Decision-making Research Articles

Informing decision makers: development of online news media monitoring and visualisation

Informing decision makers: development of online news media monitoring and visualisation

EP120 Adoption of a "mass casualty" model of triage on the surgical SDEC. A model to allow prioritisation of the Senior Decision Maker resource

eP120 Adoption of a "mass casualty" model of triage on the surgical SDEC. A model to allow prioritisation of the Senior Decision Maker resource

A framework for ecologically and socially informed risk reduction before and after outbreaks of wildlife-borne zoonoses.

Despite increasing emphasis being placed on the inclusion of upstream ecological and social perspectives for zoonotic disease control, few guidelines exist for practitioners and decision makers to work with communities in identifying suitable, locally relevant interventions and integrating these into public health action plans. With an interdisciplinary group of Kenyan stakeholders, we designed and tested a comprehensive framework for the co-design, evaluation, and prioritisation of beneficiary-oriented, ecologically and socially informed interventions for preventing and controlling outbreaks of wildlife-borne zoonoses. Our approach used four globally important wildlife-borne pathogens-Rift Valley fever virus, Congo-Crimean haemorrhagic fever virus, and the causative agents of anthrax and rabies-enabling stakeholders to develop a shared understanding of complex transmission pathways, identify a broad array of measures targeting ecological, biological, and social processes governing outbreaks of these pathogens, and explore trade-offs for specific interventions. The framework can be applied early in the decision-making process to encourage broader, cross-sectoral co-production of knowledge, ideas, and consensus on the control of complex zoonotic diseases.

A Sequential Calibration Approach to Address Challenges of Repeated Calibration of a COVID-19 Model.

Mathematical models served a critical role in COVID-19 decision making throughout the pandemic. Model calibration is an essential, but often computationally burdensome, step in model development that provides estimates for difficult-to-measure parameters and establishes an up-to-date modeling platform for scenario analysis. In the evolving COVID-19 pandemic, frequent recalibration was necessary to provide ongoing support to decision makers. In this study, we address the computational challenges of frequent recalibration with a new calibration approach. We calibrated and recalibrated an age-stratified dynamic compartmental model of COVID-19 in Minnesota to statewide COVID-19 cumulative mortality and prevalent age-specific hospitalizations from March 22, 2020 through August 20, 2021. This period was divided into 10 calibration periods, reflecting significant changes in policies, messaging, and/or epidemiological conditions in Minnesota. When recalibrating the model from one period to the next, we employed a sequential calibration approach that leveraged calibration results from previous periods and adjusted only parameters most relevant to the calibration target data of the new calibration period to improve computational efficiency. We compared computational burden and performance of the sequential calibration approach to a more traditional calibration method, in which all parameters were readjusted with each recalibration. Both calibration methods identified parameter sets closely reproducing prevalent hospitalizations and cumulative deaths over time. By the last calibration period, both approaches converged to similar parameter values. However, the sequential calibration approach identified parameter sets that more tightly fit calibration targets and required substantially less computation time than traditional calibration. Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters and potentially identifies better-fitting parameter sets than traditional calibration. This study used a sequential calibration approach, which takes advantage of previous calibration results to reduce the number of parameters to be estimated in each round of calibration, improving computational efficiency and algorithm convergence to best-fitting parameter values.Both sequential and traditional calibration approaches were able to identify parameter sets that closely reproduced calibration targets. However, the sequential calibration approach generated parameter sets that yielded tighter fits and was less computationally burdensome.Sequential calibration is an efficient approach to maintaining up-to-date models with evolving, time-varying parameters.

Primary care in the COVID-19 pandemic and beyond: Lessons from Ontario.

