Understanding dynamics to Streamline Access: Systems Engineering and Agent-Based Probabilistic Modelling in Memory Assessment Services

Understanding dynamics to Streamline Access: Systems Engineering and Agent-Based Probabilistic Modelling in Memory Assessment Services | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding dynamics to Streamline Access: Systems Engineering and Agent-Based Probabilistic Modelling in Memory Assessment Services Reinhold Scherer, Mohammadreza Jamalifard, Benjamin R. Underwood, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5898259/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background As the global population ages, healthcare systems must anticipate and manage the interactions of older adults with dementia and other long-term conditions. Optimising dementia assessment and diagnosis services is essential to enhance efficiency and improve patient experience. Solutions must address the evolving needs and complexity of patients, caregivers, and healthcare services. Aims We sought to develop a comprehensive model of a memory assessment service (MAS) to examine how patients, unpaid carers and health system factors influence service performance. The primary focus was to gain insight into how these factors affect the time from referral to diagnosis and to identify evidence needed for future modelling work. Methods We used systems engineering and an agent-based probabilistic modelling approach, informed by real NHS clinical data, to simulate the MAS. To reflect the experience of most people attending memory assessment services our model included the impact of multiple long-term conditions (MLTC) and if the patient had a known carer (dyad). Results We found that system dynamics and behaviour change based on the pressure the system is under, with a tipping point in the length of the waiting list after which performance is consistently impacted. The impact of staff absences appears to be less significant than comparable levels of patient non-attendance. High levels of triage adversely affected service performance, whereas increased staffing levels and, in particular, reduced administrative burdens improved performance. Conclusions We have demonstrated the feasibility of constructing sophisticated models of memory assessment services, incorporating multiple factors to study behaviours that emerge from interactions in the system. Such models could be valuable tools for NHS managers, not only to optimise the efficiency of services but also to develop new approaches to service delivery. Operations Research Computational Mathematics Medical Informatics Dementia multiple-long term conditions patient-carer dyads systems engineering agent-based modelling (ABM) probabilistic models complex systems Figures Figure 1 Figure 2 Introduction Dementia is a leading cause of mortality and morbidity worldwide. As the global population ages, the prevalence of dementia is projected to increase significantly. For example, the number of individuals in the UK with dementia is expected to rise from approximately 1 million in 2024 to 1.4 million by 2040 (Alzheimer Society, 2024 ). This growing prevalence underscores the urgent need for highly efficient services designed around persons with dementia and other long-term conditions, as well as their family carers (Whitty, 2023 ). Access to National Health Service (NHS) services is a key priority for both this group and the NHS. One way to optimise and gain deeper insight into the dynamics of services is by building models and simulations that allow in silico alterations to predict their impact on service efficiency. Some of these factors (for example number of staff) are relatively simply modelled. However, crucially, this does not reflect the complexity of either the services or of the people accessing them. For example, two critical issues for people with dementia are the presence of a carer (i.e. a family member or friend who helps them navigate the services and manage the condition) (to form a ‘patient-carer dyad’) and the common occurrence of dementia as part of a more complex burden including multiple long-term conditions (MLTC). Managing MLTC makes access to healthcare services more difficult. (Rees et al., 2023 ; Yew et al., 2024 ) This is due to a number of factors, including increased healthcare needs, complex care coordination, transportation and mobility issues, psychosocial factors, and a higher risk of hospitalisation. This paper uses systems engineering, quantitative modelling, and computer simulation approaches based on real-world NHS data to model patients with dementia, including those with multiple long-term conditions (DiMLTC), from general practitioner (GP) referral to dementia diagnosis. (Ramos et al., 2011 ; Royal Academy of Engineering, 2017 ; Aujla et al., 2024 ) We included both straightforward and more complex factors, including the impact of multimorbidity and patient carer dyads, to build a model that will have clinical utility in enhancing efficiency for memory assessment services. Methods Mapping and validating patient-carer dyad pathways The pathways for DiMLTC patients, from GP referral to diagnosis at the Memory Assessment Service (MAS), have been mapped in collaboration with clinical professionals from Cambridgeshire and Peterborough NHS Foundation Trust (CPFT) and summarised in a directed state graph (see Fig. 1 ). In this graph, the nodes represent different states in the process, while the directed edges illustrate the transitions between them. When referred by a GP, patients may live alone, with family, in assisted living or in a care home. The triaging service, conducted remotely by a team of clinicians, typically occurs the next working day following receipt of referral. During triaging, referrals are divided into two categories based on clinical risk: High-risk cases are scheduled for a MAS appointment at the earliest possible date (typically within 6 weeks), while low-risk cases are placed on a waiting list. Individuals, some of whom are part of dyads (i.e. have a carer), on the waiting list remain there until the MAS contacts them with an appointment. A diagnosis is usually made on the day of the MAS appointment. If further information is needed (for example the results from imaging), a new MAS appointment is arranged at the earliest possible time; otherwise, a diagnosis is made. At any point, a medical episode may occur requiring patients to be hospitalized, necessitating them to be discharged from hospital, recover and a new appointment scheduled before the diagnostic process can continue. This event is more likely to happen in those suffering MLTC. Additionally, patients on the waiting list may convert from low-risk to high-risk and need to be prioritized, and an MAS appointment will be scheduled. In some cases, patients may need to relocate to a care facility prior to assessment if their condition progresses. To ensure these pathways accurately reflect real-world scenarios, they were discussed with relevant stakeholders, including people living with dementia, carers, and service providers. Ethical permission for interviews and co-production workshops was obtained (London - Bromley Research Ethics Committee, reference 23/LO/0829). Interview and co-production workshop participants provided written informed consent. People living with DiMLTC and carers retold their experiences and frustrations regarding long waiting times for diagnosis. This discussion, combined with the outcomes of a realist review (Handley et al., 2025), provided valuable insights into the interactions between these groups and the healthcare system. It also led to the identification of key factors expected to significantly impact the diagnosis process and overall care. Identified key factors are: DiMLTC patient : Age, Sex, Number and type of MLTC, Known to services (i.e. medical history, treatment records, and other relevant information are already on file), Place of residence, Postcode, Has personal transportation available, Ethnicity, Language. Caregiver : Relationship to patient (e.g., spouse, daughter, son, friend, etc.), Co-residence with patient, Age, Number and type of MLTC, Postcode, Has personal transportation available, and Employment status (e.g. full-time, part-time, unemployed, etc.). Services : Location, Workforce management (e.g., service hours, number of consultants, junior doctors, and nurses, their working hours and workload distribution), Communication channels (e.g., letters, phone, email, etc.), and Cultural sensitivity (i.e., recognize, respect, and effectively respond to the diverse cultural backgrounds, beliefs, and practices of patients/dyads). Service level and patient population data Integrating empirical data into models significantly enhances their precision and relevance, providing a robust foundation for accurate simulations and informed decision-making. This comprehensive approach ensures that the model not only reflects theoretical pathways but also aligns closely with the practical experiences of the various services and professional disciplines involved in the dementia care process. To gain a realistic insight into the patient population characteristics and determine transition probabilities between states within the pathways (see Fig. 1 ), data from CPFT was used. CPFT provides mental and community health services for a population of approximately 1 million people including more than 165,000 people over the age of 65. The MAS service receives about 3,000 referrals for memory assessment per year. We used service-level data, including the number of referrals and staff, the average time for appointments and administration, and the percentage of patients triaged as high-risk as part of a trust approved service evaluation. To better understand the patient population, we conducted a search using the Clinical Records and Text Extraction (CRATE) database. This resource is a pseudonymised copy of the electronic patient record. It has overarching NHS ethical approvals (12/EE/0407, 17/EE/0442, 22/EE/0264). The CPFT Research Database Oversight Committee further approved this study. The search strategy included the following criteria and Boolean operators: (“Patients over the age of 65 with a diagnosis of dementia (ICD10 code starting F00, F01, F02, F03)” AND “Recorded Rockwood Clinical Frailty Score” AND “Recorded Yes or No to carer ”). We applied filters to include data from the past 3 years. The Rockwood Clinical Frailty Score (RCFS) assesses frailty in older adults, ranging from 1 (very fit) to 9 (terminally ill). In the current study, we used the RCFS as a proxy for MLTC. While the RCFS is not a direct proxy for MLTC, it does incorporate these conditions. In our simulation, patients with higher RCFS scores had higher probabilities of suffering adverse medical events due to MLTC, requiring recovery before a diagnosis could be made. Data extracted from the CRATE database included: Basic demographic data including age, sex, diagnosis (and date of dementia diagnosis), ethnicity, marital status, Health of the Nation Outcome Scale (HoNOS) scores and cognitive scores including the Addenbrooke’s Cognitive Examination (ACE), Number of lifetime prescriptions, Deprivation index, Rockwood Clinical Frailty score (RCFS), Presence of carer yes or no, Admission to hospital – psychiatric, Admission to hospital – general, Number of calls to first response service (FRS), an emergency service for mental health (111 option 2), Care from psychiatric crisis team, Death, and CPFT physical healthcare service use. All variables were as recorded at the time of diagnosis with dementia. The data was analysed using descriptive statistics to determine probabilities and distributions of the patient population, which informed the modelling process. Where real-world data was unavailable, for example for complex outcomes such as medical decision making, we employed simplified assumptions, taken wherever possible from the literature and where that was not possible using expert opinion. These assumptions will be continually refined as more comprehensive data becomes available, thereby enhancing the model's accuracy and applicability over time. The CRATE search yielded 2,851 unique individuals with a diagnosis of dementia in the time period indicated. 2,480 of those had both RCFS and carer status recorded. 1,508 (61%) were female. The distribution of RCFS was as follows: 1 (0%), 2 (2.5%),3 (6.5%), 4, (11.5%), 5 (24.5%), 6 (32%), 7 (21.5%), 8 (1.5%), and 9 (0%). Hence, 55% of patients have an RCFS score of 6 or higher, suggesting most people were at least moderately frail. 