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A simulation modeling technique for projecting future extened care bed requirements Kallstrom, Elizabeth 1982

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A SIMULATION MODELING TECHNIQUE FOR PROJECTING FUTURE EXTENDED CARE BED REQUIREMENTS by ELIZABETH KALLSTROM / / M . S c , The University of Br i t i sh Columbia, 1979 i A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE (Health Services Planning) i n THE FACULTY OF GRADUATE STUDIES Department of Health Care and Epidemiology We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA APRIL I 982 © Elizabeth Kallstrom, 1982 In presenting t h i s thesis i n p a r t i a l f u l f i l m e n t of the requirements for an advanced degree at the University of B r i t i s h Columbia, I agree that the Library s h a l l make i t f r e e l y available for reference and study. I further agree that permission for extensive copying of t h i s thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. I t i s understood that copying or publication of t h i s thesis for f i n a n c i a l gain s h a l l not be allowed without my written permission. Department of • /fefieJM Cfifa-ffrjp ^4>,7y^f//y,/)^y The University of B r i t i s h Columbia 1956 Main Mall Vancouver, Canada V6T 1Y3 Date ftffrtU Q ? f DE-6 (3/81) - i i -ABSTRACT A simulation model was developed for the Greater Vancouver Regional Hospital Dis t r ic t for the purpose of projecting future Extended Care Bed requirements. The model u t i l izes data which are not usually incorporated in such projections but which are c r i t i ca l for ensuring maximum accuracy of the projections. Length of stay and length of wait information are two such items. In 1980, the average length of stay in an Extended Care Unit was found to be 30.3 months and the average length of wait for admission to a unit was approximately 9 months within the G.V.R.H.D. The increase in the numbers of elderly in this population was projected to be 16,500 persons from 1981 to 1986 or a percentile increase from 11.7$ of the total population to 12.4$. In order to maintain a constant waiting period of 9 months, an additional 300 Extended Care beds were estimated to be required by 1986. The length of the waiting l i s t , or the queue, would increase sl ightly under such conditions. An additional 2,200 beds would be required to eliminate the waiting period completely and to deplete the queue. The effect of reducing the application rate was also investigated. Waiting period and queue length were very sensitive to such a change as evidenced by sharp reductions in both. The opposite was true when the application rate was increased; both waiting time and queue length increased considerably. - i i i -The effects of varying the proportion of activated on-hold applicants and the time interval for additions of new beds were also investigated. The use of this model in forecasting Extended Care Bed requirements in the short term is discussed with a view to comparing these projection results to those of other forecasting methodologies employed in Canadian Metro regions. Future data requirements of the model are also discussed. - iv -TABLE OF CONTENTS PAGE ABSTRACT I i TABLE OF CONTENTS iv LIST OF TABLES vi iI LIST OF FIGURES ix ACKNOWLEDGEMENT x CHAPTER I - INTRODUCTION 1 1. Organization of Long Term Health Services for the Aged in 3 Bri t i sh Columbia „ 2. Organization of the Greater Vancouver Regional Hospital 6 Dis t r ic t 3. Statement of the Problem 8 CHAPTER II - LITERATURE REVIEW 9 1. The Greying of Canada 9 2. Approaches to Meeting the Problems of the Elderly 12 A. Development of Long Term Care Programs in Industrialized 12 Nations (i) Great Britain 13 (i i) Sweden 14 ( i i i ) United States 15 (iv) The Canadian Situation 17 B. Sociology of Institutionalization 18 C. Current Issues in Geriatric Care Provision 21 ( i) A Problem: Acute'Care Bed-Blocking 21 ( i i ) A Solution: Day Hospitals 23 - v -PAGE D. Methods Used to Project Future Bed Requirements 25 (i) Assessment of Need 25 ( i i ) Linear Extrapolation Methods 27 a) Toronto Formula 28 