Mental health service managers must take into account multiple factors when making decisions about the best way to deliver care to clients across increasingly larger service areas. This task is made more difficult by the lack of evidence and tools historically available to inform these decisions. In recent decades, the increasing availability of epidemiological and service use data for mental illness has solved the problem of evidence, but there still exists a challenge to make these data easily accessible and understandable for managers.This study aims to develop a simulation modeling tool to allow managers to explore various service configurations in virtual reality, enabling predictions to be made about the cost and quality of care.This is a longitudinal, mixed-methods case study, comprising overlapping intervention and evaluation phases. In partnership with senior managers of a mental health program, the researchers will develop a series of simulation models in Arena to address key strategic issues facing the service. Thematic and content analyses of semistructured interviews, meeting observations, and document analysis will be used to evaluate the process of model implementation and the outcomes for both researchers and managers. The study is being conducted in Australia.Data collection has been ongoing since late 2013. To date, 3 prototype simulation models have been developed and presented to senior managers, and 18 evaluation interviews have been conducted. The project is expected to conclude in late 2018.Findings of this study have the potential to shape decision making in mental health service delivery, by providing key examples of how to integrate patient data using simulation modeling. In addition, the results will provide key insights into how researchers and consultants can effectively implement simulation modeling in real-world health care organizations.RR1-10.2196/11119 The health care sector is characterized by complexity, where balancing the demands of multiple stakeholders in geographically disparate areas makes the task of service-wide strategy planning extraordinarily difficult ,2. In meIn the clinical space, this complex environment is managed through the use of evidence-based practice  and clinSimulation models are simplified abstractions of real systems, often created on a computer. They allow users to predict future states by tracking changes in the system over time, with these changes determined by attributes assigned to individuals or entities (agent-based modeling), time-specific state transitions (Markov models), events (discrete event simulation), or system flows (system dynamics) . SimulatHowever, little direct evidence is provided to support these claims of improved decision making outcomes. This is due to a general lack of reporting on the implementation of simulation models, with multiple reviews of health care simulation highlighting this as a key problem facing the literature -18. IndeHence, this paper aims to describe the protocol for the development and implementation evaluation of a simulation model depicting the real-world activities of an Australian public mental health service (MHS).The primary aims of this study are (1) to develop a sophisticated health care management decision support tool and bring it into practical use by managers of MHS as they go about service reform and redevelopment and (2) to evaluate the effectiveness of this decision support tool in improving the process and outcome of strategic decision making by MHS managers.The intervention and evaluation follow an iterative, mixed-method design. The intervention and evaluation timelines are staggered, but intentionally overlap, to allow evaluation results to inform refinements to the intervention in the latter stages of the study. The intervention was designed and overseen by GNM, and the evaluation was designed and conducted by KML.The intervention has 4 major phases: (1) development of a conceptual framework for the simulation model; (2) integration with simulation software; (3) validation of the model; and (4) implementation of the model within the MHS . In the The evaluation design is a longitudinal, mixed-method case study that parallels the intervention. The analysis focuses on 2 levels: outcome and process.Outcome will be measured by changes in mental models, reflecting increased decision process agreement and increased similarity to the rational decision-making model . In addiThe process will be assessed by group changes in behavioral and linguistic patterns during the intervention workshops, reflecting increased similarity to the features of good group decision-making processes . These oThe research was conducted with the cooperation of the senior leadership group (SLG) of a major public MHS in Australia. The MHS provides government-funded inpatient and community mental health services across the age spectrum, with different, but overlapping, catchment areas for Early in Life Mental Health Services (<25 years), adult, and aged (<65 years) services. There are 3 operational service groups, Early in Life Mental Health Services, community services, and bed-based services, and 3 primary hospital sites, which were added to the organizational chart in 2016. The MHS employs approximately 800 staff members who provide approximately 250,000 client contacts per year, at a total cost of Aus $125 million, 8.0% of the health provider’s operating expenditure.Strategic decision making for the MHS lies with the SLG. Members of the group attend monthly meetings as representatives of their clinical specialty , operational units, administrative units (finance and human resources), and allied research/university groups. The membership of the SLG includes the Chief Investigator (CI) and an Associate Investigator of the intervention project, who brokered access to the group.At the start of the evaluation project, off-the-record interviews were conducted by KML with organizational gatekeepers  to gain a basic understanding of strategic decision making in the MHS. In addition, the Executive Director invited the evaluator (KML) to brief participants on the project (October 2013) and informally observe a senior leadership meeting (November 2013).The SLG emailing list was then used to invite participants to workshops and interviews; this ensured that data were collected only from active decision makers and members of the SLG. All participants were contacted at least 3 times for each data collection point, unless they had previously withdrawn from the study. All communication regarding meeting scheduling was logged, including cancellations and rescheduling. Signed consent was obtained from all participants during their first in-person contact with the study. The project was approved by the Human Research Ethics Committee of the partner MHS, with approval being valid from December 5, 2013 to January 9, 2019.Owing to instability in the membership and meeting schedule of the SLG during 2014-15, participant access for interventions and their evaluation became limited. There was also marked organizational staff turnover, with 9 managerial departures, 8 internal promotions, and 4 external hires. Only 6 of the recruited participants remained in the senior management group for the duration of the project.For the intervention, engagement became reliant on the interests of individual participants, with ad-hoc one-on-one and small group discussions replacing workshops and presentations with the entire SLG. These interactions were facilitated by the dual membership of the CI as both a researcher and participant.For evaluation, the scope of the project was expanded to include the experiences of the researchers in responding to this environment. Hence, all researchers who were actively involved in the project between 2014 and 2016, defined by attendance at a minimum one project meeting, were invited to participate in interviews in 2017. Furthermore, research team meeting minutes and notes were retrospectively added to the data analysis, with the consent of the research team and the appropriate ethics amendments.In the first phase, we will analyze the components of a mental health system and develop a generic framework for the simulation model. Subphases will be (1) scenario generation; (2) entity modeling; (3) parameter modeling; (4) temporal changes modeling; and (5) output.Participants will be consulted to determine the scenarios to be modeled. However, 3 general model scenarios are planned: (1) policy change affecting the structure of services; (2) population distribution changes; and (3) organizational innovation in the delivery of care models.The main entities of this model are patients, staff, services, and resources , with their interactions representing the activities of an actual health care system. A priority-based queuing model ,22 will Parameter modeling consists of 2 components, namely, calculation and prediction. During the model building phase, this module will calculate arrival and transition rates and the length of stay using the observational data for a given scenario. During the validation and predictive assessment phases, the values of the above parameters will be predicted taking into consideration the expected changes and the data for validation.The temporal changes that mainly influence the mental health system are demographics and technological changes. Demographic changes largely result from changes in birth and migration rates and will be projected from data available through the Australian Bureau of Statistics.For assessing the impact of a service component or policy option in terms of health gain, we plan to use 2 quantitative measures: quality-adjusted life year (QALY) and disability-adjusted life year (DALY).QALY is an outcome measure for evaluating the burden of disease. It takes into account both the quantity and the quality of the extra life provided by a health care intervention or policy option and is calculated as the product of the life expectancy and the quality of the remaining years. While QALY is useful for cost-effectiveness analysis, weights used in calculation are not linked to a particular disease, condition, or disability, but are rather based on an individual’s health state.DALY is a measure of disease burden that captures both morbidity and mortality effects for a wide range of disorders and interventions and the baseline information for the health status in Australia is readily available . The DALThe model will allow end users to choose either of the measures through a graphical user interface. Apart from QALY and DALY, impacts on blocking rate and resource utilization will be investigated, and specific illness outcomes could be considered, depending on the focus of the scenario chosen.A specialist in modeling will build a simulation model in Arena , a widelThe data collected from the MHS will be divided into 2 sets. One will be used for model building, while the other will be used for validation, the 2 sets being mutually exclusive. To test quantitatively how adequately the model represents the actual system, within the service components of a particular scenario, we will compare the model output with actual historical (ground truth) values. For this, the values of model output parameters  will be compared with their respective ground truth values through a statistical goodness-of-fit test . Similarly, to test the model for predictive performance, the output of the model in response to the validation data will be quantitatively compared with their corresponding ground truth values . Strong agreement between the model output and the corresponding actual values will assure the model’s accuracy in emulating the actual system.The project will also involve the provision of