1842 (74%) patients were documented as having a carer, the remainder were documented as having no carer. The CPFT triaging services sees in average 12 patients per day and is operating from Monday to Friday. Eight percent of the patients seen by the triaging service are categorised as high-risk. The conversion rate from low-risk to high-risk while patients are on the waiting list was c1% per month. The memory service staff typically consists of three consultants, three junior doctors, and three nurses. Patients are seen 3 days per week (Monday to Wednesday) and 8 hours per day. MAS appointments are scheduled within a six-week period. Two percent (2%) of MAS appointments are not attended (Did Not Attend, DNA) by dyads. An average staff absence rate of 5.2% was used (NHS England, 2024). 75% of MAS diagnoses are conclusive in the first appointment. All diagnoses were conclusive in the second appointment. About 1500 individuals with DiMLTC are currently on the waiting list. All of the above parameters were included and implemented in the model. Agent-based probabilistic modelling and simulation Agent-based probabilistic models (ABPM) are a computational technique designed to simulate the actions and interactions of individual entities, known as agents, within a system. (Bonabeau 2002 ; Silverman et al., 2015 ; Railsback and Grimm, 2019) These agents can represent various entities, such as patients, carers, healthcare providers, diseases, or any other relevant actor that interact with each other and their environment. Each agent follows a set of rules and behaviours, and their interactions can lead to complex, emergent patterns at the system level. In healthcare, such models are particularly useful for simulating disease spread, patient flow, and the progression of chronic conditions. (Perez et al., 2009; Liu et al., 2017 ; Li et al., 2016 ) These models incorporate elements of randomness and uncertainty to reflect the real-world variability and unpredictability in these interactions. Even when comprehensive data is not available, ABPMs can be valuable by providing insights based on hypothetical scenarios and assumptions. For example, an ABPM can simulate the spread of a new infectious disease in a community, helping to predict potential outbreak patterns and evaluate the effectiveness of different intervention strategies. By incorporating these dynamics, ABPMs provide a more realistic and nuanced understanding of complex systems at a population level, helping researchers and policymakers make informed decisions based on a range of possible scenarios. The state graph in Fig. 1 served as the basis for our ABPM. DiMLTC patients, carer, triaging service, MAS, dementia disease and MLTC are agents in the model. As time progresses in simulations, DiMLTC patients transition between states and interact with other agents when needed or appropriate. As in real life, patients are referred by GPs at different times during the simulation rather than all at once. The daily triage capacity is a critical factor in our model, as it determines the number of new patients requiring MAS and entering the diagnostic pathway. Therefore, the time to diagnosis for each patient is measured from the day before triage. If patients have a caregiver, they will be supported at every stage of the process. The agents are influenced by various factors that affect their behaviour. Refer to Table 1 for a detailed list and description of the implemented parameters and how they impact on the behaviour of agents. Only a subset of factors identified in the previous section was implemented in the current study. Table 1 List of parameters implemented and their effect in the model. Parameter Description DiMLTC patient population Rockwood Clinical Frailty Scale (RCFS) (proxy for number and type of MLTC ) If a patient has a RCFS of 6 or higher, the probability of hospitalisation due to an adverse event from MLTC is 4% per month; if the RCFS is less than 6, the probability is 2% per month. The delay to a MAS appointment is randomly drawn from a normal distribution with a mean of 12 weeks and three time standard deviation of 2 weeks. High-Risk Rate Fraction of patients in the population that are at high-risk. If a patient is classified as high-risk, a MAS appointment is scheduled at the earliest possible date. Low-risk patients are placed on the waiting list. Carer Co-residence with patient If the carer lives in the same household as the patient, then the probability of the carer being available to support the patient when needed is higher than if they live in different households. Relationship to patient If the carer is a family member then we assume the availability to support the patient when needed is higher. Spouses have the highest probability, followed by daughter and son. Has personal transport available If the carer has their own transport, we assume the probability of being able to make an appointment is higher than when the carer has to use public transport. This likelihood may vary depending on the area of residence. Employment status If a carer has full-time or part-time employment, then the ability to support the patient is limited. We assume not being employed has the minimum impact. Triaging service Daily patient triage capacity A specific number of patients can be triaged each day. Patients who are not seen wait to be seen in the chronological order in which they were referred for examination. Memory assessment service (MAS) Service hours Hours per day during which the MAS is available. The number of patients seen daily depends on staff availability and diagnostic workload. Advance scheduling Number of working days for which appointments are scheduled and the length of the MAS advance scheduling list. Patients on the waiting list will be moved to this list when an appointment is scheduled. Daily probability of staff absence If a staff member is absent, the number of patients seen that day will be adjusted. MAS appointments will be rescheduled at the earliest possible date. Workforce and Workload Staff roles include consultants, junior doctors, and nurses. Workload varies by training and experience, including patient consultation time, administrative tasks, and consultation time with a consultant for junior doctors and nurses. The maximum number of patients seen daily depends on the staff's time allocation to these tasks. The following assumptions were made: Dementia progression : Due to the absence of definitive models for dementia progression and its interactions with MLTC, we assumed a linear progression for dementia. This assumption was made due to the unavailability of consistent data (e.g. diagnostic variables such as biomarkers) and the expectation that dementia progression has a limited impact at the early diagnosis stage that we are simulating. However, it enabled us to incorporate transitions from independent to dependent living into our model. We used a four-level dementia progression model, assuming the following initial distribution of dementia levels in the population: 0 (no cognitive decline, 15%), 1 (mild, 55%), 2 (moderate, 25%), and 3 (severe, 5%). Values were increased daily to simulate dementia progression. The daily increase for each patient was independently drawn at random from a uniform distribution ranging from 0.001 to 0.005. If a threshold of 2.8 was reached, this triggered a relocation to a care facility. MLTC : The RCFS was used as a proxy for MLTC. Patients with an RCFS less than 6 had a 2% monthly chance of hospitalization, while those with an RCFS of 6 or greater had a 4% monthly chance. Caregivers : Family caregivers might have full-time jobs, get sick, miss public transport, or are not aware of an appointment, which can for example contribute to the issue of a patient missing a MAS appointment (Did Not Attend, DNA). A probability distribution defined by carer factors (Table 1 ) was used to describe the DNA probability of individual dyads. The distribution was designed to achieve an average DNA of 2%, in accordance with CRATE search. For individual patients a fixed DNA probability of 4% was used. By systematically altering parameters (i.e. creating different scenarios) and simulating how a population of individual patients and dyads moves through the system, we can gain insights on the impact these parameters have on system dynamics. By generating populations based on specific demographic distributions from different areas of the UK, we can observe how demographics, geography, and other observed properties influence system dynamics. The properties of the DiMLTC patient population and the transition probabilities between states were calculated using CRATE data or service data provided by CPFT. Supplementary Table S1 presents the pseudocode for the implemented model and simulation algorithm, detailing the steps and logic used to simulate the system. Research questions and Simulation experiments. We used the model to explore the following questions: On a population level, how does time from referral to diagnosis (i.e. the service performance) change if the number of high-risk patients or the percentage of patients with MLTC increases? How does the performance change if individual patients and dyads did not attend appointments and these need to be rescheduled? On a service level, how does performance change based on staffing levels, staff absences, and task completion times? How does the performance change as a function of how many patients are on appointment and waiting lists? The simulation stopped when all patients were diagnosed, or a maximum simulation period of 156 weeks (3 years) was reached. Each simulation step corresponds to one day, allowing for detailed daily analysis. ABPMs include stochastic elements, meaning that randomness can influence the outcomes. Running multiple simulations helps capture the variability in results, ensuring that the findings are more robust and not due to random chance, and applicable to real-world scenarios. We repeated 2,000 independent simulations and observed the distribution of the time to diagnosis to obtain a range of possible outcomes. For each repetition the same initial population was used. Also, for simulations with the same population parameters always the same population was used to facilitate comparability of results. The model was implemented in the Python 3 programming environment and calculations performed on the University of Essex’s High Performance Computing facility. Supplementary Table S2 summarises the parameter combinations used to initialise the patient-carer dyad population. Supplementary Table S3 summarises the model parameters for the simulation. Results Table 2 summarises the explored scenarios and calculated simulation results using our agent-based probabilistic approach. One accepted quality measure of waiting time is the percentage of patients diagnosed within 18 weeks (described as ‘Good Results’ here). The reported mean, median, 90th percentile (P90) of the calculated times to diagnosis, and the Good Results were derived from data pooled across all simulation runs. The upper part of Table 2 summarises scenarios with empty appointment and waiting lists (condition EMPTY). With the standard parameter setting characterising CPFT service capacity and a representative population (first line highlighted in Table 2 ), the good results are 99.77%. The mean, median, and P90 of the time to diagnosis are reported for the entire population, as well as separately for high-risk and low-risk patients and for patients that converted from low-risk to high while on the waiting list. On a whole population level, 50% of patients/dyads are seen within 18 days and 90% within 33 days. Figure 2 (a) shows the histogram of the estimated times to diagnosis. The histogram shows a bimodal distribution, with a large peak around 20 and another peak around 105 days. The second peak is attributed to medical episodes related to MLTC. About 18% of patients have been hospitalised at least once, with a median stay of 86 days outside the diagnostic process. The maximum time a patient spent outside the process was 470 days. For EMPTY, independently of the parameter settings the MAS service always manages to achieve good results above 95%. This good performance also means that patients on the waiting lists are very unlikely to convert from low-risk to high-risk while waiting for an appointment. Table 2 Simulation results. Population parameters cover the population size, the percentage of high-risk (HR) patients, the percentage of patients with RCFS larger or equal than 6 (proxy for MLTC), and the Did Not Attend (DNA) rate for dyads. Service parameters include the number of Consultants (C), Junior Doctors (JD), and Nurses (N), staff absence rate (Absn), reduction in administrative time (Admin time), and the status of the appointment list (Appt. List: empty or full) and the number of patients on the waiting list (Wait. List). The column MXP reports the maximum number of patients that can be seen in the MAS as function f(C, JD, N, Admin). The table shows the mean (M), median (Mdn), and 90th percentile (P90) of time to diagnosis for the entire population, as well as for high-risk, low-risk, and patients transitioning between risk categories. For the latter also the percentage of patients converted