To understand the role of primary care in the COVID-19 pandemic to provide insight into its functioning and inform potential reforms. The now dissolved Ontario COVID-19 Science Advisory Table (Science Table) was formed in July 2020 to provide decision makers and the public with a synthesis of rapidly evolving evidence related to COVID-19. The Science Table was based at the Dalla Lana School of Public Health at the University of Toronto, and supported by Public Health Ontario. Authors worked with the leadership and secretariat of the Science Table to synthesize evidence and inputs. Authors drew on their expertise in research, policy, and front-line care delivery and coupled this with data analysis and reviews of the literature relevant to the topic areas discussed. Data analysis and literature reviews were done with the support of the Ontario Medical Association, the INSPIRE-Primary Health Care research program, and the Department of Family and Community Medicine at the University of Toronto. Experts conducted a rapid review of the briefs prior to publication, and authors presented the briefs' content at a series of meetings attended by Science Table members for their input. As Science Table briefs were intended to provide rapid-response answers to important health system questions in real time, the intent was not to conduct a systematic review but rather to gather available relevant evidence and present it in a form that could be used by policy-makers. This summary describes the work of primary care during the COVID-19 pandemic in Ontario up to September 2022; outlines current challenges in primary care capacity and structure; and makes recommendations for strengthening the sector to better address population needs for current and future public health priorities. While the focus is on Ontario, many of the findings are relevant to other jurisdictions in Canada and elsewhere. Universal formal attachment to an accountable interprofessional primary care team supported by adequate infrastructure should be the cornerstone of pandemic recovery planning.

The Role of Sustainable Manufacturing Practices in Enhancing Business Competitiveness

This study examines the relationship between sustainable manufacturing practices and business competitiveness in the firms of the Lasbela Industrial Zones Balochistan of Pakistan. Data was collected by using a quantitative research design for a sample of 50 business owners, managers as well as the key decision makers using structured questionnaires. Demographically, the majority of respondents are male between the ages of 25 and 70, a demographic that typically fits the mold for leadership roles in manufacturing in this part of the world. And findings suggest a big shift towards sustainability with 70 percent of businesses using some kind of sustainable practice. Also, 60 per cent of respondents indicate how these practices bring about a positive corporate contribution to their competitive advantage and that such sustainability could improve organizational effectiveness and market positioning. While it’s positive that this encouraging behavior trend has formed, 55% of respondents still pointed towards high costs as a major barrier to mainstreaming more sustainable initiatives. This shows the importance of a more sympathetic environment to fund financial incentives and subsidies to offset these costs. Moreover, 65% of participants find that there is a need for additional training and resource in order to strengthen efforts in sustainable manufacturing, highlighting the need for capacity building for a transition to sustainable manufacturing. It particularly highlights the growing recognition of sustainability in the Lasbela Industrial Zones and shows the salient obstacles to tap the most from the sustainability initiatives. Firms in Balochistan can gain by focusing on these areas, to enhance their ability to use sustainable initiatives to increase competitiveness, both in local and international markets, thereby aiding overall regional economic development.

EXAMINING AGE BIAS IN CHATGPT RESPONSES TO USER QUESTIONS ABOUT DECISION-MAKING IN ADVANCED CANCER

Abstract ChatGPT is an online, AI-powered chat application that provides human-like responses to user input. There is concern about bias in ChatGPT responses based on user demographics, yet no existing research has examined age bias in ChatGPT responses to questions about decision making (DM) in advanced cancer. The current study examined the content of ChatGPT responses to a hypothetical patient question about DM in advanced cancer. Replicating Nastasi and colleagues’ (2023) approach, we posed a medical-advice-seeking vignette to ChatGPT with variations on four hypothetical patient characteristics: age (45/65/85 years), race (Black/White), ethnicity (Hispanic/Non-Hispanic), and insurance status (Insured/Not Insured). Each vignette ended with: “Should I look for other treatments or focus on my quality of life?” Three coders categorized responses (ICC=.95) for mentions of nine topics relevant to DM in advanced cancer (e.g., consideration of clinical trials, suggestion to pursue second opinions, descriptions of hospice and palliative care, and encouragement to talk to providers for DM support). ChatGPT responses more frequently mentioned consideration of clinical trials to patients who were 45 compared to patients who were 65 and 85 years old (X2(2,N=96)=8.6, p=.01). There was a non-significant trend toward ChatGPT responses suggesting pursuit of second opinions more frequently for younger patients (X2(2,N=96)=3.0 p=.22). There were no age differences in mentions of hospice care (p>.50), palliative care (p>.30), or talking to providers for DM support (p>.30). Findings indicate that ChatGPT responses to questions about advanced cancer DM may contain age bias. Future research should address how ChatGPT users navigate bias in responses.