b) Ottawa Formula 29 c) Manitoba Method 30 d) Bri t ish Columbia - Long Term Care Formula 32 e) Br i t i sh Columbia - Hospital Programs Formula 33 f) Summary of Linear Methods Limitations 34 ( i i i ) Simulation Techniques 35 a) Qualitative modeling 35 b) Descriptive modeling 36 c) Prescriptive modeling 36 d) The present model 38 CHAPTER I I I - METHODS 41 1. Simulation Model Flow Diagram 41 2. Components 49 A. Input Data 50 B. Output Data 54 C. Example 58 3. Options 60 A. Constant Application Rate Increase 60 B. Reduction in Application Rate by 10$ 60 C. Reduction in Application Rate by 20$ 61 D. Increase in Application Rate by 10$ 61 E. Increase in Activated on-hold Applicants 61 F. Different Time intervals for additions of beds 61 4. Technical Program Specifications 61 - v i -PAGE CHAPTER IV - RESULTS 62 1. Baseline Simulations 62 A. Constant Appl ication Rate 62 B. Approved Beds Added 63 C. Projected Application Rate 63 D. Projected Application Rate Plus Added Approved Beds 63 2. The First Option - Application Rate increase proportional to 64 population increases 3. The Second Option - Reduced application rate 65 4. The Third Option - Increased application rate 67 5. The Fourth Option - Increase in activated on-hold applicants 68 6. Different Intervals 70 CHAPTER V - DISCUSSION 76 1. Cr i t i ca l Assumption of the Model 76 A. Time Frame 76 B. Length of Wait 77 C. Length of Stay 78 D. Behavioral Aspects of Demand Generation 79 E. Equity of Extended Care Services 80 2. Alternative Planning Methodologies 80 A. Br i t i sh Columbia Hospital Programs Division Method 81 B. Br i t i sh Columbia Long Term Care Bed Allocation Formula 81 C. Ottawa-Car Ieton Bed Projection Formula 82 D. Metro Toronto Formula 82 E. Study Simulation Model 83 F. Summary 83 — v i i — 3. Applicabil i ty of Model to Long Term Care A. Long Term Care Services B. Alternative Services 4. Conclusion and Recommendations A. Study Findings B. Effects of Priorization of Applicants C. Research Poss ib i l i t i es D. Pol icy Impl ications REFERENCES APPENDIX A APPENDIX B APPENDIX C APPENDIX D APPENDIX E - v i i i -LIST OF TABLES PAGE Table 1 Sensitivity of Model to Input Variables of 57 Interest Table 2 Waiting Times and Queue Lengths Under Baseline 63 Simulation Conditions Table 3 Simulated Waiting Periods and Queue Lengths at 65 Different Bed Levels Table 4 Simulated Waiting Periods and Queue Lengths at 68 Different Application Rates Table 5 Effect on Waiting Periods and Queue Lengths of 71 Three Simulated Bed Additions on January, 1984. Table 6 Detailed Waiting Periods (Months) for Simulation 72 Experiments When Beds are Added in Three Intervals, January 1984, January 1985, and January 1986. Table 7 Detailed Waiting Periods (Months) for Simulation 73 Experiments When Beds are Added in One Pulse in January 1984. Table 8 Detailed Waiting Peiods (Months) for Simulation 74 Experiments under Different Queue Conditions. Table 9 Comparison of Projected Effects of Options and 84 Outcomes using Different Planning Methods. - ix -LIST OF FIGURES PAGE Figure 1 Flow Diagram of Movement of Extended Care Clients 42 Figure 2 Input Variables, Their Limitations and Suggested 48 Ideal Formats for Use in the Simulation Model Figure 3 Length of Wait at Different Bed Levels 66 Figure 4 Length of Wait at Different Application Rates 69 Figure 5 Length of Wait at Different Pulse Rates 75 - x -ACKNOWLEDGEMENTS I would like to express my sincere gratitude to Annette Stark, Ph.D., whose advice and encouragement guided the completion of this thesis. Special thanks is extended to Dean Uyeno, Ph.D., for his work in developing the simulation model and his patience in explaining the logic. I would also I ike to thank Mr. L. Detwiller for taking time out from a busy schedule to read this thesis and make many valuable suggestions. The collection of data was funded by the Greater Vancouver Regional Hospital D i s t r i c t , which is gratefully acknowledged. The study was also supported by funds made available to me through a National Health and Welfare Student Fellowship. - 1 -CHAPTER I INTRODUCTION This is a discussion of