is reported (PC). The last column reports the percentage of patients diagnosed within 18 weeks (Good Results). Other model parameters are: Probability of hospitalisation due to MLTC is 4% per month if RCFS is larger or equal to 6 and 2% otherwise, Probability of individual patients DNA (4%), Triaging capacity (from Mon-Fri, 12 people per day), Memory assessment service (MAS) hours (Mon-Wed, 8 hours per day), MAS advance scheduling (6 weeks), Time allocation for consultants (1 hour to see the patient, 1 hour for admin), junior doctors and nurses (1.5 hours to see patient, 1.5 hours for admin), diagnosis success rate (75%) with a maximum of 2 appointments required to make a successful diagnosis. Population Services All High-risk Low-risk Low-risk to high-risk converter Size HR RCFS DNA C JD N Absn Admin time Appt. List Wait. List MXP M Mdn P90 M Mdn P90 M Mdn P90 PC M Mdn P90 Good Results 3000 8% 55% 2% 3 3 3 5.2% 0 Empty 0 22 20 18 33 17 15 30 20 18 33 0.00% 99.77% 3000 2.0% 14 13 22 12 9 21 15 13 22 0.00% 99.90% 3000 4.0% 18 15 28 15 14 27 18 15 29 0.00% 99.84% 3000 8.0% 25 22 42 22 21 37 25 22 42 0.00% 56 50 60 99.51% 3000 10.0% 30 27 49 27 23 44 30 27 49 0.00% 56 52 62 99.13% 3000 4% 21 19 34 18 16 30 21 19 35 0.00% 99.73% 3000 16% 23 21 37 21 17 36 24 21 38 0.00% 49 49 50 99.64% 3000 4 24 9 8 14 7 7 13 10 8 14 0.00% 99.95% 3000 4 24 9 8 14 7 6 13 10 8 14 0.00% 99.95% 3000 4 26 6 6 7 4 2 7 6 6 7 0.00% 99.97% 3000 70% 21 19 35 18 16 30 21 20 35 0.00% 99.70% 3000 -15 24 10 8 14 7 7 13 10 8 14 0.00% 99.95% 3000 -30 26 6 6 8 4 2 7 6 6 8 0.00% 99.97% 1500 12 9 18 9 8 15 12 10 18 0.00% 99.94% 6000 40 35 72 32 34 45 40 35 73 0.07% 64 57 84 97.98% 9000 61 44 108 44 43 48 62 48 110 0.25% 74 69 105 95.57% 3000 8% 55% 2% 3 3 3 5.2% 0 Full 1500 22 248 263 287 50 44 48 269 265 295 2.81% 161 161 247 8.62% 3000 2.0% 233 245 268 48 43 44 252 246 280 2.60% 152 152 232 8.64% 3000 4.0% 242 256 280 49 44 48 262 258 289 2.72% 157 156 242 8.63% 3000 8.0% 261 279 300 51 45 49 283 280 309 2.96% 169 168 261 8.62% 3000 10.0% 271 289 311 52 45 50 294 292 320 3.09% 175 175 272 8.61% 3000 250 99 97 125 50 44 48 104 100 126 0.66% 81 75 113 90.69% 3000 500 130 133 156 50 44 48 138 135 160 1.10% 97 93 140 42.52% 3000 750 160 167 189 50 44 48 171 169 193 1.53% 113 111 167 8.79% 3000 1000 190 200 222 50 44 48 204 202 226 1.97% 130 128 192 8.66% 3000 1250 219 231 254 50 44 48 236 232 260 2.39% 145 145 219 8.64% 3000 4% 251 266 294 50 44 48 272 267 301 2.83% 163 162 251 8.62% 3000 16% 255 289 312 50 44 48 298 294 324 2.90% 176 175 274 16.23% 3000 4 24 216 226 245 50 44 48 233 227 265 2.33% 143 142 216 8.64% 3000 4 24 216 226 245 50 44 48 233 227 265 2.35% 143 142 216 8.66% 3000 4 26 188 196 216 49 44 48 201 197 227 1.92% 128 126 189 8.84% 3000 70% 250 265 294 50 44 48 271 266 301 2.81% 162 162 250 8.61% 3000 -15 24 217 227 246 50 44 48 234 229 265 2.34% 144 143 217 8.64% 3000 -30 26 189 197 217 50 44 48 203 199 229 1.93% 129 127 190 8.81% 3000 4 6 8 40 70 61 134 39 43 48 73 68 137 0.39% 86 78 128 85.70% 3000 4 6 8 -30 44 53 43 121 31 43 44 55 43 123 0.28% 82 76 119 92.58% The lower part of Table 2 summarises the results reflecting the situation where all memory service appointments are booked for the next six weeks and 1500 individuals are on the waiting list (condition CURRENT, current situation at the time this work was written.). With the standard staffing configuration 22 patients can be seen on service days. With 3 days service per week and 6-week advance schedule, there are 396 patients waiting for an appointment. In this scenario, with the same service capacity only 8.63% good results are achieved, with 50% of all patients diagnosed within 263 days (37 weeks) and 90% of patients within 287 days (41 weeks). Figure 2 (b) shows the related histogram. For this case, about 3% of the patients convert from low-risk to high-risk while waiting for an appointment. The histogram shows a multimodal distribution with peaks around 250, 300 and 350 days. This is again a result of adverse medical events due to MLTC. About 35% of patients were hospitalized at least once, with a median and maximum stay outside the diagnosis process of 86 days and 547 days, respectively. The service performance shows larger variability for CURRENT. An increase of the high-risk cases from 8–16% has a significant impact on the good results (increase from 8.62–16.12%), but this is caused by the systematic prioritisation of high-risk patients to be scheduled for an appointment within the next 6 weeks. The performance for high-risk patients is consistent for all experiments. This comes at the cost of low-risk patients, whose median/P90 time to diagnosis increased from 265/295 days to 294/324 days. An increase of patients with MLTC and associated assumptions for hospitalisation, have a limited impact on performance. The median/P90 increases by a maximum of 6 days. An increase of the DNA for dyads from 2–4% has minimal impact on the good results and leads to an expected increase of median/P90 times to diagnosis of 6 days. On a service level, increased staff leads to reductions of median/P90 time to diagnosis for low-risk patients from 265/295 to 227/265; for one additional consultant the reduction is more significant, from 265/295 to 197/227. performance. Increased levels of staff absences also have a more significant impact on low-risk patients: an increase from 5.2–10% leads to a median/P90 increase from 265/295 to 292/320, i.e. to an increase of about 4 weeks. The reduction of time for administration by 30 minutes had a significant impact and showed similar behaviour to adding one additional consultant. The 30-minute reduction means that on average not 22 patients but 26 patients can be seen every day. Increasing staffing by 1 consultant, 3 junior doctors and 5 nurses and reducing admin times by 30 min, increase the good results to about 93%. A full appointment list and with 500 patients on the waiting list, increases the good results from 8.62–43%. With 750 patients on the list there is no impact on the service performance (8.62–8.8%), which then remains constant around 8.6% independently of the length of the waiting list. Figure 2 (c) and (d) summarise the assessment of variability of the simulation results, by showing the distribution of the mean and median times to diagnosis and good results for the 2000 independent simulations. The variability of the calculated median performances is about 3 weeks for EMPTY and 4 weeks for CURRENT. Discussion We have presented what we believe to be the most sophisticated model of an NHS MAS produced to date. Its utility is enhanced by the use of real-world clinical data wherever possible for calibration. Though these clinics vary across locations, much of the basic structure is retained and we are now developing the model to include an easy-to-use interface where interested users can add details of their own service to derive individualised data. (Smith and Surr, 2024 ) Importantly we were able to model not only basic parameters such as staffing levels and clinic slots, but also more complex and crucial factors including interfaces with carers (as patient-carer dyads) and MLTC. As a demonstration of potential utility, we investigated the impact of altering several parameters and discovered some surprising results. Firstly, the simulation reveals that the system's emergent behaviour and performance vary based on the initial conditions, whether EMPTY or CURRENT where these terms reflect the presence or absence of waiting lists. The EMPTY scenario suggests that memory services can cope well and perform highly with current resources, even if the number of the patient population increases. Conversely, the CURRENT scenario confirms that the service is overloaded. Reducing the waiting list from 1500 to 250 would enable good service provision, with 90% of patients diagnosed within 18 weeks. In terms of service optimization, the model can be used to identify service parameters that would allow reducing the waiting list to a manageable size before the service resources can go back to normal, knowing that good performance can be achieved for the majority of patients in a sustainable way. This is important as it suggests that an effort to clear current waiting lists would bring sustainable benefits to the system rather than temporary respite. Such an effort to clear waiting lists is possible and may be a worthwhile use of resources. For CURRENT, doubling staff absences to 10% extended median/P90 waiting times by up to 26/24 days (16/13 days assuming 8% absences). Additionally, higher levels of DNAs added up to 3/7 days to the waiting times. When comparing median/P30 times between 4% absences (256/280) and 4% DNA (266/294), the results suggest that the impact of DNA is more significant. Therefore, while ensuring staff health is essential, efforts to decrease non-attendance, such as sending reminder text messages, distinct letters to patients and carers if they do not co-reside, or developing AI models to predict attendance and dynamically double-book appointments, may be a valuable allocation of resources. Triage is a standard clinical response to increased demand, but in our model we identify potential unintended consequences of triaging greater numbers of people to be seen quickly. If 16% of people are classified as high-risk and triaged as priority, those not prioritised face waits so long some will never be seen as they will not live long enough to reach the front of the queue. Similarly, increasing staffing levels is a way to cope with increased demand. In our model, increasing consultants had the biggest impact on the system and the number of staff required to meet the target of seeing people within 18 weeks of referral was achievable. Though consultant staff are expensive their disproportionate impact may well offset that cost by improving system efficiency. Many of the potential interventions to cut waiting time, including increasing staffing, require increased funding and the ability to find staff. Perhaps the most dramatic finding of our modelling was the impact of cutting administration time. Administrative tasks include reviewing patient history, documenting findings, coordinating with other healthcare professionals, scheduling follow-up appointments and, perhaps most profoundly, data entry. Cutting this by half had a similar impact on waiting times to increasing the number of consultants from 3 to 4. The addition (or removal) of administration is a choice for health services to make and does not necessarily require change in resource, particularly if technological tools such as ambient dictation or efficient electronic patient records are used. Our model can also be used to illustrate the impact of increased administration. Fifteen minutes of reduced administration means a decrease in the overall median/P90 times from 263/287 to 227/246; additional 15 minutes reduce the times to 189/197. Of course potential improvements depend on the combination of service parameters. However, such calculations allow managers to make decisions to add or remove admin tasks for clinicians based on the impact on clinical services. Our study has some limitations. By their nature, all models are “wrong”, not least because they unavoidably simplify complex systems. (Saltelli et al., 2020 ) There will be parameters which we have not included and others where we have made inaccurate assumptions. Despite our best efforts to use data from real clinical services we have not been able to do that in every case and it is possible the county where we have derived the data (Cambridgeshire) may be in some ways unrepresentative of other areas which could limit the applicability of our model. Nevertheless, we believe our model represents the most sophisticated attempt to date to describe NHS MAS. It incorporates crucial parameters such as MLTC and the impact of dyad relationships. This model should provide a useful template for optimising current clinical services for the benefit of both those who use them and those who pay for them. As demand grows these approaches will be vital to maximise resource and efficiency of services. Optimization typically requires a fitness function, which evaluates and compares potential solutions to identify the best one based on predefined criteria. In future work, these criteria must be defined with stakeholders to ensure that not only financial aspects are considered, but also patient and carer experiences and the overall quality of the service. The model developed is flexible and can be adjusted and expanded to fit into the broader ecosystem of NHS services. Although not all factors have been implemented in the current model, and despite many simplifications, the outcomes of different scenarios are plausible and provide insights into how patient-caregiver dynamics and MLTC impact on time to diagnosis. Future work will extend the model, and collaboration and co-design with stakeholders will be key to