Analysis of the relation between multicriteria decision-making methods and human decision strategies

Purpose This paper aims to investigate the relation between multiple criteria decision-making (MCDM) methods and the decision strategies used by the individual in the process of choosing in a specific context. Design/methodology/approach The method involves an experiment in an automobile-selling shop with 61 individuals performing the task of choosing a used vehicle independently: with and without the use of a decision support systems (DSSs) also using the Think Aloud verbal protocol. The paper used the descriptive statistics and signals test to test hypotheses. Findings The results prove the relation between two of the four analyzed decision-making (DM) strategies: additive difference and elimination by aspect. These two strategies exhibited significant differences for the TransparentChoice and M-MACBETH systems, being similar only for Visual PROMETHEE, and this system being the one that best facilitated the choice process for consumer goods, in this case, used vehicles. Research limitations/implications This study enhances understanding of decision strategies and multicriteria methods in used vehicle selection. Identifying Visual PROMETHEE as highly effective, it suggests that design and usability features play a pivotal role. However, limitations, such as the temporal cutoff and exclusion of electric vehicles, hinder a comprehensive grasp of DM processes in contemporary vehicle choices. Nonetheless, applying these insights to future contexts broadens theoretical contributions, highlighting a key potential of this study. Practical implications The study provides valuable insights into the factors influencing consumer decisions, potentially impacting purchasing patterns and, consequently, the used vehicle market. In addition, it underscores the need to tailor the design and functionalities of DSS to better align with users’ DM approaches, aiming to maximize the effectiveness and acceptance of these systems. Social implications The social contribution of this research lies in its ability to raise awareness among consumers about effective DM strategies, particularly those related to DSSs. This can be achieved through educational initiatives that inform consumers about the benefits of these systems when facing complex decisions, such as purchasing used vehicles. Furthermore, the study’s results have the potential to influence public policies in the automotive industry by allowing regulatory adjustments that promote practices aligned with consumer preferences and needs. This consumer-centric approach may lead to policies more tailored to market dynamics, fostering competitiveness and consumer satisfaction. Originality/value The innovative approach tested three types of DSS in an experiment, exploring their relationship with decision strategies in the specific context of used vehicle selection. This unique methodology contributes to the understanding of DM dynamics, particularly in the automotive scenario, providing a valuable perspective for literature and relevant professionals.

A note on Polynomial Centro-Symmetric Hesitant Fuzzy Matrices

Classical and its development towards fuzzy got a wide acceptance due to its divergent real-life applications. Matrix theory is a special tool that is widely used in solving problems of different mathematical situations. Matrix theory can be used in versatile ways in the new concept. Crisis with fuzzy uncertainties are dealt in a good manner with fuzzy matrices. Hesitant Fuzzy Set in an excellent tool for a decision maker to analyze a situation with various possible values. Apart from usual matrices, there exist several useful matrices. Some of them are very helpful in solving complicated practical problems than the existing typical ones! This paper introduces polynomial centro-symmetric hesitant fuzzy matrix with basic properties.

Classification of Inventory Items by Group Decisions Protocol

Companies classify inventory items using multiple-criteria via ABC analysis principles and then apply inventory management policies in accordance with the assigned classes’ characteristics. Different kinds of models, such as; linear programming, analytic hierarchy process (AHP), cluster analysis, and etc. are used in those analysis. In the results obtained in the models used, the scores and classes of the stock items differ. This situation is challenging for decision makers. Managers often need to unify results obtained using multiple models to make more accurate decisions. To this end, in this paper, a group decisions protocol, Borda–Condorcet rule (Herrero C, and Villar A., 2021) is used to classify inventory items, classified using multiple criteria ABC models.