training to MHS managers in the use of the simulation modeling tool to guide decision making regarding the configuration and resourcing of MHS. Such training and the availability of the simulation model will enable MHS managers to adopt new approaches to service management, with their decision making being underpinned by much stronger evidence than is currently available.To capitalize on participant interest stimulated by the October 2013 project briefing, a program logic modeling (PLM) workshop was scheduled during the SLG meeting in December 2013, with a follow-up workshop scheduled for July 2014. The aim was to generate inputs for the creation of the simulation models (phase 1 of the project) and to continue participant engagement in the project. The PLM workshops were facilitated by an experienced external contractor. In the first workshop, participants were prompted to identify strategic issues challenging the MHS and their consequences for the organization, staff, and consumer. The second workshop aimed to validate the outputs of the previous workshop and confirm the organizational structure of the MHS prior to integration with the modeling software.Research on problem structuring methods and group model building claim that the process is often more influential than the final model in the decision making of users ,26. The strongly agree and 1=strongly disagree; Immediate changes in decision-making process will be evaluated through the observation of participants’ interactions during simulation workshops and a pilot self-report survey on workshop effectiveness. Survey questionnaire items were derived from a frequency analysis of the claimed benefits of PLM in journal papers -31, focuThe primary outcome of interest is a change in the strategic decision making of the SLG to incorporate greater amounts of evidence. This will be captured by comparing the decision-making mental models of SLG members pre- and postintervention, within the group (similarity), and to an ideal standard . Mental model similarity and accuracy are both predictive of increased group performance ,33.To extract mental models of current decision-making, participants were asked, “If a new staff member arrived today, what would you tell them about how decisions get made by the management team?” They will then be prompted with statements such as “and before that?” or “after that?” Concept maps of current decision-making processes were created during the interview and validated against interview transcripts. To assess the test-retest reliability of the elicitation method, during the exit interview, participants were again asked, “If a new staff member arrived today, what would you tell them about how decisions get made by the management team?.”ad-hoc nature of meetings with participants exacerbates the lack of structured data collection, necessitating a greater reliance on the document analysis and interview content in the analysis stages.Adaptations to the intervention necessitated an adaptation to the evaluation. Of most impact was the lack of group meetings or workshops, meaning that group processes were no longer able to be directly studied through observation or questionnaire. The The document analysis includes business plans, strategic documents, meeting minutes, and other documentation relevant to the decisions addressed by the study. Documents were released by the MHS Office of the Executive Director and the CI. These documents were used to establish a decision-making context and track the development of decisions prior to the initiation of this project. Furthermore, public document sources that provide participant demographics information, organizational information, and government policy information were accessed when required.Interview content was also expanded to include more open-ended reflections from participants and researchers on the project, discussing topics of expectations, learning, and possible external factors affecting the implementation  and 2.All evaluation data collection was conducted by KML to maintain the separation between the researchers conducting the intervention and the evaluation of the intervention.A total of 18 interviews were audiorecorded and transcribed verbatim, with 1 participant refusing a recording of the exit interview, instead of allowing note-taking. All audiorecordings, notes, and documentation were imported into the qualitative data analysis software NVivo 10 for analysis .Field notes were kept by KML documenting the time, date, general content, and personal emotions and thoughts associated with contact with participants.To maintain a close relationship to the data and participants, study data are stored in an identifiable format in password-protected files and folders on password-protected computers located at the core administration site. These can only be accessed by the research staff. The study data will be stored for a minimum of 7 years, after which these may be confidentially destroyed.All evaluation analyses will be conducted by KML, with an external senior qualitative researcher providing guidance and analysis checks where required.The content analysis of the interview transcript was used to review and refine the interview diagram into a concept map. Each individual’s content map was transcribed into a matrix formation with an arbitrary distance of 1, and input into the network analysis software JPathfinder ,35 for qGroup-level concept maps will be created manually by combining all current concept maps, noting agreement by the count of participants who mentioned each concept or a similar construct. This procedure will be repeated for the second time-point. Group-level mental models at each time-point will then be compared against each other to assess any changes over the intervention period.Linguistic coding will be used to assess the process effects of the PLM workshops. Initial codes were derived from the literature on the benefits of problem structuring methods and group model building -38 and tParticipant and researcher interviews will be analyzed using thematic analysis. Open coding will be used to explore the data prior to an iterative process of thematic refinement involving member checks and the exploration of alternative interpretations. These interpretations will be presented to participants, providing them with the opportunity to provide further comment. Furthermore, the researcher-participants will be involved in the written publication of the analysis, ensuring shared ownership of the project evaluation and recommendations.The project was funded in 2012 and recruitment was completed in October 2016. Sixteen managers participated in at least one data collection (see Primary data collection has been completed. Data analysis is currently under way, with parallel member checking ongoing. The first results are expected to be submitted for publication in late 2018.This research protocol outlines the implementation and evaluation of simulation modeling in the planning of MHS in Australia. As a case study, this research design has both advantages and limitations. The iterative design of the intervention allows easy adaptation to the changing organizational context; however, this comes at the cost of clear data points for quantitative evaluation. This is addressed by favoring a qualitative case study approach for evaluation, at the cost of generalizable findings. However, given the lack of reporting on simulation implementation in the past, such deep access and analysis provide a unique opportunity to understand the realities of translational research in this area.While the methods used allow for feedback from senior staff, which includes direct-care staff and a consumer representative, the organizational level of the modeling intervention does not readily allow for the incorporation of other direct feedback from consumers, family members, or nonmanagerial staff. However, following the completion of the project, we expect that the modeling system will be a valuable decision support tool to be used by MHS managers, which will be integrated into the process of decision making around service configuration and allocation of resources within the MHS. This provides the potential for future follow-up studies measuring the intervention impact for patients, families, and nonmanagerial staff.The challenges faced by the project thus far, especially the instability of the health care context, are not unusual. Hence, lessons from this research have the potential to improve the implementation of future research projects, providing greater evidence-based service planning for the mental health sector in Australia.


In France during the last 15 years, precariousness among women has increased. In breast cancer, precariousness has been associated with an increase in mortality, but the links between precariousness, stage at diagnosis and care pathway are little explored. Our study aims to evaluate the impact of precariousness on care pathways, treatment and recovery phase according to a multidisciplinary analysis.Comparative prospective observational multicenter study of exposed / unexposed category. Patients with breast cancer are recruited in the Ile de France area. Three scores are used to identify precarious patients. Precarious patients are matched to non-precarious patients by age group. Questionnaires are distributed to patients at different times of care. The main objective is to compare the stage of the disease at diagnosis between two groups. The secondary objectives are: comparison of socio-economic and geographical characteristics, direct and indirect costs, personal trajectories of care and health. Analysis include multidisciplinary approaches. A geographical information systems method will evaluate the accessibility to health facilities and the characteristics of the places of residence of the patients. An anthropological analysis will be conducted through observation of consultations and semi-directed interviews with patients. These methods will allow to analyze the diagnostic and therapeutic routes, placing it in a life history and an economic, socio-cultural and health environment. The economic analysis will include a comparison of direct, indirect costs and out-off pocket costs, from the patient’s point of view and from the societal perspective.Conducted in a clinical setting and coupled with a qualitative study, this study will provide a better understanding of how contextual factors, combined with individual factors, can influence the course of health and thus the stage of the disease at diagnosis. The multidisciplinary approach, involving clinicians, geographers, an anthropologist, an economist and a health epidemiologist, will allow a multidimensional approach to the impact of precariousness on breast cancer.NCT02948478 registered October 28, 2016. ID RCB: 2016-A00589–42. protocol version: 2.1. decembre 13, 2018.ClinicalTrials.gov Identifier:  Breast cancer is the most frequent cancer among women with around 50,000 new cases per year in France. There is a social gradient in incidence and mortality with a lower life expectancy of persons belonging to the most disadvantaged socio-economic groups.Socioeconomic and geographical inequalities in cancer mortality have been widely described in European Countries and in the US –3. UntilOtherwise, little is known about the relationship between precariousness, breast cancer stage at diagnosis and breast cancer treatment course. Precariousness is a multifactorial concept. The definition of precariousness elaborated by J. Wresinski and adopted by the French High Committee for Public Health is “the lack of one or more of the securities, especially that of employment, allowing individuals and families to assume their