or questions answered?Was the outcome related to the target population?Are the limitations discussed?Did results compare with previous rapports?Data extraction was carried out by author AI. To analyze the etiology of changes over time and capture the heterogeneity, the studies were grouped into subgroup summary tables entitled with the name of disorders see . NumericThe PRISMA  group, with being “healthy” defined as people without a diagnosed disorder, and a group with known pathology ,48-168. The usage of the 12 ML techniques is shown in This section describes the characteristics of voice and nonvoice data used as input into the ML models. A total of 11/145 (7.6%) studies integrated nonvocal data in conjunction with vocal features to form the input feature sets for the ML models; 6 of these studies ,149,163 In all studies, raw data that consist of recordings underwent signal processing to extract features that were used as input data into ML techniques. Identified signal processing implementations were baseline acoustic (BLA), Mel-frequency cepstral coefficients, tunable Q-factor wavelet transform, wavelet transform, and spectrogram, which are frequency-transformed versions of the input signal that generates features in the form of digits and images. Other utilized features were linguistic and vocal features that generate statistical outputs . In addition, combining several vocal features is more popular than only using BLA features, where almost 69/145 (47.6%) of the studies combined several features as input. But still, BLA features  are one of the most separately used feature sets. In this SLR BLA corresponds to all or a portion of acoustic, time, and frequency domain features calculated from raw recordings .A total of 70 studies collected data for a specific study and 75 studies gathered data from an available data set. Sakar et al (2013)  and SakaSQ5 aims to find out whether studies rely on longitudinal data and observation over time or observations at the same time that study was done. As the authors predefine the participants and measure the exposures and outcomes at the same time in all included studies, all studies in this SLR follow the cross-sectional study design .F1-score, recall, mean absolute error, R2, positive predictive value, and negative predictive value were other used measures in combination with accuracy without any standard order. Under the performance column in Measures presented to assess the efficiency of the ML techniques used show diversity in the included articles. Accuracy is one of the most used measures to present the outcome of almost all studies. Sensitivity, specificity, precision, Matthew’s correlation coefficient, area under the curve, Two groups of studies ,73 from In this SLR, 10 years of research on ML techniques applied for diagnosing and monitoring voice-affecting disorders indicates an extended interest from many countries. It seems that researchers have focused mostly on the detection of 19 identified disorders with low number of individuals in data sets that lead to gaps identified as the main findings of this SLR. These are summarized below:Most studies aimed to perform a diagnostic test through the detection or classification of disorders, and only a few studies aimed to monitor a specific disorder.PD was the most investigated disorder among all 19 voice-affecting disorders.There was a broad interest from many counties.Data sets used in studies were unbalanced, and most studies collected their data without providing open access. Additionally, only 11/145 (7.6%) included studies considered using additional data in conjunction with voice features.All studies were cross-sectional.Accuracy was the most common metric for the overall performance evaluation.The majority of the studies focused on the detection or classification of the 19 identified voice-affecting disorders through emerging ML techniques. However, it is important to also consider the need for continuous monitoring of these disorders to improve the quality of life for those affected. Another consequence of focusing solely on detection is that it may not provide enough information about the severity of the disorder, which is a vital measure for decision-making on treatment or determining correct dosage for medication. Therefore, to improve the applicability of findings in clinical practices, it may be beneficial to navigate the focus of research toward methods for monitoring the progression, which involve severity measures of voice-affecting disorders.Verdolini et al  give an Based on the origin of the publication and the origin of the data sets, a wide interest from many countries was observed. However, many countries conduct research on the same data sets, which can lead to both positive and negative results regarding the clinical applicability of outcomes. Concentration on a group of data sets may increase the performance of the ML technique for the represented input data attributes. By contrast, it may also introduce limitations for the nonrepresented or underrepresented data. For example, the UCI data set in Sakar et al  containsA balanced data set means the numbers of samples are relatively equal between classes, giving equivalent contributions from all classes during training, which eventually improves the performance of the ML technique on new data. By contrast, imbalanced data can lead to bias. The results of our SLR show that using balanced data has not been considered in studies. As the voice is used as a medium to detect a disorder, it is important to consider the effect of linguistic diversity, gender, age, and other sociodemographic differences on the generalizability of a system. Training and testing an ML technique on balanced data offer higher reliability for use in clinical practices. Balancing data based on different characteristics may be another option for higher reliability . The studies included in the analysis provided demographic information about their respective data sets. However, only a limited number of studies incorporated this