Incidental affect influences choice preference among competing alternatives in a modified Affect Misattribution Procedure

Affective feelings exert a powerful influence on decision making, even when the source of those feelings is incidental, i.e., unrelated to the decision at hand. Research on the role of affect in decision making has typically focused on how incidental affect shapes evaluations of an individual target, and thus decisions about how to engage with that target. It is less clear, however, if and how individuals use their incidental affective feelings when evaluating and comparing multiple competing targets and deciding which one to choose. To investigate this, we modified the Affect Misattribution Procedure (AMP) to include two competing targets, presented sequentially, that individuals needed to choose between. In two pre-registered studies (N = 196 and N = 214), participants were presented with pairs of landscape images (e.g., beaches, lakes) and asked to choose which image in the pair they liked more. Each landscape was preceded by an affective prime: a briefly flashed image of a face that was either smiling (a positive prime), scowling (a negative prime), or neutral (a neutral prime). We found that participants were significantly more likely to choose landscapes preceded by primes of more positive valence, and this effect was driven by trials on which the positive prime came second. Our studies demonstrate that decision makers use their incidental affective feelings when making choices among competing alternatives, and introduce a novel methodology for understanding the constructive role of affect in preference formation.

An Analysis on Sustainable Automotive Car Body Material Using Weighted Sum method

Introduction: In order to reduce its influence on the environment, the automobile industry must overcome a significant obstacle and switch to sustainability car body materials. This overview examines the demand for cutting-edge materials that enhance vehicle performance, increase recycling, and minimize carbon emissions. The sector can meet expanding transportation demands while promoting a greener future by using sustainable options. The automotive industry plays a major role in the global carbon footprint, with car body products being a major contributor. As sustainability becomes a major concern, the need for eco-friendly alternatives to traditional materials is clear. This introduction explores the concept of sustainable automotive car body materials, highlighting their importance in reducing emissions, conserving resources and promoting a greener future. By exploring innovative solutions, the industry can embrace sustainability while maintaining high-performance standards. Research significance: Research on sustainable automotive car body materials is significant in addressing the pressing challenges facing the automotive industry. By focusing on the development and use of environmentally friendly alternatives, this research aims to reduce the environmental impact of the industry. By reducing carbon emissions and promoting resource conservation, sustainable car body materials can contribute to a greener and more sustainable future. Furthermore, this research has the potential to improve energy efficiency and vehicle performance, leading to safer and more efficient vehicles. Additionally, by meeting growing consumer demand for sustainable options, this research will help automakers gain a competitive edge in the market and align their products with the values and expectations of environmentally conscious consumers. Method: The weighted sum method is a decision-making technique that assigns weights to different criteria and calculates an overall performance score of the alternatives. By assigning relative importance to each criterion, this method allows decision makers to objectively evaluate options and make informed choices. The weighted sum method combines the weighted scores of each criterion to obtain a comprehensive performance measure that enables a systematic approach to decision making. The method provides a structured framework for analyzing complex decision-making problems, finding applications in fields as diverse as project selection, supplier evaluation, and product design. Alternate parameters: Steel Alloy (AHSS), Aluminium Alloy (A7075 T6), Carbon Fiber/ epoxy laminate (CL), Titanium Alloy (Ti6Al4 V). Evaluation parameters: Tensile Strength (Mpa), Stiffness (Gpa), Damping capacity, Cost (Rs), CO2 Emission (Ton/Ton), Results: The Steel Alloy (AHSS) is in 1st rank. The Aluminium Alloy (A7075 T6) is in 4th rank. Carbon Fiber/ epoxy laminate (CL) is in 2nd rank. Titanium Alloy (Ti6Al4V) is in 3rd rank. The result is done by using the WSM method. Conclusion: sustainable automotive car body material in the Steel Alloy (AHSS) is in 1st rank. The Aluminium Alloy (A7075 T6) is in 4th rank. Carbon Fiber/ epoxy laminate (CL) is in 2nd rank. Titanium Alloy (Ti6Al4V) is in 3rd rank. The result is done by using the WSM method.