professional, family and social obligations and to enjoy of their fundamental rights “. PrecariIn the light of recent studies, several elements occurring at different times of the care pathway can help understanding precariousness impact.First, precarious women have a poor access to screening (mammography) which is one of the causes of a diagnosis delay. Thus, in France, it has been shown that the women’s economical and geographical situation is a significant determinant of breast cancer screening and that diverse situations of insecurity could lead to a renunciation to breast cancer screening . Even inSecond, there may be a delay between confirmed diagnosis and initiation of treatment varying with the medical diagnosis context , varying availability, accessibility and coordination of healthcare resources in geographical areas, and finally, individual factors.Third, variations in access and quality of care for people with breast cancer could also explain differences in survival rates. The French health insurance system ensures patient access to the most effective treatments, regardless of income level and type of insurance . This prThe description and analysis of the care pathway has been the subject of numerous works including sociological and anthropological studies, which invariably note its diversity, plurality and complexity. This complexity is part of our multicultural contemporary societies . The conThere are few studies in France about socioeconomic and geographic inequalities in cancer and most of the studies are on data registries with incomplete clinical and demographical data . In addiOur study is a specifically designed study in the field of social and human sciences grounded on a multidisciplinary approach and conducted in a clinical environment allowing avoiding the weaknesses of studies performed on registers. Our study is designed to assess the impact of precariousness on the history of breast cancer, on the treatment and the rehabilitation phases in a multidisciplinary contextual analysis. Geographical, social, economic and anthropological analysis will be conducted at different times of the care pathway. The final aims are: first to implement corrective measures for an appropriate breast cancer diagnosis in precarious women to design care management adapted to each situation of insecurity, second, to warn policy makers on these precarious situations and the impact of possible correctives measures.Disparités Economiques et Sociales et cancer du SEIN”) is an academic, investigator-driven, prospective observational multicenter cohort study comparing patients “Exposed” to precariousness to patients “unexposed” to precariousness.The DESSEIN  .Three scores of precariousness will be applied to patients for determining precariousness:Precariousness will be defined by identification of this condition by at least one of the three scores. Absence of precariousness is defined by absence of identification of this condition by the 3 scores.Each precarious patient will be matched to a non- precarious patient belonging to the same age group, regardless of the center. Each woman will receive breast cancer treatment according to stage of the disease and to local multidisciplinary procedures of each center. In France, treatment decisions are made during multidisciplinary meetings in agreement with the national and international recommendations and/or with the local referential.Deprived patients are compared to non-deprived patients in the breast cancer care pathway.The primary outcome is the comparison of the stage of disease at diagnosis (according to the TNM classification) between precarious and non-precarious patients.to describe the socio-economic situations and geographical context between precarious and non-precarious patients in quantitative and qualitative approaches,to describe diagnostic and therapeutic pathways of women with breast cancer including the diagnostic phase, the treatment phase and the phase of post-treatment and rehabilitation,to compare those pathways between precarious and non-precarious women,to compare in both groups of women the direct and indirect costs of care and the out-of-pocket costs.Secondary outcomes are:thAugust 2004 on public health policy and its implementing decree (No. 2006–477) of 26th April 2006. . In each center participating, patients will be asked by their physician to sign an informed consent. The study was approved by the Ethics Committee-Paris Ile de France III on 24 May 2016.The study is conducted in accordance with the guidelines of the Declaration of Helsinki. This protocol is part of a research to evaluate routine care as defined by art No. 2004–806 of 9The study was supported by a grant (2015–1-PL SHS-01) of INCA .All patients seeking treatment for breast cancer, regardless of the cancer stage, are invited to participate in this study.Women with a breast cancer over 18 years of age are eligible for the study, if they have no history of cancer (any site) treated in the previous five years and no associated cancer at time of diagnosis. Inclusion and exclusion criteria are summarized in the Table Women will be recruited in breast cancer centers of the Ile de France region. Centers participating to the study are listed in the Table The questionnaires analysis will be a socio-anthropological, socio-economic and geographical multidisciplinary analysis that will compare individual data with contextual data and clinico- pathological data, according of the levels of precariousness of the patients.For geographical analysis, Health care and social environment will be assessed with Geographic Information Systems (GIS) based on available Geo-referenced databases  at neighbourhood level (IRIS) for the Ile-de-France region (n IRIS = 5261). Briefly, GIS can be defined as computer-based methods and tools which, via different information sources, enable organising, managing and combining spatial and thematic data, and representing and analysing results according to geographic location .Anthropological analysis will be performed through observation of consultation and patients interviews. The observation of consultations will provide useful elements for the global analysis of care pathways . Different dimensions will be explored in individual interviews that can help understand the attitudes of women towards screening and their behavior in relation to the disease, the treatment, the professionals and the impact in everyday and professional life. Interviews will be conducted on a small number of patients in order to describe deeply the healthcare pathways and articulate different determinants of attitudes and behaviors with environment  and context. An inductive approach in interviews is likely to provide news elements of understanding of the health care pathways. Interviews are performed by an anthropologist.Study procedures are summarized in Table The economic analysis will describe and compare the costs between the two groups of patients. The first part of analysis will reply to the question “is the precariousness in breast cancer induces over-costs from the society perspective?” This part will include the direct costs  and nonmedical direct costs ) and the indirect costs (productivity losses) , 26. ProPatient data will be recorded using CleanWeb® electronic Case Report Form (eCRF) The following data will be retrieved from the patient’s medical record by trained personnel: demographic characteristics, diagnosis characteristics, imaging, stage of disease, treatments received , pathology, delay between diagnosis and first treatment.Inclusion: the questionnaire administered at baseline, before beginning treatment will comprise elements allowing calculation of the three precariousness scores allowing to identify women with and without precariousness.The questionnaires administered at 3, 6 and 12 months will focused on the health care renunciation, out-off pocket costs, return to work, rehabilitation. The last questionnaire at 12 months will also include the 3 scores of precariousness to determine if the disease worsened or not socio-economic deprivation.Questionnaires will be distributed to all women to assess socio-economic and geographical determinants at inclusion and at 3, 6, and 12 months of follow-up.Our hypothesis is that the proportion of stage 1 patients will be 50% among precarious patients versus 60% among non-precarious, a 10% absolute difference. To detect such a difference with a 90% power, 520 patients are needed . Therefore, our targeted sample size is 1040 patients (520 in each group). Inclusion period will last 36 months. Subject’s length of participation is 12 months. We recruited 20 participating sites in order to be able to include 1040 patients.http://www.consort-statement.org/).The strategy for design and analysis will be made in compliance with the CONSORT statement .The primary endpoint analysis will be carried out by a two-sided chi-square test on proportions. Odds ratio and its 95% confidence interval will be presented.For secondary endpoints, the same principle will be applied for qualitative variables. For quantitative variables the mean difference and its 95% confidence interval will be presented.All statistical analysis will be carried with SAS and R software, and the statistical significance level will be (two-sided) 0.05.A Clinical Research Associate (CRA) appointed by the sponsor will be responsible for the proper conduct of the research, for collecting and documenting, recording and reporting the data generated in writing, in accordance with the Standard Operating Procedures applied within the sponsor and in accordance with the French Good Clinical Practices as well as with the legislative and regulatory provisions in force. The sponsor’s CRAs will perform regular monitoring visits to the investigating centers at a rate corresponding to the patient follow-up schedule and enrolment rate. An audit can be carried out at any time by individuals appointed by the sponsor and not associated with the research directors. The objective of the audit is to ensure the quality of the research, the validity of the results and compliance with the legislation and regulations in force.DESSEIN is one of the first prospective multidisciplinary study collecting real-life data on precariousness and breast cancer. Monitoring patients since the onset of the disease and up to 12 months will allow analyzing the whole pathway of care and rehabilitation based on individual data in all phases. Multi-dimensional and contextual analyses are among the strengths of our study. The multidisciplinary approach involving clinicians, geographers, anthropologists, health economists and epidemiologists, will allow an overview of the impact of precariousness on breast cancer management and the role of the different aspects of precariousness. The first patient was included on 15 December 2016. Twenty centers are now active and about 800 patients have been recruited up to June 2019.The expected results of this study are to assess the extent of difference in the initial stage of the disease between precarious and non-precarious women. In addition we will be able to describe the different types of precariousness and their impact on the course of the disease, during treatment and during rehabilitation. Finally we will be able to analyze the relationships between geographical, economic, social and anthropological vulnerability and clinico-pathological prognostic data in both groups of patients.On a longer perspective, this study will allow to build new tools for a better diagnosis of precariousness in all its dimensions, to design corrective measures and to warn policy makers. These corrective measures could be: local measures , social measures , medical and economic measures .