ensuring that the findings have a meaningful impact on patients, dyads and healthcare delivery. (Wong-Lin et al., 2020 ) There is a consensus that reducing waiting times is important, but the findings also support the view that system-wide changes are needed to make a difference. Otherwise, patients will be diagnosed quickly, only to queue up elsewhere in the system when new bottlenecks arise. Declarations Acknowledgments This study was funded by the National Institute for Health and Care Research (NIHR) Engineering and Physical Sciences Research Council (EPSRC) under its Systems Engineering Innovation hubs for Multiple long-term Conditions (SEISMIC) Programme (NIHR158147). This study is supported by the Applied Research Collaboration East of England (NIHR ARC EoE) at Cambridgeshire and Peterborough NHS Foundation Trust. All research at the Department of Psychiatry in the University of Cambridge is supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014) and NIHR Applied Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. BRU is supported by a generous donation from Gnodde Goldman Sachs Giving. References Alzheimer Society (2024) The economic impact of dementia - Module 1: Annual costs of dementia, ( https://www.alzheimers.org.uk/sites/default/files/2024-05/the-annual-costs-of-dementia.pdf) Aujla N, Tooman T, Arakelyan S, Kerby T, Hartley L, O’Donnell A, Guthrie B, Underwood I, Jacko JA, Anand A (2024) New horizons in systems engineering and thinking to improve health and social care for older people. Age Ageing 53(10):afae238 Bonabeau E (2002) Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences. ;99(suppl_3):7280-7 Handley M, Windle M, Mathie E et al Living with dementia and other long-term conditions: what works for patient/caregiver dyads? A realist review, 23 January 2025, PREPRINT (Version 1) available at Research Square [ https://doi.org/10.21203/rs.3.rs-5874431/v1] Li Y, Lawley MA, Siscovick DS, Zhang D, Pagán JA (2016) Peer reviewed: agent-based modeling of chronic diseases: a narrative review and future research directions. Prev Chronic Dis. ;13 Liu Z, Rexachs D, Epelde F, Luque E (2017) An agent-based model for quantitatively analyzing and predicting the complex behavior of emergency departments. J Comput Sci 21:11–23 NHS England, Sickness Absence NHS, Rates A (2024) NHS England, 9 January 2025 ( https://digital.nhs.uk/data-and-information/publications/statistical/nhs-sickness-absence-rates/august-2024 [cited 22 Jan 2025]) Perez L, Dragicevic S (2009) An agent-based approach for modeling dynamics of contagious disease spread. Int J Health Geogr 8:1–7 Railsback SF, Grimm V (2019 Mar) Agent-based and individual-based modeling: a practical introduction. Princeton University Press, p 26 Ramos AL, Ferreira JV, Barceló J (2011) Model-based systems engineering: An emerging approach for modern systems. IEEE Trans Syst Man Cybernetics Part C (Applications Reviews) 42(1):101–111 Rees J, Burton A, Walters K, Cooper C (2023) Exploring the provision and support of care for long-term conditions in dementia: A qualitative study combining interviews and document analysis. Dementia 22(4):820–837 Royal Academy of Engineering (2017) Engineering better care: A systems approach to health and care design and continuous improvement, ( https://raeng.org.uk/media/wwko2fs4/final-report-engineering-better-care-version-for-website.pdf) Saltelli A, Bammer G, Bruno I, Charters E, Di Fiore M, Didier E, Espeland WN, Kay J, Piano SL, Mayo D, Pielke R Jr (2020) Five ways to ensure that models serve society: a manifesto. Nature. ;582 Silverman BG, Hanrahan N, Bharathy G, Gordon K, Johnson D (2015) A systems approach to healthcare: agent-based modeling, community mental health, and population well-being. Artif Intell Med 63(2):61–71 Smith SJ, Surr C (2024) Exploring challenges and innovation in memory assessment services in England and Wales–a national survey and case study approach. BMC Health Serv Res 24(1):1–6 Whitty C (2023) Chief Medical Officer’s annual report 2023: health in an ageing society - GOV.UK 2023 ( https://www.gov.uk/government/publications/chief-medical-officers-annual-report-2023-health-in-an-ageing-society Wong-Lin K, McClean PL, McCombe N, Kaur D, Sanchez-Bornot JM, Gillespie P, Todd S, Finn DP, Joshi A, Kane J, McGuinness B (2020) Shaping a data-driven era in dementia care pathway through computational neurology approaches. BMC Med 18:1–0 Yew PY, Devera R, Liang Y, Khalifa RA, Sun J, Chi NC, Chou YC, Tonellato PJ, Chi CL (2024) Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions. Alzheimer's & Dementia. Jun 11 Additional Declarations The authors declare no competing interests. Supplementary Files MDLABPMSupTab1.docx Supplementary Table S1 MDLABPMSupTab2.docx Supplementary Table S2 MDLABPMSupTab3.docx Supplementary Table S3 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5898259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406859106,"identity":"2f0a8cde-da59-4ef2-995f-ab9a5c71b040","order_by":0,"name":"Reinhold Scherer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJACZjDJ3oAQYWzAqhBdC88BkrVIJBCpxbyB/eHngop7cuYzHx/++KPiMAN/+wE2yRl4tMgc4DGWnnGm2FjmdlqaNM+ZNAaJMwlskhvwaJFg4GFj5m1LSJwhnWPGzNhmw8Bwg4FN8gFeLezPmHn/JdTPkDz/+ePPfxIM8oS1MJgx8zYkJEhI8DBI8DbYMBiAtOB1GDPQLzzHEgxn8KSZARlpPIZnEpst8Xlfgr394WeemgR5CfbDjz/+qDksJ3f88MGbPXi0QCMFAXiIiMhRMApGwSgYBYQAADDmQFc26YsyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3407-9709","institution":"University of Essex","correspondingAuthor":true,"prefix":"","firstName":"Reinhold","middleName":"","lastName":"Scherer","suffix":""},{"id":406859107,"identity":"245809d5-ce69-4411-b2c6-6f068e659a27","order_by":1,"name":"Mohammadreza Jamalifard","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mohammadreza","middleName":"","lastName":"Jamalifard","suffix":""},{"id":406859108,"identity":"fb5cb492-e784-47b5-aa3f-2f5d5bfd0aaf","order_by":2,"name":"Benjamin R. Underwood","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"R.","lastName":"Underwood","suffix":""},{"id":406859109,"identity":"bef5d022-76e1-46ff-ab33-8652aa75bccd","order_by":3,"name":"Jonathan R. Lewis","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"R.","lastName":"Lewis","suffix":""},{"id":406859110,"identity":"0c27ff49-9edd-420f-a695-3d38996f4b46","order_by":4,"name":"Alison Skea","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"","lastName":"Skea","suffix":""},{"id":406859111,"identity":"4c5f64f4-884f-44fa-8044-559a64789aa8","order_by":5,"name":"Elspeth Mathie","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Elspeth","middleName":"","lastName":"Mathie","suffix":""},{"id":406859112,"identity":"35f0c086-90e7-44f7-ae1a-9ab0f05a35a4","order_by":6,"name":"Karen Harrison Dening","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"Harrison","lastName":"Dening","suffix":""},{"id":406859113,"identity":"f04336c8-fd3b-4265-9f86-c9adb1f11c2d","order_by":7,"name":"Steve Milton","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Steve","middleName":"","lastName":"Milton","suffix":""},{"id":406859114,"identity":"0d89c682-a138-4c35-a613-a55a3c07423f","order_by":8,"name":"Claire Surr","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Surr","suffix":""},{"id":406859115,"identity":"4ed366cd-f112-49b8-8878-80d625beb2c7","order_by":9,"name":"Reda M. Lebcir","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Reda","middleName":"M.","lastName":"Lebcir","suffix":""},{"id":406859116,"identity":"6a06821a-da4e-432c-9543-bf80c108debf","order_by":10,"name":"Amit N. Pujari","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amit","middleName":"N.","lastName":"Pujari","suffix":""},{"id":406859117,"identity":"966b5d30-d3b2-4c9a-ab1d-3d9eb5d0df02","order_by":11,"name":"Robert C. Free","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"C.","lastName":"Free","suffix":""},{"id":406859118,"identity":"0af74a25-15b1-4f29-8ba6-9e179a8c309d","order_by":12,"name":"Theng Yin Leng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Theng","middleName":"Yin","lastName":"Leng","suffix":""},{"id":406859119,"identity":"d986215f-1850-46da-a257-40565d8bd70a","order_by":13,"name":"Michael Craven","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Craven","suffix":""},{"id":406859120,"identity":"b27542f7-79c9-48f9-aa4e-7aabf73027e3","order_by":14,"name":"Lucy Beishon","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"","lastName":"Beishon","suffix":""},{"id":406859121,"identity":"a0573523-2a1c-4838-9d30-f01a0f392741","order_by":15,"name":"Victoria L. Keevil","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"L.","lastName":"Keevil","suffix":""},{"id":406859122,"identity":"d0118ac0-7464-4cfc-838a-c70892b851d5","order_by":16,"name":"Claire Goodman","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Goodman","suffix":""},{"id":406859123,"identity":"15ddd752-1d99-451c-969f-086c17b60782","order_by":17,"name":"Melanie Handley","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Handley","suffix":""}],"badges":[],"createdAt":"2025-01-24 21:09:49","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5898259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5898259/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74914959,"identity":"c58327b8-a690-4fd1-9d9a-203e46d300a6","added_by":"auto","created_at":"2025-01-28 09:43:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":687761,"visible":true,"origin":"","legend":"\u003cp\u003eState graph for individual patients with dementia, including multiple long-term conditions (MLTC), and patient-carer dyad pathways from GP Referral to Memory Assessment Service (MAS) diagnosis. There are two starting points: patients may live independently (alone, with family, or assisted living) or in care institutions. At any time, patients can move outside of the MAS cycle due to an adverse medical event from MLTC (e.g. hospitalisation). Different coloured arrows indicate various cycles in the graph.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5898259/v1/1edd9fa639572e2f8e7ce326.png"},{"id":74913416,"identity":"2211a12a-aa2d-45bc-9c9b-366f35b7b70d","added_by":"auto","created_at":"2025-01-28 09:27:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1400319,"visible":true,"origin":"","legend":"\u003cp\u003eSimulation results. (a) Histogram of the time to diagnosis for the standard scenario with empty appointment and waiting lists (EMPTY). The upper histogram shows the whole range, while the lower histogram shows a zoomed in version for enriched details. The distribution is shown for high-risk, low-risk and patients who convert while waiting for an appointment. (b) Histogram of the time to diagnosis for the scenario with full appointment list (6 weeks, 396 patients/dyads) and 1500 patients/dyads on the waiting list (CURRENT). (c) Distribution of the mean, median and good results from the 2000 repeated simulations for EMPTY and (d) for CURRENT.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5898259/v1/346c1a4c33680a4196a42278.png"},{"id":74915082,"identity":"4d38bda1-3c7a-4f84-a74e-73e702bf858c","added_by":"auto","created_at":"2025-01-28 09:51:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2744156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5898259/v1/6a9cd60f-d198-43e4-8a65-68554ad37d88.pdf"},{"id":74913409,"identity":"47fbf17f-c8ce-41cf-b44e-a1f964b7d016","added_by":"auto","created_at":"2025-01-28 09:27:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18647,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S1\u003c/p\u003e","description":"","filename":"MDLABPMSupTab1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5898259/v1/96ded9c0bd170e15d40981b2.docx"},{"id":74913716,"identity":"10dafef9-795c-423a-956a-17426ee0a09a","added_by":"auto","created_at":"2025-01-28 09:35:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16628,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S2\u003c/p\u003e","description":"","filename":"MDLABPMSupTab2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5898259/v1/3d2c5631b333012c63dad8fd.docx"},{"id":74913718,"identity":"c878b2b5-6529-4830-b84a-9a8f0d7fff92","added_by":"auto","created_at":"2025-01-28 09:35:51","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20801,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S3\u003c/p\u003e","description":"","filename":"MDLABPMSupTab3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5898259/v1/e33f63fd115d78a492f9749e.