A Socioecological Approach to Support the Transition to Adult Care for Youth With Medical Complexity: Family Perspectives and Recommendations

ABSTRACTIntroductionThe transition from paediatric to adult health care (i.e., ‘health care transition’) poses many challenges for youth with medical complexity (YMC) and their families. YMC need specific approaches to supporting transition, tailored to individual youth and family contexts. In this study, we examine the contextual factors influencing families' transition experiences and describe their recommendations for improving the experience.MethodsWe conducted a qualitative explanatory case study in Ontario, Canada. We completed 21 interviews with 17 participants (11 mothers, 2 fathers, 2 YMC, 2 siblings) from 11 families of YMC. Six YMC (55%) were under 18 years of age (pre‐transfer) and five (45%) were aged 18 years and older (post‐transfer). Analytic approaches included reflexive thematic analysis and directed content analysis.FindingsParticipants described how the interplay of personal and environmental factors impacted their transition experiences. Recommendations for health care providers focused on providing instrumental and psychological support, advocacy and care continuity. Families expressed a need for better access to information and support from primary care providers. System‐level recommendations included streamlining transition processes, improving adult health care services and expanding community supports. A socioecological model is presented to guide health care providers and decision makers in assessing and tackling the challenges faced by YMC and their families during transition.ConclusionFindings highlight the complexity and scope of issues surrounding the transition to adult care for YMC in Ontario, with evidence of major gaps in services across multiple sectors and settings. Ongoing efforts are needed to move evidence into practice and advocate for more equitable and responsive care for YMC during the transition and beyond.Patient or Public ContributionThe research team included two parent co‐researchers with lived experience, who contributed to protocol refinement, funding acquisition, recruitment, findings interpretation and ongoing knowledge translation efforts.

Involvement Of Males In Utilization Of Maternal And Child Health Services: A Cross-Sectional Study Of Women’s Perspective

Background: Men in patriarchal societies of developing countries like India are often identified as decision makers in all aspects of life. Men’s supportive stance in MCH services utilization plays crucial role in reduction of maternal and child mortality. Aim: To study the involvement of males in maternal and child healthcare services. Objective: To study sociodemographic profile of study participants and knowledge and practices of their husbands about MCH services. Material and methods: The cross-sectional study was conducted among 130 married women having one living child less than 2 year of attending immunization clinics of Government medical college and hospital, Miraj. Information about socio-demographic and reproductive health characteristics, male participation in reproductive and child health care services was collected entered in MS excel and analyzed. Results: Among the females 70% were below 25 years of age and age of the husbands of 71% participants were less than 30 years. 64% females were belonging to Muslim religion, 84% were residing in urban area, 82% stayed in joint family, 58% educated up to secondary school, 67% belongs to socio-economic class IV. 61% husbands were decision maker in family planning, 78% husbands accompanied for ANC visit,79% husbands knew about the govt. MCH scheme 94% husbands knew about increased need for food & rest by pregnant women, 63% were having knowledge about complications of pregnancy and delivery. But only 22% husbands accompanied for child’s immunization. Only 34% husband help in domestic work, 63% paid attention on diet/food frequency of their wives and 51%encouraged & paid attention on adequate rest of their wives. Conclusion: Men are aware of various aspects of antenatal and intra natal care, their husband’s involvement during antenatal period was found to be good but it decreases during immunization phase of the child. Involvement for helping in domestic work, encouraging for adequate rest & food during ANC was also less. So, creating specific Health education and SBCC activities about maternal & child health among men may enhance their positive involvement.

Some Comply While Some Defy: Elementary Teachers' Responses to Leadership Mandates on Reading Comprehension Instruction.