Pluvial flash floods in urban areas are becoming increasingly frequent due to climate change and human actions, negatively impacting the life, work, production and infrastructure of a population. Pluvial flooding occurs when intense rainfall overflows the limits of urban drainage and water accumulation causes hazardous flash floods. Although flash floods are hard to predict given their rapid formation, Early Warning Systems (EWS) are used to minimize casualties. We performed a systematic review to define the basic structure of an EWS for rain flash floods. The structure of the review is as follows: first, Section 2 describes the most important factors that affect the intensity of pluvial flash floods during rainfall events. Section 3 defines the key elements and actors involved in an effective EWS. Section 4 reviews different EWS architectures for pluvial flash floods implemented worldwide. It was identified that the reviewed projects did not follow guidelines to design early warning systems, neglecting important aspects that must be taken into account in their implementation. Therefore, this manuscript proposes a basic structure for an effective EWS for pluvial flash floods that guarantees the forecasting process and alerts dissemination during rainfall events. Flooding is considered as one of the major threats to human civilization and is directly attributed to heavy precipitation leading to loss of life, infrastructure damage, as well as huge economic losses ,2. ClimaCities with high population density present a higher disaster risk. They are expected to experience the effects of climate change with the increment of intensity and frequency of harmful events such as flash floods . VulneraDifferent types of floods can affect urban areas and some of them may be more applicable to some regions than others. These floods are mainly classified into four types: coastal, fluvial, pluvial and flash floods.Coastal flooding results from a combination of extreme climatic phenomena. The sea level exceeds the elevation of the land or of a natural or human barrier; water flows and floods the land behind it ,8. When The flooding that affects the vast majority of the world’s regions is fluvial flooding or river flooding. This type of flood occurs when the rivers overflow or burst their banks due to excessive rainfall over an extended period of time and spill onto the floodplain ,11. It cPluvial flooding or surface water flooding is a problem in many cities and occurs when, during high intensity rainfall, the sewage and drainage system becomes overwhelmed and excess water cannot be absorbed into the soil . This pra flood of short duration with a relatively high peak discharge” [a flash is a flood that rises and falls quite rapidly with little or no advance warning, usually as the result of intense rainfall over a relatively small area” [a rapid and extreme flow of high water into a normally dry area, or a rapid water level rise in a stream or creek above a predetermined flood level, beginning within six hours of the causative event ” [Of all the negative impact generated by floods, none is as harmful as flash floods , which cause millions of dollars in property damage every year . The Worscharge” . The Amell area” . The U.Sce jam)” . These fce jam)” . Dam breAs reported by The World Bank, in 2008 half of the world’s population lived in urban areas but in 2030 this number will increase to 60% and 70% in 2050 . This acAlthough, historically, the most reported flood events are fluvial followed by pluvial and sea water, pluvial floods have increased in cities and have the highest proportion of occurrence since 2000 compared to other types of floods in the same period . Figure The United Kingdom is one of the areas in Europe most affected by pluvial flooding. Around 5% of the urban population is exposed to an annual pluvial flood risk of 0.5% or greater . It is eNot only European cities are affected by pluvial flooding. In Japan, approximately USD 1 billion in damage occurs annually due to pluvial floods affecting densely populated urban areas with poor drainage systems . HoweverDue to the deficient drainage infrastructure in the cities, these pluvial floods turn into dangerous flash floods and not only affect the economy but also lead to human losses as is the case for the city of Barranquilla (Colombia). In this city, during high rainfall events, the streets become torrential streams endangering pedestrians and drivers. Since there is no system that alerts in a timely manner the community about the danger of these floods, pedestrians and drivers trying to cross the streets are washed away by the dangerous streams .Chinese cities including Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, and Hangzhou are also affected by pluvial flash floods . In JulyPluvial flash floods are not simply caused by weather phenomena. They depend not only on the amount and duration of precipitation but also on the hydrological characteristics of the basin such as runoff magnitude, antecedent moisture condition, drainage area, soil type and land ,27. HydrAlthough there is a huge demand for understanding pluvial flash floods in cities, until now, few works have attempted to systematically examine the potential impacts of a pluvial flash flood in cities in order to develop efficient solutions . In order to mitigate the risk of human losses and economic damages caused by floods in cities, measures of adaptation and minimization should be considered. The International Strategy for Disaster Reduction (ISDR) has classified these measures into structural and non-structural ,30:(a) Structural measuresThey include the construction of physical structures to reduce or avoid potential impacts of hazards such as protection, retention and drainage systems, as well as the use of engineering techniques to improve resistance and community resilience . Most of(b) Non-structural measuresThese actions do not involve building physical structures, but rather use existing knowledge, laws or policies to reduce risk and its impacts ,31. ThesEarly warning systems are nonstructural tools useful to populations that do not have sufficient resources to minimize the risk of flooding. They are tools to reduce economic losses, and protect the life and property of a community . InformaThe aim of this manuscript is to provide guidelines to develop an effective EWS for pluvial flash floods in real time. Understanding the hazard posed by pluvial flash floods in cities and the limited information available for EWS design to mitigate this risk is mandatory. The causes and variables that influence the formation of flash floods in urban areas, as well as the key elements of an EWS, are described. Likewise, different EWS for pluvial flash floods implemented worldwide were reviewed to determine the primary and secondary instruments used to measure the variables and the methods for processing information and alerting the community at risk.Each architecture describes the instruments and methods used for the detection, monitoring and real-time analysis of variables related to flash floods and alert dissemination. From the reviewed projects, the most used instruments to measure hydrological and hydraulic variables during pluvial flash floods were selected. Also studied were the communication protocols to send the information and main media for alert dissemination. In this review, we identified the need for forecasting and dissemination–communication processes to have fail-safe systems. These processes guarantee that the community receives timely alerts. However, none of the reviewed projects had a fail-safe system for these processes. The lack of these systems makes early warning systems more susceptible to the loss of measured data. Therefore, alerts cannot be sent timely to the community. For this reason, this manuscript proposes a basic structure for an effective pluvial flash flood early warning system that guarantees the dissemination and communication of alerts during rainfall events. This proposed EWS suggests which hydrological and hydraulic variables should be monitored in real-time during rainfall events; compares the characteristics of communication protocols and the most effective media to disseminate the alerts.Developing techniques and criteria for solving the complex problems associated with runoff in urban areas is one of the most challenging tasks for hydrologic engineers. During high-intensity rainfall events, drainage systems become rapidly saturated. For this reason, soil cannot absorb the water quickly enough. There are a number of factors that affect the intensity of pluvial flash floods. Climate change, extensive and rapid urbanization and unsustainable urban development combined with management failure are considered the main factors for the development of pluvial flash floods. In general, higher precipitation intensity can result in an additional runoff. A deficient drainage system also increases the volume . LikewisA number of studies and reviews have assessed the influence of a combination of climate change and rapid urban development in flood risk ,35,36. IUrbanization and vegetation area reductions are significant threats that affect water quality and increase the risk of flooding in urban areas. In the UK alone, over 80% of the population live in urban areas and the population has risen from 32 million in 1901 to 64.6 million in 2014 . During Urban densification, deforestation and inadequate urban drainage design have led to greater runoff volumes since the percentage of impermeable surfaces and compacted soils is greater . LikewisIn urban areas, soil moisture and soil permeability are critical properties that influence the formation of flash floods. Soil moisture is considered the most important soil factor for rapid runoff and flash flooding because it can vary significantly, even on a sub-daily time scale . In dry The infiltration capacity can also be affected by the permeability of the soil. This property depends on the different characteristics of the soil, such as texture, crust formation, soil compaction, soil contraction and expansion, microbial activity, soil hydraulic conductivity and root distribution. Soil texture is the most important ,45 and iThe physical properties of a basin-like size, shape and surface roughness and its streams, influence the amount and the timing of runoff . The sizThe relation between the infiltration rate and the basin’s slope is inversely proportional. If the basin’s slope increases, the infiltration rate decreases. The reason is that gravity pulls less water into the land surface and more water across that surface . Unlike This section describes the architecture of an early warning system for the dissemination of timely alerts during pluvial flash floods. EWS is understood as a set of procedures, steps or key elements related and interconnected with each other . The Uni(a) Disaster Risk KnowledgeAn event becomes a disaster when it abruptly affects a community’s daily activities and involves human and material losses and has economic or environmental impacts . It woulIn this key element, it is necessary to identify hazards, the exposure, vulnerabilities and risks of a population. According to the United Nations Office for Disaster Risk Reduction (UNISDR) , a hazarvulnerability as “the factors or constraints of an economic, social, physical or geographic nature, which reduce the ability to prepare for and cope with the impact of hazards” [vulnerability as “the conditions determined by physical, social, economic and environmental factors or processes which increase the susceptibility of an individual, a community, assets or systems to the impacts of hazards” [Vulnerability relates to a number of factors such as physical, economic, social and environmental . The Asihazards” . The UNIhazards” . The exposure is “The situation of people, infrastructure, housing, production capacities and other tangible human assets located in hazard-prone areas” . The risk is “the probability that negative consequences may arise when hazards interact with vulnerable areas, people, property, environment” . Historical backgroundGeographical aspectsEnvironmental and physical aspectsSocio-cultural aspectsEconomic aspectsTo reduce the risk of flooding in urban areas, the data collected should be relevant and concise, qualitative or quantitative, and should be obtained through official sources ,53. The Likewise, vulnerability assessment of the area at risk is necessary. The various components and essential functions of a city that may be at the heart of possible dangers should be considered . FurtherAre the hazards and the vulnerabilities well known?What are the patterns and trends in these factors?Are risk maps and data widely available?The following questions must be answered:First, the initial data processing, establishment of the hydrological model to predict runoff, probability analysis and elaboration of a flood risk map is performed.Second, interviews, discussion groups and workshops are conducted with the community at risk to determine vulnerability, taking into account community perceptions and historical records.To answer these questions, Fakhruddin et al.  propose Finally, the result of these processes resulted in the development of an integrated system, flood risk map and response option. Once a detailed risk map is made and relevant information from the area is obtained, the warning design must be established. This process is complex and requires the integration of different activities, devices and the processing of large volumes of information. The Forecasting section describes the procedure for designing timely, clear and useful alerts for the community.