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eUnderstanding dynamics to Streamline Access: Systems Engineering and Agent-Based Probabilistic Modelling in Memory Assessment Services\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDementia is a leading cause of mortality and morbidity worldwide. As the global population ages, the prevalence of dementia is projected to increase significantly. For example, the number of individuals in the UK with dementia is expected to rise from approximately 1\u0026nbsp;million in 2024 to 1.4\u0026nbsp;million by 2040 (Alzheimer Society, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This growing prevalence underscores the urgent need for highly efficient services designed around persons with dementia and other long-term conditions, as well as their family carers (Whitty, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Access to National Health Service (NHS) services is a key priority for both this group and the NHS. One way to optimise and gain deeper insight into the dynamics of services is by building models and simulations that allow in silico alterations to predict their impact on service efficiency. Some of these factors (for example number of staff) are relatively simply modelled. However, crucially, this does not reflect the complexity of either the services or of the people accessing them. For example, two critical issues for people with dementia are the presence of a carer (i.e. a family member or friend who helps them navigate the services and manage the condition) (to form a \u0026lsquo;patient-carer dyad\u0026rsquo;) and the common occurrence of dementia as part of a more complex burden including multiple long-term conditions (MLTC). Managing MLTC makes access to healthcare services more difficult. (Rees et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yew et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) This is due to a number of factors, including increased healthcare needs, complex care coordination, transportation and mobility issues, psychosocial factors, and a higher risk of hospitalisation. This paper uses systems engineering, quantitative modelling, and computer simulation approaches based on real-world NHS data to model patients with dementia, including those with multiple long-term conditions (DiMLTC), from general practitioner (GP) referral to dementia diagnosis. (Ramos et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Royal Academy of Engineering, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Aujla et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) We included both straightforward and more complex factors, including the impact of multimorbidity and patient carer dyads, to build a model that will have clinical utility in enhancing efficiency for memory assessment services.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMapping and validating patient-carer dyad pathways\u003c/h2\u003e \u003cp\u003eThe pathways for DiMLTC patients, from GP referral to diagnosis at the Memory Assessment Service (MAS), have been mapped in collaboration with clinical professionals from Cambridgeshire and Peterborough NHS Foundation Trust (CPFT) and summarised in a directed state graph (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this graph, the nodes represent different states in the process, while the directed edges illustrate the transitions between them. When referred by a GP, patients may live alone, with family, in assisted living or in a care home. The triaging service, conducted remotely by a team of clinicians, typically occurs the next working day following receipt of referral. During triaging, referrals are divided into two categories based on clinical risk: High-risk cases are scheduled for a MAS appointment at the earliest possible date (typically within 6 weeks), while low-risk cases are placed on a waiting list. Individuals, some of whom are part of dyads (i.e. have a carer), on the waiting list remain there until the MAS contacts them with an appointment. A diagnosis is usually made on the day of the MAS appointment. If further information is needed (for example the results from imaging), a new MAS appointment is arranged at the earliest possible time; otherwise, a diagnosis is made. At any point, a medical episode may occur requiring patients to be hospitalized, necessitating them to be discharged from hospital, recover and a new appointment scheduled before the diagnostic process can continue. This event is more likely to happen in those suffering MLTC. Additionally, patients on the waiting list may convert from low-risk to high-risk and need to be prioritized, and an MAS appointment will be scheduled. In some cases, patients may need to relocate to a care facility prior to assessment if their condition progresses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo ensure these pathways accurately reflect real-world scenarios, they were discussed with relevant stakeholders, including people living with dementia, carers, and service providers. Ethical permission for interviews and co-production workshops was obtained (London - Bromley Research Ethics Committee, reference 23/LO/0829). Interview and co-production workshop participants provided written informed consent. People living with DiMLTC and carers retold their experiences and frustrations regarding long waiting times for diagnosis. This discussion, combined with the outcomes of a realist review (Handley et al., 2025), provided valuable insights into the interactions between these groups and the healthcare system. It also led to the identification of key factors expected to significantly impact the diagnosis process and overall care. Identified key factors are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eDiMLTC patient\u003c/em\u003e: Age, Sex, Number and type of MLTC, Known to services (i.e. medical history, treatment records, and other relevant information are already on file), Place of residence, Postcode, Has personal transportation available, Ethnicity, Language.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eCaregiver\u003c/em\u003e: Relationship to patient (e.g., spouse, daughter, son, friend, etc.), Co-residence with patient, Age, Number and type of MLTC, Postcode, Has personal transportation available, and Employment status (e.g. full-time, part-time, unemployed, etc.).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eServices\u003c/em\u003e: Location, Workforce management (e.g., service hours, number of consultants, junior doctors, and nurses, their working hours and workload distribution), Communication channels (e.g., letters, phone, email, etc.), and Cultural sensitivity (i.e., recognize, respect, and effectively respond to the diverse cultural backgrounds, beliefs, and practices of patients/dyads).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eService level and patient population data\u003c/h3\u003e\n\u003cp\u003eIntegrating empirical data into models significantly enhances their precision and relevance, providing a robust foundation for accurate simulations and informed decision-making. This comprehensive approach ensures that the model not only reflects theoretical pathways but also aligns closely with the practical experiences of the various services and professional disciplines involved in the dementia care process.\u003c/p\u003e \u003cp\u003eTo gain a realistic insight into the patient population characteristics and determine transition probabilities between states within the pathways (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), data from CPFT was used. CPFT provides mental and community health services for a population of approximately 1\u0026nbsp;million people including more than 165,000 people over the age of 65. The MAS service receives about 3,000 referrals for memory assessment per year. We used service-level data, including the number of referrals and staff, the average time for appointments and administration, and the percentage of patients triaged as high-risk as part of a trust approved service evaluation.\u003c/p\u003e \u003cp\u003eTo better understand the patient population, we conducted a search using the Clinical Records and Text Extraction (CRATE) database. This resource is a pseudonymised copy of the electronic patient record. It has overarching NHS ethical approvals (12/EE/0407, 17/EE/0442, 22/EE/0264). The CPFT Research Database Oversight Committee further approved this study. The search strategy included the following criteria and Boolean operators: \u003cem\u003e(\u0026ldquo;Patients over the age of 65 with a diagnosis of dementia (ICD10 code starting F00, F01, F02, F03)\u0026rdquo; AND \u0026ldquo;Recorded Rockwood Clinical Frailty Score\u0026rdquo; AND \u0026ldquo;Recorded Yes or No to carer\u003c/em\u003e\u0026rdquo;). We applied filters to include data from the past 3 years. The Rockwood Clinical Frailty Score (RCFS) assesses frailty in older adults, ranging from 1 (very fit) to 9 (terminally ill). In the current study, we used the RCFS as a proxy for MLTC. While the RCFS is not a direct proxy for MLTC, it does incorporate these conditions. In our simulation, patients with higher RCFS scores had higher probabilities of suffering adverse medical events due to MLTC, requiring recovery before a diagnosis could be made.\u003c/p\u003e \u003cp\u003eData extracted from the CRATE database included: Basic demographic data including age, sex, diagnosis (and date of dementia diagnosis), ethnicity, marital status, Health of the Nation Outcome Scale (HoNOS) scores and cognitive scores including the Addenbrooke\u0026rsquo;s Cognitive Examination (ACE), Number of lifetime prescriptions, Deprivation index, Rockwood Clinical Frailty score (RCFS), Presence of carer yes or no, Admission to hospital \u0026ndash; psychiatric, Admission to hospital \u0026ndash; general, Number of calls to first response service (FRS), an emergency service for mental health (111 option 2), Care from psychiatric crisis team, Death, and CPFT physical healthcare service use. All variables were as recorded at the time of diagnosis with dementia. The data was analysed using descriptive statistics to determine probabilities and distributions of the patient population, which informed the modelling process. Where real-world data was unavailable, for example for complex outcomes such as medical decision making, we employed simplified assumptions, taken wherever possible from the literature and where that was not possible using expert opinion. These assumptions will be continually refined as more comprehensive data becomes available, thereby enhancing the model's accuracy and applicability over time.\u003c/p\u003e \u003cp\u003eThe CRATE search yielded 2,851 unique individuals with a diagnosis of dementia in the time period indicated. 2,480 of those had both RCFS and carer status recorded. 1,508 (61%) were female. The distribution of RCFS was as follows: 1 (0%), 2 (2.5%),3 (6.5%), 4, (11.5%), 5 (24.5%), 6 (32%), 7 (21.5%), 8 (1.5%), and 9 (0%). Hence, 55% of patients have an RCFS score of 6 or higher, suggesting most people were at least moderately frail. 1842 (74%) patients were documented as having a carer, the remainder were documented as having no carer. The CPFT triaging services sees in average 12 patients per day and is operating from Monday to Friday. Eight percent of the patients seen by the triaging service are categorised as high-risk. The conversion rate from low-risk to high-risk while patients are on the waiting list was c1% per month. The memory service staff typically consists of three consultants, three junior doctors, and three nurses. Patients are seen 3 days per week (Monday to Wednesday) and 8 hours per day. MAS appointments are scheduled within a six-week period. Two percent (2%) of MAS appointments are not attended (Did Not Attend, DNA) by dyads. An average staff absence rate of 5.2% was used (NHS England, 2024). 75% of MAS diagnoses are conclusive in the first appointment. All diagnoses were conclusive in the second appointment. About 1500 individuals with DiMLTC are currently on the waiting list. All of the above parameters were included and implemented in the model.\u003c/p\u003e\n\u003ch3\u003eAgent-based probabilistic modelling and simulation\u003c/h3\u003e\n\u003cp\u003eAgent-based probabilistic models (ABPM) are a computational technique designed to simulate the actions and interactions of individual entities, known as agents, within a system. (Bonabeau \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Silverman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Railsback and Grimm, 2019) These agents can represent various entities, such as patients, carers, healthcare providers, diseases, or any other relevant actor that interact with each other and their environment. Each agent follows a set of rules and behaviours, and their interactions can lead to complex, emergent patterns at the system level. In healthcare, such models are particularly useful for simulating disease spread, patient flow, and the progression of chronic conditions. (Perez et al., 2009; Liu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) These models incorporate elements of randomness and uncertainty to reflect the real-world variability and unpredictability in these interactions. Even when comprehensive data is not available, ABPMs can be valuable by providing insights based on hypothetical scenarios and assumptions. For example, an ABPM can simulate the spread of a new infectious disease in a community, helping to predict potential outbreak patterns and evaluate the effectiveness of different intervention strategies. By incorporating these dynamics, ABPMs provide a more realistic and nuanced understanding of complex systems at a population level, helping researchers and policymakers make informed decisions based on a range of possible scenarios.