We report the qualitative findings from a survey of elementary teachers regarding reading instruction. The purpose is to extend on quantitative findings in a previously described survey to gain a more in-depth understanding of Australian elementary teachers' approaches to the literacy block in their schools: how this is used, who makes instructional decisions, and how teachers respond to these. A web-based survey was used to collect data from Australian elementary teachers about their perspectives regarding reading comprehension instruction. Of the 284 respondents, 182 (64.1%) provided free-text comments in response to open-ended questions. Responses were coded and analyzed for themes. Responses were categorized into three broad themes. The first theme concerned the level of satisfaction with how literacy block time was used. The second theme pertained to the locus of decisions about the use of the literacy block time, and the third theme concerned the ways in which teachers responded to decisions made by "leadership": compliance or defiance. There are many different ways that the literacy block is structured and used across Australian elementary schools, and participants indicated an overall sense that they perceive that their literacy block is effective for students. There is, however, evidence of tension between teachers and decision makers when the teachers' pedagogical position does not align with the locally prescribed instructional approach. In these cases, teachers report choosing to either defy or comply with the mandated approach. Although formal decisions about how this block is to be used are made by school leaders, teachers make individual decisions about the degree to which they comply with those. Departures from these mandates, regardless of their rationales, have significant implications for data monitoring and interpretation at the school level.

Moving Ottawa's Department and Agency Reporting Forward: Encouraging Accountability and Sustaining Reform

AbstractGovernments produce a steady stream of data, reports, and other information to ministers, legislators, and the public, and generate enormous flows of administrative data. In the digital era the flows of information generated by departments and agencies have expanded by orders of magnitude. The paradox, though, is that much of this information is never used, but many decision makers believe there is insufficient information to meet their specific needs at any time. This note focuses on two streams of reporting by departments and agencies in the Government of Canada informed by “open government” principles—the streams of information supplied via the Canada InfoBase and other reporting, and the Management Accountability Framework—and considers if they are accessible and useful. After setting out options for making them both more useful, this note argues it will inform the work of spending reviews, Parliamentary committees, and engagement with experts and citizens.

Machine Learning Solutions for Predicting Stock Trends in BRICS amid Global Economic Shifts and Decoding Market Dynamics

In this paper we examine the role of machine learning (ML) in predicting stock market trends within BRICS economies. Complex, interdependent global and regional economic factors are today and will in the future increasingly influence stock markets, which necessitates innovative techniques for trend analysis. Using this state of the art ML models Support Vector Machines (SVMs), Random Forests, and neural networks the study predicts market fluctuations based on historical stock data, economic indicators and geopolitical events. This research emphasizes the increasing role of deep learning, especially with models such as Transformers and LSTMs, to meet the demand for high accuracy predictive systems in the volatile market. Its analysis brings model performance into comparison of BRICS nations, taking into account the peculiar financial and economic behavior peculiar to each of them. These results illuminate how ML can provide actionable intelligence for investors and policymakers to better manage risk and better make strategic investments. Findings from the study underscore the requirement for adopting sophisticated data driven tools in order to negotiate the intricacies of globalized financial systems. This study also explains the basis in helping us understand how machine learning changes the perspective on stock market analysis. It is a great source to understand how different ML techniques such as Support Vector Machines which are good at classification problems and Random Forests, which are known to handle over fitting, can be used on a financial dataset. It shows cutting edge tools for market prediction such as deep learning models like LSTMs, which are able to handle sequential time series data, or Transformers that further improve the traditional architectures with attention mechanisms. The paper also discusses data preprocessing methods, such as feature engineering and normalization, and the importance of their inclusion in improving model performance. This research shows the value of ML literacy and provides future financial analysts and decision makers with tools for addressing market volatility in a data driven and strategic context.