(b) ForecastingFor urban flash floods, the main goal of this key element is forecasting and establishing alert levels in real time. This process is divided into two sections: Monitoring and Information Processing. The Monitoring section monitors and transmits information on meteorological and hydraulic variables related to urban flash floods . The Information Processing section receives the data of the meteorological and hydraulic variables, and through analysis tools, computer models and simulator design alert .The forecasting process requires the use of a number of technologies and areas of expertise for the analysis of large volumes of data and predictions based on simulations. These technologies include sensors to measure meteorological and hydraulic variables and computational models and simulation software to process the information. It is necessary to provide an advanced visualization technology to interact with people at risk and a decision support system with remote access to assist public authorities and citizens in timely decision making ,59.Are the right parameters being monitored?Is there a scientific basis for making forecasts?Can accurate and timely warnings be generated?According to the UNISDR , this seAs part of the decision support tools during high rainfall events, different runoff and hydraulic models are available for urban flooding forecasting. They are a rainfall-runoff model and hydrodynamic models in 1D, 2D and 3D. Most of these models use as input the measurements of the amount of precipitation, water level and water velocity. Therefore, these variables should be monitoring in real time ,61.The alerts must be sent in a timely manner and the message transmitted must be clear and understandable for all people. The Dissemination–Communication section details the characteristics of alert messages during flash floods and the means used to send them.(c) Dissemination–CommunicationDo warnings reach all those at risk?Are the risk and warnings understood?Is the warning information clear and usable?Sending and communicating warnings is the determining step between forecast and action . DissemiDissemination of warnings through organizations or leadersSending warnings through multiple credible sourcesPeriodic and constant warningsScientifically certified warningsTo achieve positive answers to these questions, alerts must be available in different formats, such as text, graphics, colour coding, audio, etc. This facilitates the reception and action on warnings. According to the WMO , for aleHaving information regarding their preparationA communication plan, evacuation strategiesFirst-Aid and disaster knowledgeFinancial resiliencePreparedness behavioursAccording to The Economist Intelligence Unit, Hong Kong is considered to be one of the safest cities in the world today, due to its ability to prepare and respond to disasters ,56. In 2(1)Places to seek medical attention (92.2%)(2)Evacuation routes (85.2%)(3)Shelter information (84.8%)(4)Details of the disaster (67.4%)(5)Missing persons (65.2%)(6)Victims (45.2%)Regarding which information they considered most important, the results show the following:For the communication process, 65% of respondents would use emergency contact numbers on their mobile phones, but 73.1% had password-protected phones. In the event that the mobile phone network failed, 37.4% of the respondents stated that they would use a landline to communicate with their family, 32.5% said they would go home and 4% reported having a place to meet your family. This study concludes that, ultimately, most residents are interested in receiving additional information on disaster preparedness through the Internet using mobile devices and television.EWS are considered effective not only when an alert is sent in a timely manner but when this alert is correctly understood and the community takes protective actions . This im(d) Preparedness–ResponseDisaster preparedness includes all the activities necessary for a community to react to such an event . It is n(1)Perceptions and understandings of flash flood risks(2)Perceptions and interpretations of flash flood forecasts, warnings, and other alerts(3)Protective decision making in response to flash flood warningsThe results of a public survey of 418 people in Boulder, Colorado, USA, on how people perceive, understand, and respond to flash floods and warnings received through different means were presented in 2015 . They esIn Alert dissemination and communicationClarity regarding responsibilities in case of warningPreparing authorities and communities to respond to the disasterInstitutional and social conditions that must be fulfilled to ensure timely decision-making regarding the warnings should be as follows:The involvement of local communities and authorities in the design of EWS increases the effectiveness of the entire early warning process and thus leads to a greater and better response to an alert.It is necessary not only to notify people about the danger of a sudden flood but also to motivate them to take protective measures. According to Quevauiller and Innocenti , the folAccording to the ISDR, each key element has key actors that should be involved to develop a people-centred Early Warning System. Another feature of the effectiveness of an EWS is that the key elements must be interrelated. The activities carried out in each section should be aimed at the satisfactory development of the following section.This section presents different architectures of early warning systems for pluvial flash floods implemented worldwide. Each project installed different types of sensors to monitor variables used in urban flood forecasting and modelling.Wireless communication was the most used technology for transmitting data to the processing centre. On the other hand, each one developed a different method for information processing and alert dissemination.Chang and Guo  proposedUltrasonic Water Level Monitoring module uses an ultrasonic sensor to measure water level and it is connected to a data acquisition board and this, in turn, is connected to a wireless system. The wireless system is an MDA300CA unit manufactured by Crossbow Technology  and uses IEEE 802.15 standard to send the information to the data processing module.The Network Video Recording Module is composed of a group of cameras installed at main intersections. Cameras provide traffic monitoring information in video and images. This system includes four Redeye Z205 network cameras and can be connected via Ethernet to the data processing module. Each camera has an IP address assigned to which users will have access from any Web searcher.The Data Processing Module combines all sources of information. This module provides three types of information: raw data, predicted data, and video information. The raw data is the information obtained by the sensors, while the predicted data are obtained through mathematical models. All of this must be accessible online.The wireless sensor network has six nodes and each node has a temperature, humidity and atmospheric pressure sensor connected to a mote  and powered by a photovoltaic system. This system was used by Ramírez-Cerpa et al. [The a et al.  to detera et al. . This moa et al. , a test A server receives the data from the wireless sensor network and, through a Web and mobile application, gives information to end-users.The city of Barranquilla is located in the Caribbean Region of Colombia and does not have an efficient rainwater drainage system; therefore, during rainy events, streets become dangerous streams called “arroyos” ,71. ReseIn two streets near the Manila subway, a real-time urban flood monitoring system was installed . A floodElectronic Instrumentation has a ground-based pressure sensor and a tipping bucket rain gauge connected to the data logger and powered by a photovoltaic system. The obtained information is sent through a General Packet Radio Service (GPRS) module to a server. Two nodes were installed on two nearby streets (Earnshaw and San Diego) on Boulevard Spain, Manila.The Server receives the data and processes it to provide real-time information. A Web application provides real-time information, historical data and flood data to users. Likewise, a mobile application shows the real-time variation of flash floods in the streets so that users can adjust their routes and travel schedules. The In Nakhon Si Thammarat, a province in southern Thailand, a wireless flood monitoring system was developed for the mitigation and management of flood disasters in urban and suburban areas . The sysRemote Site. The monitoring section contains 15 remote devices located around the Nakhon Si Thammarat flood risk zone. A tipping bucket rain gauge was used to measure the amount and intensity of the rain. These remote devices use an ultrasonic Doppler instrument called STARFLOW  to measure water level and velocity. Since the STARFLOW equipment is very sensitive to fluctuations in water velocity in the channel, the average velocity was used in a time interval rather than raw measurement data. The STARFLOW unit is connected to the GPRS Data Unit (GDU) and sends the information every 10 min to the control centreThe Control Center has a server that contains the historical database, processes in real time the information and displays it through a WEB application. End-users can access this system through the Internet or mobile devices. The alert messages are also sent via text messages (SMS), FAX and email to the community.The To avoid unexpected power disruptions, an uninterruptible power supply (UPS) and a surge protector was installed. This allows the whole equipment to work for at least 24 h with a continuous electrical energy supply when not available.The University of Puerto Rico, Mayagüez (UPRM) campus developed a weather radar network that provides accurate and real-time hydro-meteorological information to the west region of the island ,79. ThesThis weather radar network information was used to develop a flood alert system in western Puerto Rico for convective precipitation of time periods of a few hours or less (nowcasting) . 6 m−3) through the Marshall–Palmer [Weather radars provide information on cloud reflectivity and this data can be transformed into rainfall amount using empirical equations. There is an empirical relationship between the amount of precipitation and radar reflectivity, which in turn depends on the distribution of raindrops. The Rain Rate R (mm/h) is related with the reflectivity factor Z . For example, on 29 October 2013 for an observation point in Palma de Mallorca, the forecasting accumulation values (30 min accumulation) exceeded the user-defined red threshold and an email was sent 90 min before the flooding at the city caused several problems. In the email, the top panel shows the areas forecasted to be over the thresholds  and how those areas affect the point centred in the city. Unlike the other projects reviewed, this one does not describe the type of communication used to send the information to the data processing centre, or if the data was processed at the radar installation site. Similarly, it does not describe the power supply system.All the reviewed projects focused their early warning system design on the forecasting and alert dissemination processes. The project developed in Nakhon Si Thammarat (Thailand) included the greatest number of hydrological and hydraulic variables for establishing alert levels. Likewise, this project used more than three communication channels to send alerts but it was the only one that was connected to the electrical grid.With regard to pluvial flash floods, for an early warning system to be effective, the alerts must be issued timely, be clear and understandable to the entire community at risk. Through this review, the key elements of an EWS for flash floods in urban areas were described as well as the variables that influence their formation.Early Warning Systems implemented in different locations worldwide were reviewed to identify the main elements used for the forecasting