\u003c/p\u003e \u003cp\u003eThe state graph in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e served as the basis for our ABPM. DiMLTC patients, carer, triaging service, MAS, dementia disease and MLTC are agents in the model. As time progresses in simulations, DiMLTC patients transition between states and interact with other agents when needed or appropriate. As in real life, patients are referred by GPs at different times during the simulation rather than all at once. The daily triage capacity is a critical factor in our model, as it determines the number of new patients requiring MAS and entering the diagnostic pathway. Therefore, the time to diagnosis for each patient is measured from the day before triage. If patients have a caregiver, they will be supported at every stage of the process. The agents are influenced by various factors that affect their behaviour. Refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for a detailed list and description of the implemented parameters and how they impact on the behaviour of agents. Only a subset of factors identified in the previous section was implemented in the current study.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of parameters implemented and their effect in the model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParameter\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDescription\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDiMLTC patient population\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRockwood Clinical Frailty Scale (RCFS) (proxy for \u003cem\u003enumber and type of MLTC\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf a patient has a RCFS \u0026nbsp;of 6 or higher, the probability of hospitalisation due to an adverse event from MLTC is 4% per month; if the RCFS is less than 6, the probability is 2% per month. The delay to a MAS appointment is randomly drawn from a normal distribution with a mean of 12 weeks and three time standard deviation of 2 weeks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-Risk Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFraction of patients in the population that are at high-risk. If a patient is classified as high-risk, a MAS appointment is scheduled at the earliest possible date. Low-risk patients are placed on the waiting list.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-residence with patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf the carer lives in the same household as the patient, then the probability of the carer being available to support the patient when needed is higher than if they live in different households.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship to patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf the carer is a family member then we assume the availability to support the patient when needed is higher. Spouses have the highest probability, followed by daughter and son.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas personal transport available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf the carer has their own transport, we assume the probability of being able to make an appointment is higher than when the carer has to use public transport. This likelihood may vary depending on the area of residence.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf a carer has full-time or part-time employment, then the ability to support the patient is limited. We assume not being employed has the minimum impact.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriaging service\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily patient triage capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA specific number of patients can be triaged each day. Patients who are not seen wait to be seen in the chronological order in which they were referred for examination.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMemory assessment service (MAS)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHours per day during which the MAS is available. The number of patients seen daily depends on staff availability and diagnostic workload.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvance scheduling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of working days for which appointments are scheduled and the length of the MAS advance scheduling list. Patients on the waiting list will be moved to this list when an appointment is scheduled.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily probability of staff absence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf a staff member is absent, the number of patients seen that day will be adjusted. MAS appointments will be rescheduled at the earliest possible date.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkforce and Workload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStaff roles include consultants, junior doctors, and nurses. Workload varies by training and experience, including patient consultation time, administrative tasks, and consultation time with a consultant for junior doctors and nurses. The maximum number of patients seen daily depends on the staff's time allocation to these tasks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe following assumptions were made:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eDementia progression\u003c/em\u003e: Due to the absence of definitive models for dementia progression and its interactions with MLTC, we assumed a linear progression for dementia. This assumption was made due to the unavailability of consistent data (e.g. diagnostic variables such as biomarkers) and the expectation that dementia progression has a limited impact at the early diagnosis stage that we are simulating. However, it enabled us to incorporate transitions from independent to dependent living into our model. We used a four-level dementia progression model, assuming the following initial distribution of dementia levels in the population: 0 (no cognitive decline, 15%), 1 (mild, 55%), 2 (moderate, 25%), and 3 (severe, 5%). Values were increased daily to simulate dementia progression. The daily increase for each patient was independently drawn at random from a uniform distribution ranging from 0.001 to 0.005. If a threshold of 2.8 was reached, this triggered a relocation to a care facility.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eMLTC\u003c/em\u003e: The RCFS was used as a proxy for MLTC. Patients with an RCFS less than 6 had a 2% monthly chance of hospitalization, while those with an RCFS of 6 or greater had a 4% monthly chance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eCaregivers\u003c/em\u003e: Family caregivers might have full-time jobs, get sick, miss public transport, or are not aware of an appointment, which can for example contribute to the issue of a patient missing a MAS appointment (Did Not Attend, DNA). A probability distribution defined by carer factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was used to describe the DNA probability of individual dyads. The distribution was designed to achieve an average DNA of 2%, in accordance with CRATE search. For individual patients a fixed DNA probability of 4% was used.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBy systematically altering parameters (i.e. creating different scenarios) and simulating how a population of individual patients and dyads moves through the system, we can gain insights on the impact these parameters have on system dynamics. By generating populations based on specific demographic distributions from different areas of the UK, we can observe how demographics, geography, and other observed properties influence system dynamics. The properties of the DiMLTC patient population and the transition probabilities between states were calculated using CRATE data or service data provided by CPFT.\u003c/p\u003e \u003cp\u003eSupplementary Table S1 presents the pseudocode for the implemented model and simulation algorithm, detailing the steps and logic used to simulate the system.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch questions and Simulation experiments.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used the model to explore the following questions: On a population level, how does time from referral to diagnosis (i.e. the service performance) change if the number of high-risk patients or the percentage of patients with MLTC increases? How does the performance change if individual patients and dyads did not attend appointments and these need to be rescheduled? On a service level, how does performance change based on staffing levels, staff absences, and task completion times? How does the performance change as a function of how many patients are on appointment and waiting lists?\u003c/p\u003e \u003cp\u003eThe simulation stopped when all patients were diagnosed, or a maximum simulation period of 156 weeks (3 years) was reached. Each simulation step corresponds to one day, allowing for detailed daily analysis. ABPMs include stochastic elements, meaning that randomness can influence the outcomes. Running multiple simulations helps capture the variability in results, ensuring that the findings are more robust and not due to random chance, and applicable to real-world scenarios. We repeated 2,000 independent simulations and observed the distribution of the time to diagnosis to obtain a range of possible outcomes. For each repetition the same initial population was used. Also, for simulations with the same population parameters always the same population was used to facilitate comparability of results.\u003c/p\u003e \u003cp\u003eThe model was implemented in the Python 3 programming environment and calculations performed on the University of Essex\u0026rsquo;s High Performance Computing facility. Supplementary Table S2 summarises the parameter combinations used to initialise the patient-carer dyad population. Supplementary Table S3 summarises the model parameters for the simulation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the explored scenarios and calculated simulation results using our agent-based probabilistic approach. One accepted quality measure of waiting time is the percentage of patients diagnosed within 18 weeks (described as \u0026lsquo;Good Results\u0026rsquo; here). The reported mean, median, 90th percentile (P90) of the calculated times to diagnosis, and the Good Results were derived from data pooled across all simulation runs. The upper part of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises scenarios with empty appointment and waiting lists (condition EMPTY). With the standard parameter setting characterising CPFT service capacity and a representative population (first line highlighted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the good results are 99.77%. The mean, median, and P90 of the time to diagnosis are reported for the entire population, as well as separately for high-risk and low-risk patients and for patients that converted from low-risk to high while on the waiting list. On a whole population level, 50% of patients/dyads are seen within 18 days and 90% within 33 days. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a) shows the histogram of the estimated times to diagnosis. The histogram shows a bimodal distribution, with a large peak around 20 and another peak around 105 days. The second peak is attributed to medical episodes related to MLTC. About 18% of patients have been hospitalised at least once, with a median stay of 86 days outside the diagnostic process. The maximum time a patient spent outside the process was 470 days. For EMPTY, independently of the parameter settings the MAS service always manages to achieve good results above 95%. This good performance also means that patients on the waiting lists are very unlikely to convert from low-risk to high-risk while waiting for an appointment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimulation results. Population parameters cover the population size, the percentage of high-risk (HR) patients, the percentage of patients with RCFS larger or equal than 6 (proxy for MLTC), and the Did Not Attend (DNA) rate for dyads. Service parameters include the number of Consultants (C), Junior Doctors (JD), and Nurses (N), staff absence rate (Absn), reduction in administrative time (Admin time), and the status of the appointment list (Appt. List: empty or full) and the number of patients on the waiting list (Wait. List). The column MXP reports the maximum number of patients that can be seen in the MAS as function f(C, JD, N, Admin). The table shows the mean (M), median (Mdn), and 90th percentile (P90) of time to diagnosis for the entire population, as well as for high-risk, low-risk, and patients transitioning between risk categories. For the latter also the percentage of patients converted is reported (PC). The last column reports the percentage of patients diagnosed within 18 weeks (Good Results). Other model parameters are: Probability of hospitalisation due to MLTC is 4% per month if RCFS is larger or equal to 6 and 2% otherwise, Probability of individual patients DNA (4%), Triaging capacity (from Mon-Fri, 12 people per day), Memory assessment service (MAS) hours (Mon-Wed, 8 hours per day), MAS advance scheduling (6 weeks), Time allocation for consultants (1 hour to see the patient, 1 hour for admin), junior doctors and nurses (1.5 hours to see patient, 1.5 hours for admin), diagnosis success rate (75%) with a maximum of 2 appointments required to make a successful diagnosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"26\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c11\" namest=\"c5\"\u003e \u003cp\u003eServices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c15\" namest=\"c13\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c18\" namest=\"c16\"\u003e \u003cp\u003eHigh-risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c21\" namest=\"c19\"\u003e \u003cp\u003eLow-risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e \u003cp\u003eLow-risk to high-risk converter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c26\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRCFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAbsn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdmin time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAppt. List\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eWait. List\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMXP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eMdn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eP90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eMdn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003eP90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003eMdn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003eP90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003eMdn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003eP90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003eGood Results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e55%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eEmpty\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e\u003cb\u003e33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e\u003cb\u003e0.00%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e\u003cb\u003e99.77%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e 3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e10.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.95%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.95%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.95%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e99.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e97.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e95.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e55%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e5.2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eFull\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e248\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e263\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e287\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e269\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e265\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e\u003cb\u003e295\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e\u003cb\u003e2.81%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e\u003cb\u003e161\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e\u003cb\u003e161\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e\u003cb\u003e247\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e\u003cb\u003e8.62%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e4.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e8.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e10.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e3.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e250\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e90.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e500\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e1.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e42.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e750\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e1.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e1.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1250\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e16.23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e1.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e70%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e2.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e1.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e8.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e85.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c24\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c25\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e92.58%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe lower part of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the results reflecting the situation where all memory service appointments are booked for the next six weeks and 1500 individuals are on the waiting list (condition CURRENT, current situation at the time this work was written.). With the standard staffing configuration 22 patients can be seen on service days. With 3 days service per week and 6-week advance schedule, there are 396 patients waiting for an appointment. In this scenario, with the same service capacity only 8.63% good results are achieved, with 50% of all patients diagnosed within 263 days (37 weeks) and 90% of patients within 287 days (41 weeks). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b) shows the related histogram. For this case, about 3% of the patients convert from low-risk to high-risk while waiting for an appointment. The histogram shows a multimodal distribution with peaks around 250, 300 and 350 days. This is again a result of adverse medical events due to MLTC. About 35% of patients were hospitalized at least once, with a median and maximum stay outside the diagnosis process of 86 days and 547 days, respectively.\u003c/p\u003e \u003cp\u003eThe service performance shows larger variability for CURRENT. An increase of the high-risk cases from 8\u0026ndash;16% has a significant impact on the good results (increase from 8.62\u0026ndash;16.12%), but this is caused by the systematic prioritisation of high-risk patients to be scheduled for an appointment within the next 6 weeks. The performance for high-risk patients is consistent for all experiments. This comes at the cost of low-risk patients, whose median/P90 time to diagnosis increased from 265/295 days to 294/324 days. An increase of patients with MLTC and associated assumptions for hospitalisation, have a limited impact on performance. The median/P90 increases by a maximum of 6 days. An increase of the DNA for dyads from 2\u0026ndash;4% has minimal impact on the good results and leads to an expected increase of median/P90 times to diagnosis of 6 days. On a service level, increased staff leads to reductions of median/P90 time to diagnosis for low-risk patients from 265/295 to 227/265; for one additional consultant the reduction is more significant, from 265/295 to 197/227. performance. Increased levels of staff absences also have a more significant impact on low-risk patients: an increase from 5.2\u0026ndash;10% leads to a median/P90 increase from 265/295 to 292/320, i.e. to an increase of about 4 weeks. The reduction of time for administration by 30 minutes had a significant impact and showed similar behaviour to adding one additional consultant. The 30-minute reduction means that on average not 22 patients but 26 patients can be seen every day. Increasing staffing by 1 consultant, 3 junior doctors and 5 nurses and reducing admin times by 30 min, increase the good results to about 93%. A full appointment list and with 500 patients on the waiting list, increases the good results from 8.62\u0026ndash;43%. With 750 patients on the list there is no impact on the service performance (8.62\u0026ndash;8.8%), which then remains constant around 8.6% independently of the length of the waiting list.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(c) and (d) summarise the assessment of variability of the simulation results, by showing the distribution of the mean and median times to diagnosis and good results for the 2000 independent simulations. The variability of the calculated median performances is about 3 weeks for EMPTY and 4 weeks for CURRENT.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have presented what we believe to be the most sophisticated model of an NHS MAS produced to date. Its utility is enhanced by the use of real-world clinical data wherever possible for calibration. Though these clinics vary across locations, much of the basic structure is retained and we are now developing the model to include an easy-to-use interface where interested users can add details of their own service to derive individualised data. (Smith and Surr, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) Importantly we were able to model not only basic parameters such as staffing levels and clinic slots, but also more complex and crucial factors including interfaces with carers (as patient-carer dyads) and MLTC.\u003c/p\u003e \u003cp\u003eAs a demonstration of potential utility, we investigated the impact of altering several parameters and discovered some surprising results. Firstly, the simulation reveals that the system's emergent behaviour and performance vary based on the initial conditions, whether EMPTY or CURRENT where these terms reflect the presence or absence of waiting lists. The EMPTY scenario suggests that memory services can cope well and perform highly with current resources, even if the number of the patient population increases. Conversely, the CURRENT scenario confirms that the service is overloaded. Reducing the waiting list from 1500 to 250 would enable good service provision, with 90% of patients diagnosed within 18 weeks. In terms of service optimization, the model can be used to identify service parameters that would allow reducing the waiting list to a manageable size before the service resources can go back to normal, knowing that good performance can be achieved for the majority of patients in a sustainable way. This is important as it suggests that an effort to clear current waiting lists would bring sustainable benefits to the system rather than temporary respite. Such an effort to clear waiting lists is possible and may be a worthwhile use of resources.\u003c/p\u003e \u003cp\u003eFor CURRENT, doubling staff absences to 10% extended median/P90 waiting times by up to 26/24 days (16/13 days assuming 8% absences). Additionally, higher levels of DNAs added up to 3/7 days to the waiting times. When comparing median/P30 times between 4% absences (256/280) and 4% DNA (266/294), the results suggest that the impact of DNA is more significant. Therefore, while ensuring staff health is essential, efforts to decrease non-attendance, such as sending reminder text messages, distinct letters to patients and carers if they do not co-reside, or developing AI models to predict attendance and dynamically double-book appointments, may be a valuable allocation of resources.\u003c/p\u003e \u003cp\u003eTriage is a standard clinical response to increased demand, but in our model we identify potential unintended consequences of triaging greater numbers of people to be seen quickly. If 16% of people are classified as high-risk and triaged as priority, those not prioritised face waits so long some will never be seen as they will not live long enough to reach the front of the queue. Similarly, increasing staffing levels is a way to cope with increased demand. In our model, increasing consultants had the biggest impact on the system and the number of staff required to meet the target of seeing people within 18 weeks of referral was achievable. Though consultant staff are expensive their disproportionate impact may well offset that cost by improving system efficiency.