information into the vocal feature set that use additional nonvoice data for training the ML models. Integrating the demographic data into the automated process of data set preparation could prove beneficial, as opposed to the manual preparation of data sets based on disparate attributes. Additionally, combining multiple sensory inputs along with vocal features may further enhance the performance of the ML algorithms. However, this practice appears to be infrequently observed in recent studies.Results of this SLR showed that 70/145 (48.3%) studies collected data specific to the research without making them publicly available. It is observable that PD is one of the most investigated disorders. That might be a result of publicly available data obtained from the UCI Parkinson data set repository. It is worthwhile to extend publicly available data sources with varied voice-affecting disorders and features to preserve research reliability and homogeneity in the scope. Another aspect that would influence clinical applicability is the small number of participants being considered in the research. Increasing the number of participants might increase the reliability of ML techniques.In SLRs, “longitudinal study” refers to a recurrent sample taken from the same participant over time, which is a way of following the progression and trend of a disorder that helps to identify the patterns and causal relationships. Conducting a longitudinal study may even help to reduce the confounding variables .The absence of longitudinal studies makes it difficult to conduct an epidemiological trend analysis of time effects on digital features extracted for ML techniques to diagnose and monitor voice-affecting disorders. All included studies in this SLR considered cross-sectional analysis, which does not represent the possible divergence tied to the progression of a specific disorder and individual. Therefore, longitudinal studies are essential to discover the voice changes over time ,192.The majority of the studies chose to represent the performance in accuracy, specificity, and sensitivity metrics, which were tied to the overall classification performance in research on voice-affecting disorder diagnosis and monitoring. In this SLR, only 8 results indicated that gender and language diversity may affect the performance in terms of accuracy ,170-172.The decision to include studies published only in English is a risk of missing important evidence in other languages, which at the same time is an unavoidable limitation for the generalization of this SLR. Another factor that can be considered as a limitation is only including peer-reviewed studies, which do not consider conference papers. Relying on the conference papers can be problematic due to the limitations including the potential for incomplete or preliminary results . AdditioUnderrepresented monitoring purposes, research on a low number of voice-affecting disorders, unbalanced data, limited public voice data, lack of longitudinal research, and performance evaluation without paying attention to diversities were 6 gaps addressed in this SLR, which may be considered in future research. We suggest the following:One research direction may be to include disorders that were underrepresented in the state of the art. It is essential to take the gaps into consideration, such as working with balanced and extended data sets, to generate more reliable results.Conducting cross-sectional and longitudinal studies to identify specific digital features that are associated with voice-affecting disorders can be beneficial for determining the severity of the disorder and monitoring it over time.Studying the effects of demographic characteristics, such as gender, age, linguistic factors, and other relevant additional data on the classification models may also provide insights for building more accurate ML techniques for specific disorders.Through the methodology of an SLR, we identified 145 studies on the use of voice for diagnosing or prognosing disorders, by the means of ML algorithms. These studies were summarized in terms of many aspects, including disorders and conditions that affect the voice, characteristics of the input data, ML techniques used for voice-based diagnosis, and research interests from countries. The findings of this SLR indicated that most of the studies are concerned with the detection and classification of investigated disorders and conditions based on cross-sectional studies. This study also found gaps in the literature, such as the usage of unbalanced data sets, lack of longitudinal studies, research not addressing nonvoice data in the voice studies, and most voice-affecting disorders in the interest of this study being underrepresented in research. Research in the field of voice-based diagnostics with the utilization of ML is making the practical application of this technology in health care more achievable. The use of voice as a digital biomarker could open the possibilities to large population screening of many disorders in a low-cost, noninvasive, and scalable way. To implement such a system in a clinical setting, the exploration of unknown aspects is an essential process to proceed with. To do that, it is necessary to extend the research on all possible voice-affecting disorders and identify the nuances between all different voice-affecting disorders and their effect on vocal features. Currently, research in this field primarily focuses on detection using a limited number of participants. However, for more generalizable results in the future, research may not only consider increasing the participant numbers but also maintaining a balance among them and identifying the measures that can be used for monitoring purposes.There is a broad research interest from many countries, which creates a potential for observing the effects of cultural and language differences on ML algorithms. However, contribution to data collection and increasing the size of available data with diverse characteristics are crucial steps that each country might consider.