process such as measurement instruments, data transmission protocols and power supply equipment, as well as information processing methods. Also reviewed were the means and strategies for alert dissemination to the community. The forecasting process is linked to the detection, monitoring and analysis of meteorological and hydraulic variables related to flash floods. It can be carried out using various instruments and methods, but there are indispensable devices for this work. From the reviewed projects, three considered the amount of rain (mm) as a basic element for the development of alerts for urban flash floods. The most used instrument for direct precipitation measurement is the rain gauge and for indirect measurement is weather radars. Rain gauges measure the liquid precipitation expressed in mm during a period of time. Once this measure is registered, it is sent to an information-processing centre. The reviewed projects implemented tipping bucket rain gauges and consist of a light metal container or bucket divided into two compartments. The liquid precipitation is collected into the uppermost compartment and, after a predetermined amount has entered, the bucket becomes unstable and tips toward its alternative rest position .Weather radars are widely used instruments to locate precipitation, identify the types and monitor their movements. This instrument emits microwave pulses and measures the reflected signal from the raindrops ; the higOne of the advantages of weather radars is that they have a higher coverage than rain gauges. Since they can also monitor the movement of the clouds, weather radars can predict phenomena ahead of time, and serve as a backup system in case the on-site devices are removed by flooding or high winds. However, weather radars are more expensive than rain gauges, have higher power consumption and need more technical and social requirements for their implementation.Three projects included sensors for measuring the water level and can be classified into pressure sensors and ultrasonic sensors. Pressure sensors measure the uniform weight of a column of water. Since weight is a force, a column of water with a specific height will always exert the same amount of pressure on the sensor. At the output, the sensor produces a voltage equivalent to the received pressure and then this voltage value translates it to a level measurement .Ultrasonic sensors send a sound wave with a specific frequency to an object and receive the reflected sound wave. The sensor measures the distance by calculating the sending and receiving time of this sound wave . UltrasoUltrasonic wave propagation speed depends on both the nature of the propagation medium and the temperature. When the air temperature and humidity increase, the speed of sound increases and the reach is shorter. This reduction is not linear and differs from sensor to sensor .For some applications, one of the disadvantages of the ultrasonic sensor is that it cannot work underwater, but for flooding applications, this is suitable because the streams sweep away different objects that can collide with the sensor, introducing wrong measurements and causing damage to the equipment. Thus, it is suggested that non-submersible sensors be used for the implementation of early warning systems for flash floods in urban areas.For the communication technology implemented to send the sensors’ data, most of the reviewed projects used wireless communication. Wireless modules under IEEE 802.15 standard and GPRS modules were implemented to send the information from the measurement stations to a data centre. However, these projects only implemented one communication channel for sending the data. If there are failures in the communication system, the alert will not be timely. Therefore, it is necessary to have a minimum of two communication channels for sending the alerts.Ch. Saad et al.  performeThe transmission time of a wireless system depends on the data rate, the message size, and the distance between two nodes . From TaDuring high rainfall events, the power supply may fail. It is recommended to have a photovoltaic system connected to the equipment as a primary source of power supply or as a backup system in case of failure. Almost all projects use photovoltaic systems to supply electricity for measuring and communication instruments. Only one project was connected to the electric power grid and used a UPS as a protection measure.Information processing is carried out in a data centre equipped with applications, and analysis software necessary for alert design. The data centre processes the sensor data and transforms it into alerts in real time. Some of these data centres have a historical database and provide online access to them like the projects implemented in Nakhon Si Thammarat (Thailand) and Florida (United States).The measurement of water velocity as a hydraulic variable should be included in all pluvial flash flood EWS. The parameters, water level, water velocity and their combined effect, are responsible for the stability loss of pedestrians and drivers when trying to cross hazardous streams.The dissemination of information must be timely, gathered and understood by the whole community. Warnings must be simple, clear and useful messages so that opportune decisions may be taken. It is necessary to have an integrated system that allows the information to be sent through different channels, ensuring that it is received and understood by everybody. Studies by Fakhruddin et al.  and Lam The most used methods for sending alerts to the community at risk were Web and mobile applications. Four projects developed a Web application to visualize the alerts, water level and precipitation measurements. One of these projects developed a mobile application too. Another project offers online access to raw data and video information. However, none of the projects integrated television or radio to send the alerts.According to WMO, the alerts should be brief, concise and understandable , but durTelevision and radio were not included in any of the reviewed projects for alert dissemination, but they are very useful media for broadcasting messages to a large part of the population at risk. Likewise, an up-to-date system that visualizes streets during floods and applications that provide alternate routes for drivers is ideal. Taking into account the guidelines provided in Disaster risk knowledge is a necessary phase, prior to EWS design. It comprises identification and mapping of the risk. First, hazards, exposure, vulnerabilities and risk in the population are identified. This information must be obtained from official sources and must be relevant and concise. The next step is to develop a risk map to prioritize the EWS’s needs and guide preparations for disaster prevention and response. The forecasting process is divided into four sections: main section, complementary elements, communication protocols and information processing. The main section is compounded by water level and water velocity sensors and the rain gauge. This set is powered by a photovoltaic system. Radar and video cameras can be considered as complementary elements. Weather radars are the most suitable instruments for monitoring during extreme rainfall events but they are quite expensive. Once the alerts are ready, there are different media to send them. Web and mobile applications were implemented in all the reviewed projects to visualize the alerts, but it is necessary to have more than one channel to cover the entire community at risk. Television is the preferred media to receive the alerts, but government support is needed to be able to send broadcast messages .Sending warning messages to the community at risk is not the last action in an EWS. The communication is established when people receive, understand the message and take timely decisions. To achieve this, it is necessary that the community and the local authorities participate actively in the decision-making process. One proposal on this topic is the project named FloodCitiSense “Early warning service for urban pluvial floods for and by citizens and city authorities”. The aim of this project is to reduce urban areas and citizen vulnerability to pluvial floods. They propose integrating crowdsourced hydrological data measured by different participants such as citizens, local authorities, research units and industrial partners. Furthermore, they suggest implementing low-cost sensors and web-based technologies to display warnings . This pr-Probability of detection-Accuracy: Forecast flood levels compared with measured flood levels.-Reliability: Flood-hit, miss and false alarm rates.-Probability : Amount or percentage of certainty/uncertainty associated with the forecast-Time range ahead of the flood: How far ahead in time a forecast can be made-Timeliness: Warning lead time-Spatial resolution: The smallest area for which a forecast can be madeAfter implementation of an early warning system, it is very important to measure its performance in order to determine its effectiveness. Parker  mentione-Warning information: Recipients’ assessments of the degree to which the warning provided them with the flood information they needed.-Satisfaction with the flood warning service: Levels of satisfaction among those for whom flood warnings were/should have been provided.-Damage Reduction: The amount of flood damage saved by the warning.-Protection of life and limb: The assessed number of lives and injuries avoided by the warning.-Benefit–cost ratio: The ratio of the assessed benefits and costs of providing a flood warning.According to Parker , to measSome of the reviewed projects evaluated the EWS performance, considering only technical aspects. However, after their implementation, they did not register the level of acceptance by the community at risk or the damage reduction.The information obtained from this review study was applied to the development of an early warning system for detection in real-time of urban pluvial flooding hazard levels in an ungauged basin in Barranquilla, Colombia . This deThis project developed a Web application considering the recommendations about the diffusion of information. The aim was to generate an effective and timely response from the population during flash floods. The information was updated every 5 min with the received precipitation value.The application offers the option to subscribe to receive notifications during rainy events through the social networks Twitter and Telegram. Likewise, it allows the addition of more streams as well as the inclusion of as many observation points and rain gauges. In the future, it will display the atmospheric information obtained by different sensors. The effects of climate change have become evident in the increased formation of natural phenomena that can adversely affect people’s lives . The incEWS has been established worldwide as a useful tool for populations to adapt and mitigate the impact of flash floods in urban areas. Through this review, the basic architecture of EWS for flash floods in urban areas was determined. This EWS is people-centred and the community can have an active participation from design to implementation. The EWS is divided into four structures: Disaster Risk Knowledge, Forecasting, Dissemination and Communication of information and Preparedness and Response. Through this review, it was identified that the variables that must be monitored in real time during the rain events are the amount of rain and water level. The information of these variables is also processed in real time to issue alerts in a timely manner. Rain gauges, weather radars, ultrasonic and pressure sensors were the instruments implemented to measure these variables. Although weather radars have more coverage than rain gauges, they are more expensive and need more technical requirements for their implementation.Since flash flood stream flow is turbulent and can wash away different objects in its path, the use of submersible sensors such as pressure sensors is not recommended. Therefore, ultrasonic or radar sensors are more suitable for flooring applications.To send the sensor data measurements to a data centre, the reviewed projects used wireless communication systems; GPRS modules and wireless modules under 802.15 standard were the most used. GPRS modules have a better range of coverage than other wireless communication protocols; nevertheless, they have higher power consumption and longer transmission time.This article has shown that not all the reviewed projects fully comply with the suggested norms for an effective early warning system. This article serves as a guide for the design of early warning systems for pluvial flash floods that affect urban areas, taking into account the instruments, protocols and primary and secondary means for the forecasting and alert dissemination process.