\u003c/p\u003e \u003cp\u003eMany of the potential interventions to cut waiting time, including increasing staffing, require increased funding and the ability to find staff. Perhaps the most dramatic finding of our modelling was the impact of cutting administration time. Administrative tasks include reviewing patient history, documenting findings, coordinating with other healthcare professionals, scheduling follow-up appointments and, perhaps most profoundly, data entry. Cutting this by half had a similar impact on waiting times to increasing the number of consultants from 3 to 4. The addition (or removal) of administration is a choice for health services to make and does not necessarily require change in resource, particularly if technological tools such as ambient dictation or efficient electronic patient records are used. Our model can also be used to illustrate the impact of increased administration. Fifteen minutes of reduced administration means a decrease in the overall median/P90 times from 263/287 to 227/246; additional 15 minutes reduce the times to 189/197. Of course potential improvements depend on the combination of service parameters. However, such calculations allow managers to make decisions to add or remove admin tasks for clinicians based on the impact on clinical services.\u003c/p\u003e \u003cp\u003eOur study has some limitations. By their nature, all models are \u0026ldquo;wrong\u0026rdquo;, not least because they unavoidably simplify complex systems. (Saltelli et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) There will be parameters which we have not included and others where we have made inaccurate assumptions. Despite our best efforts to use data from real clinical services we have not been able to do that in every case and it is possible the county where we have derived the data (Cambridgeshire) may be in some ways unrepresentative of other areas which could limit the applicability of our model. Nevertheless, we believe our model represents the most sophisticated attempt to date to describe NHS MAS. It incorporates crucial parameters such as MLTC and the impact of dyad relationships. This model should provide a useful template for optimising current clinical services for the benefit of both those who use them and those who pay for them. As demand grows these approaches will be vital to maximise resource and efficiency of services. Optimization typically requires a fitness function, which evaluates and compares potential solutions to identify the best one based on predefined criteria. In future work, these criteria must be defined with stakeholders to ensure that not only financial aspects are considered, but also patient and carer experiences and the overall quality of the service.\u003c/p\u003e \u003cp\u003eThe model developed is flexible and can be adjusted and expanded to fit into the broader ecosystem of NHS services. Although not all factors have been implemented in the current model, and despite many simplifications, the outcomes of different scenarios are plausible and provide insights into how patient-caregiver dynamics and MLTC impact on time to diagnosis. Future work will extend the model, and collaboration and co-design with stakeholders will be key to ensuring that the findings have a meaningful impact on patients, dyads and healthcare delivery. (Wong-Lin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) There is a consensus that reducing waiting times is important, but the findings also support the view that system-wide changes are needed to make a difference. Otherwise, patients will be diagnosed quickly, only to queue up elsewhere in the system when new bottlenecks arise.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e This study was funded by the National Institute for Health and Care Research (NIHR) Engineering and Physical Sciences Research Council (EPSRC) under its Systems Engineering Innovation hubs for Multiple long-term Conditions (SEISMIC) Programme (NIHR158147). This study is supported by the Applied Research Collaboration East of England (NIHR ARC EoE) at Cambridgeshire and Peterborough NHS Foundation Trust. All research at the Department of Psychiatry in the University of Cambridge is supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014) and NIHR Applied Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. BRU is supported by a generous donation from Gnodde Goldman Sachs Giving.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlzheimer Society (2024) The economic impact of dementia - Module 1: Annual costs of dementia, (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.alzheimers.org.uk/sites/default/files/2024-05/the-annual-costs-of-dementia.pdf)\u003c/span\u003e\u003cspan address=\"https://www.alzheimers.org.uk/sites/default/files/2024-05/the-annual-costs-of-dementia.pdf)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAujla N, Tooman T, Arakelyan S, Kerby T, Hartley L, O\u0026rsquo;Donnell A, Guthrie B, Underwood I, Jacko JA, Anand A (2024) New horizons in systems engineering and thinking to improve health and social care for older people. Age Ageing 53(10):afae238\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonabeau E (2002) Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences. ;99(suppl_3):7280-7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHandley M, Windle M, Mathie E et al Living with dementia and other long-term conditions: what works for patient/caregiver dyads? A realist review, 23 January 2025, PREPRINT (Version 1) available at Research Square [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-5874431/v1]\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-5874431/v1]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Lawley MA, Siscovick DS, Zhang D, Pag\u0026aacute;n JA (2016) Peer reviewed: agent-based modeling of chronic diseases: a narrative review and future research directions. Prev Chronic Dis. ;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Rexachs D, Epelde F, Luque E (2017) An agent-based model for quantitatively analyzing and predicting the complex behavior of emergency departments. J Comput Sci 21:11\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHS England, Sickness Absence NHS, Rates A (2024) NHS England, 9 January 2025 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://digital.nhs.uk/data-and-information/publications/statistical/nhs-sickness-absence-rates/august-2024\u003c/span\u003e\u003cspan address=\"https://digital.nhs.uk/data-and-information/publications/statistical/nhs-sickness-absence-rates/august-2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [cited 22 Jan 2025])\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerez L, Dragicevic S (2009) An agent-based approach for modeling dynamics of contagious disease spread. Int J Health Geogr 8:1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRailsback SF, Grimm V (2019 Mar) Agent-based and individual-based modeling: a practical introduction. Princeton University Press, p 26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos AL, Ferreira JV, Barcel\u0026oacute; J (2011) Model-based systems engineering: An emerging approach for modern systems. IEEE Trans Syst Man Cybernetics Part C (Applications Reviews) 42(1):101\u0026ndash;111\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRees J, Burton A, Walters K, Cooper C (2023) Exploring the provision and support of care for long-term conditions in dementia: A qualitative study combining interviews and document analysis. Dementia 22(4):820\u0026ndash;837\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoyal Academy of Engineering (2017) Engineering better care: A systems approach to health and care design and continuous improvement, (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://raeng.org.uk/media/wwko2fs4/final-report-engineering-better-care-version-for-website.pdf)\u003c/span\u003e\u003cspan address=\"https://raeng.org.uk/media/wwko2fs4/final-report-engineering-better-care-version-for-website.pdf)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaltelli A, Bammer G, Bruno I, Charters E, Di Fiore M, Didier E, Espeland WN, Kay J, Piano SL, Mayo D, Pielke R Jr (2020) Five ways to ensure that models serve society: a manifesto. Nature. ;582\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilverman BG, Hanrahan N, Bharathy G, Gordon K, Johnson D (2015) A systems approach to healthcare: agent-based modeling, community mental health, and population well-being. Artif Intell Med 63(2):61\u0026ndash;71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith SJ, Surr C (2024) Exploring challenges and innovation in memory assessment services in England and Wales\u0026ndash;a national survey and case study approach. BMC Health Serv Res 24(1):1\u0026ndash;6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitty C (2023) \u003cem\u003eChief Medical Officer\u0026rsquo;s annual report 2023: health in an ageing society - GOV.UK 2023\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.uk/government/publications/chief-medical-officers-annual-report-2023-health-in-an-ageing-society\u003c/span\u003e\u003cspan address=\"https://www.gov.uk/government/publications/chief-medical-officers-annual-report-2023-health-in-an-ageing-society\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong-Lin K, McClean PL, McCombe N, Kaur D, Sanchez-Bornot JM, Gillespie P, Todd S, Finn DP, Joshi A, Kane J, McGuinness B (2020) Shaping a data-driven era in dementia care pathway through computational neurology approaches. BMC Med 18:1\u0026ndash;0\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYew PY, Devera R, Liang Y, Khalifa RA, Sun J, Chi NC, Chou YC, Tonellato PJ, Chi CL (2024) Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions. Alzheimer's \u0026amp; Dementia. Jun 11\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"3cc6851d-5643-4fb2-9462-f79b5803a627","identifier":"10.13039/501100000272","name":"National Institute for Health Research","awardNumber":"NIHR158147","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Essex","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dementia, multiple-long term conditions, patient-carer dyads, systems engineering, agent-based modelling (ABM), probabilistic models, complex systems","lastPublishedDoi":"10.21203/rs.3.rs-5898259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5898259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs the global population ages, healthcare systems must anticipate and manage the interactions of older adults with dementia and other long-term conditions. Optimising dementia assessment and diagnosis services is essential to enhance efficiency and improve patient experience. Solutions must address the evolving needs and complexity of patients, caregivers, and healthcare services.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAims\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe sought to develop a comprehensive model of a memory assessment service (MAS) to examine how patients, unpaid carers and health system factors influence service performance. The primary focus was to gain insight into how these factors affect the time from referral to diagnosis and to identify evidence needed for future modelling work.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe used systems engineering and an agent-based probabilistic modelling approach, informed by real NHS clinical data, to simulate the MAS. To reflect the experience of most people attending memory assessment services our model included the impact of multiple long-term conditions (MLTC) and if the patient had a known carer (dyad).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe found that system dynamics and behaviour change based on the pressure the system is under, with a tipping point in the length of the waiting list after which performance is consistently impacted. The impact of staff absences appears to be less significant than comparable levels of patient non-attendance. High levels of triage adversely affected service performance, whereas increased staffing levels and, in particular, reduced administrative burdens improved performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe have demonstrated the feasibility of constructing sophisticated models of memory assessment services, incorporating multiple factors to study behaviours that emerge from interactions in the system. Such models could be valuable tools for NHS managers, not only to optimise the efficiency of services but also to develop new approaches to service delivery.\u003c/p\u003e","manuscriptTitle":"Understanding dynamics to Streamline Access: Systems Engineering and Agent-Based Probabilistic Modelling in Memory Assessment Services","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 09:27:46","doi":"10.21203/rs.3.rs-5898259/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd5cb344-923a-4a28-967f-8808fd4169dc","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43392854,"name":"Operations Research"},{"id":43392855,"name":"Computational Mathematics"},{"id":43392856,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2025-01-28T09:27:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 09:27:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5898259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5898259","identity":"rs-5898259","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}