Justification of strategic decisions on development of the transport and logistics system’ elements of the northern region

Subject. The formation of a full-fledged transport and logistics system based on large–scale infrastructure projects is the basis of the modern concept of the development of the Russian North and the Arctic. However, in the current geopolitical conditions and the influence of a number of environmental, social, economic and other factors, decision makers often face problems in justifying and choosing a particular project. Objectives. The study aims to offer a procedure for substantiating the choice of strategic alternatives for development of elements of the transport and logistics system, based on expert assessments, to management decision makers at regional and municipal levels. Methods. The study rests on the systems approach, methods of economic analysis, expert analytical technologies, and matrix positioning of management decisions. Results. Using the case of the Arkhangelsk transport hub, we considered three projects related to its development. A number of expert interviews enabled to identify the most significant factors and their groups that influence the condition and development of the territory during each project implementation. We determined weights of groups of factors and their intragroup weights, the degree of consistency of expert judgments, the leading factors in the groups, and provided estimates of relevant correlations. Conclusions. We unveiled significant differentiation of the considered projects in terms of totality and strength of influence of groups of factors affecting the territory development. The findings can be used as information basis for justifying and choosing the priorities of their implementation.

I see therefore we are: the potential for aggregating individual future visions into a collective imaginary through artificial intelligence (AI)

Purpose This analysis draws on ethnographic research in a Northern UK city where a series of engagement activities produced hand-drawn sketches about the future. This paper shows how different groups revealed contradictory aspirations for the area with growth-focused politicians and planners projecting affluent prosperity rather than the modest, family-oriented social stability sought by local people. Design/methodology/approach Reconciling multiple perspectives is the greatest challenge of civic leadership. This paper considers how the emerging potential of artificial intelligence (AI) could support a structured imagining process for masterplanners to aggregate aspirational sketches at scale and so develop a closer relationship between citizen ideas for the future and civic decision makers’ own strategies and action plans. Findings This paper argues for a collective intelligence paradigm that aggregates individual futures thinking at city scale. Urban masterplanning strategies provide an organising structure to allow a city to emerge as a flourishing of multiple aspirations all at once. Research limitations/implications Rather than having individualism or collectivism as binary alternatives, generative AI offers an intriguing process for combining individual aspirations into a pluralist endeavour. Practical implications Engaging with citizens on the ground, in their homes and community spaces is the only way to uncover what is important to them. Ethnography provides that. Social implications Although AI’s use of aggregated data is collective, intelligence comes from nuanced and ethnographic engagement with the data. Originality/value The principle of the emergent city (Symons 2017) provides a conceptual approach to amalgamating the individual with the collective.

When Should Colon Cancer Screening Begin? The Impact of Early-Onset Colorectal Cancer and the Reality of an Unscreened Older Population.

Recent increases in colorectal cancer (CRC) incidence and mortality under age 50 have led the US to recommend starting screening at age 45years instead of 50. Several other countries are now also reconsidering the age to start CRC screening. To aid decision makers in making an informed decision about lowering the starting age of CRC screening in their jurisdictions. In this article, we present the clinical and modeling evidence for the optimal age to start CRC screening and provide a checklist of considerations for decisions on age to start CRC screening. Two observational studies showed that detection of advanced neoplasia in those aged 45-49years undergoing colonoscopy was at least as high as in those aged 50-54years. One Taiwanese study reported a 22% reduction in CRC incidence and a 39% reduction in CRC mortality from FIT screening in those 40-49years compared to those 50years and older. Nine modeling studies concluded that lowering the age to start screening to age 45 was cost-effective. However, lowering the start age can have negative spill-off effects, such as increased wait times for diagnostic colonoscopy for symptomatic individuals and decreased screening participation. In an effort to support decision making and prevent negative spill-off, the National Colorectal Cancer Screening Network in Canadaproposed a Worksheet to determine the resource impact of earlier screening initiation. Lowering the age to start CRC screening to 45years likely leads to a reduction in CRC incidence and mortality but requires additional healthcare resources. Policy makers can use the worksheet to assess the expected increase and assess the feasibility within their jurisdictions.