Despite recent progress in improving access to maternal health services, the utilization of these services remains inequitable among women in developing countries, and rural women are particularly disadvantaged. This study sought to measure i) disparities in the rates of institutional births between rural and urban women in Ghana, ii) the extent to which existing disparities are due to differences in the distribution of the determinants of institutional delivery between rural and urban women, and iii) the extent to which existing disparities are due to discrimination in resource availability.Using Demographic and Health Survey data from 2003, 2008, and 2014, this study decomposed inequalities in institutional delivery rates among urban and rural Ghanaian woman using the Oaxaca, the Blinder, and related decompositions for non-linear models. The determinants of the observed inequalities were also analyzed.Institutional delivery rates in urban areas exceeded those of rural areas by 32.4 percentage points due to differences in distribution of the determinants of institutional delivery between the two areas. The main determinants driving the observed disparities were wealth, which contributed to about 16.1% of the gap, followed by education level, and number of antenatal visits.Relative to urban women, rural women have lower rates of institutional deliveries due primarily to lower levels of wealth, which results in financial barriers in accessing maternal health services. Economic empowerment of rural women is crucial in order to close the gap in institutional delivery between urban and rural women. Despite progress in reducing maternal mortality worldwide, pregnancy and childbirth remain a major cause of death for women in low- and middle-income countries (LMICs). In 2015 alone, it was estimated that over 300,000 women globally died as a result of pregnancy or childbirth, almost two thirds of whom resided in Africa , 25. IndWhile institutional delivery rates have been increasing worldwide, substantial disparities persist within countries. In additional to sociodemographic factors, such as wealth status and education , 22, stuPast studies examining differences in institutional deliveries between urban and rural women have examined urbanicity as a determinant of institutional delivery and have generally found higher rates in urban areas, holding all the other determining factors constant. For example, Stephenson, et al.  showed tAfful-Mensah et al.  used datThe following paper aims to explore the causes of the persistently lower institutional delivery rates in rural compared to urban Ghana using the Oaxaca and related decompositions to quantify the gap in institutional delivery into two parts; that explained by differences in the levels of the determinants of institutional delivery between urban and rural-dwellers, and that explained by differences in the effect of the determinants on institutional delivery between urban and rural-dwellers. To our knowledge, no study has previously attempted to distinguish between the two explanations for the urban-rural disparity in the utilization of maternal health services in any international context.This study analyzed data from the three most recent rounds of the Ghana Demographic and Health Survey (DHS): a large, nationally representative household survey that collected information on fertility and family planning, infant and child mortality, maternal and child health, nutrition, malaria, HIV/AIDs, and a number of other household characteristics using questionnaires administered by trained interviewers. Additional details about the survey methodology and sampling procedures can be found elsewhere , 15.For the purposes of this study, our sample was restricted to women of reproductive age (15–49 years), and focused on their most recent births during the 5 years preceding each of the 2003, 2008, and 2014 DHS surveys. Thus, the final sample spanned the years 1999–2014 and included 13,802 births; of which 5672 (41.10%) occurred in urban areas and 8130 (58.90%) occurred in rural areas.Institutional delivery was the primary outcome of interest and was assessed using self-reported data on the location of delivery of all births that occurred within 5 years of the dates of the surveys. For analyses, it was considered a binary variable and was classified as one if a woman delivered in a public or private healthcare institution and zero otherwise.Area of residency was the key independent variable and was a binary; classified as urban or rural based on the definition used in the 2010 Population and Housing Census. Under this definition, communities were considered urban if they had a population of 5000 persons or greater and rural if they had a population of fewer than 5000 persons .Additional data were extracted from the DHS surveys and were treated as covariates in all analyses. These included factors that are known to be associated with institutional delivery and/or area of residency, such as wealth quintile , education level , parity (continuous variable representing the number of children born to a woman) and distance from a health facility (binary variable classified as one if the woman considered distance to be a barrier to accessing care and zero otherwise), as well as more general sociodemographic factors such as age , ethnicity , and religion  and pregnancy-related factors such as pregnancy complications (dummy variable classified as one if a woman had complications and zero otherwise), antenatal care visits attended (continuous variable), contraceptive use (binary variable classified as one if a woman practiced family planning and zero otherwise), and birth year . A variable denoting the year of the survey  was also included.Given that the Ghanaian government has previously introduced reforms in an attempt to increase maternal healthcare utilization and improve outcomes, we also chose to include a categorical variable denoting the reform period in which the birth occurred for all analyses, so as to control for the effects these may have had on service usage. This variable took on a value of one for births that took place outside of the reform periods, two for the first reform period, which was characterised by the provision of free maternal care in public facilities and was in place from 2003 until 2007, when it was integrated into the already functional National Health Insurance Scheme (NHIS) , and thrStatistical analyses were conducted using STATA. Descriptive statistics including means for continuous variables and percentages for categorical variables were calculated to describe the characteristics of the study sample by area of residency. The Oaxaca, Blinder, Reimers, and Cotton decomposition methods for non-linear models were used to explain the gap in rates of institutional delivery between urban and rural women. According to the Oaxaca decomposition theory, differences in the mean of an outcome for two groups can be explained by differences in the level or distribution of the determinants of the outcome (explained component), differences in the impact of these determinants on the outcome (unexplained component), and/or the interaction of the two .Y, the outcome variable, to a set of covariates, X with a vector of coefficients, β.Assume a regression model that links Yj = βjXj where j = rural, urban.urban and rural Blinder decompositionWhere Xs of the urban and rural women and that of the βs, with each multiplied by weights. Typically, the first component is weighted by coefficients while the second component is weighted by covariates.Equations (1) and (2) are equivalent and describe the decomposition of the difference between the outcomes of the groups. The three terms on the right-hand side represent the three components of the difference between the outcomes. The first two components (the explained and unexplained components) are the average differences between the The Oaxaca  decomposOther studies propose using the weighted averages of the two groups as weights. Hence, the weight of the differences in covariates is equal to the weighted mean of the coefficients of the urban and rural groups. According to Reimers , the wei(3)Decomposition EquationThe equation for the decomposition analysis conducted in this study is specified as follows:j = U, R U = urban, and R = rural.y represents the outcome variable, institutional delivery, X2 represents age, X3 represents education, X4 represents household wealth quintile, X5 represents parity, X6 represents pregnancy complications, X7 represents distance from a healthcare facility as a perceived barrier, X8 represents the maternal care reform period, X9 represents the number of antenatal visits the mother attended, X10 represents ethnicity, X11 represents religion, and X12 represents survey year. Four decompositions were estimated: one pooling the data from the three survey years used in the study, and one for each individual survey year.Where The Heckman Selection Model approach was used to account for possible selection bias wherein only women who had already given birth could be selected for inclusion. The Heckman approach is a two-stage procedure involving the estimation of the selection equation using the larger sample by probit and then computing the inverse mills ratio variable using the predicted outcome in the first stage, and the estimation of the equation of interest with the inclusion of the inverse Mills ratio variable in the second stage. In this study, the dependent variable of the selection equation was a previous delivery and the independent variables were individual-level characteristics including the mother’s age, education level, wealth status, religion, ethnicity, marital status, location of residency, and use of contraceptives. The Mills ratio computed was then added to the response eq. (3) for estimating the decompositions.Overall decompositions were computed to determine the contribution of the explained component, along with that of the unexplained component plus the contribution of the interaction to the gap in the outcome. Additionally, detailed decompositions were computed to determine the contribution of each independent variable to the explained and unexplained decompositions, as described by Kaiser . FollowiThe sociodemographic and pregnancy-related characteristics of the sample are presented in Table In terms of pregnancy characteristics, both the number of antenatal visits attended and the proportion of women who delivered in an institution were consistently higher among urban populations compared to rural. Indeed, more than 90% of urban mothers made at least the minimum of four antenatal visits versus 74% of rural mothers, although when disaggregating by year, the results show an upward trend in the proportion of women attending four or more antenatal visits for both urban and rural women, with a higher increase observed for rural women. Overall, institutional delivery rates among urban women exceeded those of rural women by 41.6 percentage points. Across the survey years, institutional deliveries increased faster for rural women compared to urban women, resulting in a narrowing of the gap from 50.6 percentage points in 2003 to 31.6 percentage points in 2014. A higher proportion of urban women reported experiencing complications during pregnancy , and rural women had, on average, one child more than urban women. Almost half of rural women (45.34%) felt that distance to a healthcare facility was a major barrier in accessing care, compared to just 17.55% of urban women.The results of the decomposition analyses are demonstrated in Table Table As discussed previously, the Oaxaca decomposition assumes that the unexplained component is due to discrimination against rural women, while the Blinder assumes it is due to favoritism toward urban women. In Table The results from the three individual surveys as demonstrated in Table Under the Oaxaca decomposition, the unexplained component, representing the contribution of discrimination against rural women to the gap in institutional delivery, increased over time: from about 34% in 2003 to 43.8% in 2014. However, under the Blinder decomposition the unexplained component, representing the contribution of favoritism towards urban women to the gap in institutional delivery, decreased over time: from 34.7% in 2003 to 31.1% in 2014. These findings are confirmed by the decrease in the advantage component and the increase in the disadvantage component observed in the Cotton and Reimer’s decompositions. With the exception of the 2008 survey, the interaction component made the smallest contribution to the gap in institutional delivery. The significant change in the unexplained component in 2008 and onward could be due to the maternal care reform in 2008 that integrated the free maternal care program into the NHIS hence expanding the free maternal care to all women, rural or urban. The resulting increase in institutional delivery rates must have been greater in the urban than rural areas due to the disadvantage conditions in rural areas. Such disadvantage could include lack of access to health facilities for the free maternal care.The contributions of each of the determinants of institutional delivery to the gap in institutional delivery rate between urban and rural women are reported in Table The results of the pooled data analysis demonstrate that education, wealth, parity, antenatal visits, region of residence, religion, and distance to a health facility all contributed significantly to the inequity in institutional delivery observed between rural and urban women, with wealth differences contributing the most. With the exception of region of residence, which mitigated the inequity, all of these factors widened the gap between the two groups. In examining the results of the individual surveys, antenatal visits, education, and wealth contributed to the widening of the gap regardless of the period, while region of residence contributed to the narrowing of the gap. Across all survey periods, wealth differences contributed the most to the inequity between rural and urban women, followed distantly by education. The proportion of the gap in institutional deliveries due to wealth differences increased from 17.7 percentage points in 2003 to 22.8 percentage points in 2008, but decreased to 11.4 percentage points in 2014, potentially due to the introduction of free NHIS enrollment for pregnant women in 2008. Despite this, wealth remained the dominant contributor to the inequality. Region of residence contributed narrowly to the closing of the gap in all periods, while the remaining variables made marginal contributions to widening it.There is substantial evidence to suggest that institutional delivery rates in developing countries are higher in urban areas compared to rural , 27. ResOur results demonstrate that over 50% of the observed gap in rates of institutional delivery among urban and rural women in Ghana can be attributed to differences in the distribution of the determinants of institutional delivery between these groups, irrespective of the decomposition type and survey year. In other words, urban women are more likely to give birth in an institutional setting because they are better endowed with the determinants that favour institutional delivery. In particular, we found that wealth, education, parity, antenatal care visits, religion, region, and distance from a health facility were all important contributors to the urban-rural gap in institutional delivery, with all factors except for region acting to widen the disparity. These results are in line with the findings of previous studies, which identified wealth, education, antenatal visits, and parity as important predictors of institutional delivery , 22, 26,Across all survey years, wealth differences contributed the most to the gap in institutional delivery. The pooled data in Table Along with wealth, differences in education levels between urban and rural women was an important contributor to the gap in institutional delivery, explaining approximately 15% of the inequality. Importantly, the contribution of education did not appear to decrease across survey years, suggesting that urban-rural inequalities in education may not be improving. The positive coefficients imply that urban women have higher levels of education on average compared to rural women. This is confirmed by Table A final important determinant of the gap in institutional delivery between urban and rural women was the attendance of antenatal visits, although its contribution to the inequity was found to decrease over time. Descriptive statistics reveal that this decline was likely the result of antenatal care visit attendance increasing more rapidly in rural compared to urban areas over the study periods, however urban women were still more likely to meet the minimum of four recommended visits. The positive relationship between antenatal visits and institutional delivery is well documented, and women who attend more antenatal visits are more likely to deliver in an institution irrespective of region .Along with differences in the distribution of the determinants of institutional delivery between urban and rural areas, the urban-rural gap in institutional delivery can also be attributed to differences in the effect of the determinants on increasing institutional delivery rates in rural compared to urban areas, although to a lesser extent. The positive coefficients of the unexplained component imply that a change in a determinant that increases institutional delivery would lead to smaller improvement in rural areas compared to urban. For example, because the quality of education is worse in rural areas , an educThe unexplained component of the decomposition, which can also be thought of as favoritism or discrimination that leads to differences in institutional delivery, contributed between 9 and 42% of the observed disparity in institutional delivery rates in urban versus rural Ghana, compared to 58.1–72.3% contributed by the explained component. The wide range of the contribution of unexplained component is due to the different weights used by the various methods. The Blinder method assuming no discrimination but only favouritism of urban women shows a high contribution of favouritism (28%) to the inequality than the Cotton (9%) and Reimer (11.1%) methods which assumed the gap is partly due to favouritism of urban women and partly due to discrimination against rural women. Similarly, the Oaxaca decomposition which assumes discrimination against rural women shows a higher contribution (42%) of the unexplained component to the inequality. Even though the results are sensitive to the weights chosen by each method of decomposition, the results consistently demonstrate that discrimination against rural women contributed more to the observed gap in institutional delivery rates than favoritism towards urban women.Given that institutional delivery is crucial in order to improve maternal mortality rates, which are disproportionately high among rural women, efforts must be made to close the urban-rural gap in institutional delivery, particularly by targeting the factors that contribute most. Effective policies to lift rural women out of poverty, for example, must be developed if any significant improvements in institutional delivery rates are to be achieved. These programs should be sensitive to rural circumstances and must address cultural barriers that may prevent women from generating an income. Indeed, the majority of rural women in Ghana work as farmers, and collectively contribute up to 70% of all agricultural labour and produce up to 70% of all food. These women face discrimination in land acquisition, however, preventing them from controlling agricultural outputs and thus earning an income, and this results in continuing poverty .In addition to wealth, programs to increase education levels among rural women must be implemented in order to improve rates of institutional delivery. Education, after all, increases a women’s earning potential and also makes her more knowledgeable about the benefits of giving birth in an institutional setting. Currently, rural children attend school at lower rates than urban children, primarily due to issues of poverty. Indeed, the widespread rural poverty that we observed in this study often requires children participate in household or agricultural labour instead of going to school, or else prevents them from paying school fees, purchasing school equipment, or travelling the, sometimes, long distances to school . Where rEfforts to increase antenatal visits in rural areas is needed to close the gap in antenatal visits between rural and urban women. The positive relationship between antenatal visits and institutional delivery is well documented thus antenatal visits in rural areas need to rise to the level of urban areas. To achieve this, antenatal services need to be available in rural health facilities. Finally, implementation of maternal programs should be location sensitive. Making free maternal healthcare accessible in rural areas may require frequent use of mobile clinics, as well as increasing the number and quality of rural health facilities. Specifically, extending the Community-based Health Planning and Services (CHPS compound) initiative to rural communities is likely to enhance healthcare utilization including antenatal care visits and institutional deliveries. CHPS compound program could be very effective in increasing antenatal visits because the program is well integrated into the community and so the health education in the program could include educating pregnant women to go for antenatal care and deliver in health facilities.This study was not without limitation. First, the data analyzed here is relatively outdated; thus, the findings may not accurately reflect the current situation in Ghana with regards to institutional delivery. While more recent survey data does exist, it was not collected using the DHS but rather a different survey instrument, and may therefore not be comparable to earlier data. As such, we chose to limit our analysis solely to DHS data. Second, this study made use of self-reported data, which is vulnerable to recall bias, particularly given that each survey recorded information for the 5 years preceding the survey. Because the survey focused on births, however, which are considered to be big events that are not soon forgotten, we expect the accuracy of the data to be acceptable.Using various decompositions, the present study of inequalities in institutional delivery in urban compared to rural Ghana found that the rates of institutional births among urban women exceed those of rural women by approximately 32.46 percentage points. This gap was primarily the result of differences in the distribution of the determinants of institutional delivery between urban and rural women, and wealth disparities were particularly important, contributing around 16.1% of the inequality. Future interventions to promote institutional births in rural Ghana should therefore focus on economic empowerment, and should aim to reduce the existing financial barriers that prevent rural women from accessing health care. Additional research is required in order to identify the policies and programs that may be most effective at lifting rural women out of poverty. The gap is also due to the discrimination against rural women. Implementation of maternal or healthcare programs including the free maternal care should take the disadvantaged condition of rural women into account to ensure effectiveness of such programs in rural areas.


Dear Editor,1 with great interest and would like tocontribute with some suggestions.We read the article entitled "Lifestyle Intervention on Metabolic Syndrome and its Impacton Quality of Life: A Randomized Controlled Trial", by Saboya et al.2 since theinterviewers, even unconsciously, may influence the responses and their view of theparticipants.Firstly, regarding the method. There are no reports about the blinding of the evaluators,both for the interviews and for body mass index and waist circumference measurements.This is a factor considered a high risk of bias by the "Cochrane Collaboration's toolfor assessing risk of bias"1 The prevalence of depression and anxietywas 41.7% and 22.2%, respectively, and these data are not associated with the metabolicsyndrome components.Secondly, regarding the results. The researchers reported that the quality of life ofpatients with metabolic syndrome is affected not only by the clinical picture but isalso significantly affected by the presence of depression and anxiety.Anxiety and depression have often been associated with metabolic syndrome, as well asother non-transmissible chronic diseases, due to the limiting characteristic the diseasehas on the individuals' lives. The present study did not demonstrate this association,perhaps due to the sample size, which was too small for a study with so many stages andvariables for assessment.4that associate metabolic syndrome with depression and anxiety, and also evaluate thesepatients' quality of life, have a larger sample size.The substantial loss of subjects may have compromised the results. Most studies