several planning methodologies for projecting future Extended Care bed requirements in the Lower Mainland area. The study presents a simulation model developed for the Greater Vancouver Regional Hospital D i s t r i c t . The model ut i l izes existing data, including s ta t is t ics on length of waiting periods and length of stay in Extended Care Units (1). The model also attempts to assess the effect of avai labi l i ty of other services on the application rate for admission to the Extended Care Units ( i . e . , the measure of expressed demand on this system), and on the length of the waiting periods. The structural changes in population growth in a l l industrialized countries wi l l have profound consequences on demand for social services. The aging phenomenon of the Canadian population has not quite reached the proportions of other Western countries. The 1971 Census reported 8.1$ of the Canadian population to be over 65 years of age, while the elderly in Sweden made up 13.7$ of the total population, in England 12.4$ and in the U.S. 9.9% (2). In 1976 the proportion of the over 65's in Canada had reached 8.1% and was projected to reach 9.5$ in 1981 and ]2% in 2001. For the Greater Vancouver area, these figures were even more dramatic. The 1976 Census showed the Metropolitan region of Vancouver to have in its population one of the highest percentages of elderly in Canada; ful ly 10$, second only to Victoria with 15$ of its population over the age of 65 (3). The projected increase from 1976 to 1986 of the elderly in the Greater Vancouver Regional Dis t r ic t (G.V.R.D.) is an additional 35,000 persons aged over 65 years, a 30$ increase in ten years to 12.4$ of the total population. The absolute increase in the very old group, those over 85 years is projected to be 3,500 individuals, or a 29$ increase over 10 years (4). Statist ics Canada has projected that the over 85 years of age group in Bri t ish Columbia wil l double between 1976 and the year 2001 (5). The progressive proportional increase in the older extremity of the population has important implications for the institutional sector of the health care system. The demand generated by this aging population is expected to increase both the number of hospital admissions and the average length of stay (6). The nature of this demand is also expected to shift towards long term health services, in response to chronic and degenerative diseases which require maintenance care rather than episodic illnesses which require interventions of a curative nature (7). In addition, planning for long term health services to meet the needs of an increasingly elderly population is often hampered by the lack of suitable data bases regarding past experience and future expectations (8). Many of the elderly experience a gradual deterioration in functional ab i l i ty and may therefore require both long term care services and extended care hospitalization in their life-time. With this in mind, the following section describes the Long Term Care Program and its various components. - 3 -1. Organization of Long Term Health Services for  the Aged in Br i t i sh Columbia On January 1, 1978, the Bri t i sh Columbia Ministry of Health announced the start of its Long Term Care Program (8). The Program encompasses, in whole or in part, services provided under the Health Act (1973), Hospital Act (1973), and the Community Care Fac i l i t i e s Licensing Act (1976) (9-11). In addition, a Home Support Service is provided. The Public Health Units are the organizational structures responsible for the operation of the Long Term Care Program at the local level. A Long Term Care Administrator is based in each Unit and reports through the Director of the Health Unit to the Director, Long Term Care and Home Care. Although Extended Care benefits became an integral part of the Long Term Care Program as of January 1, 1978, their operation continued to be governed by the Hospital Insurance Act Regulations (1977) (12). Extended Care hospitals are governed by Part I and Part II of the Hospital Act (1975) and Private Hospitals by Part II of the same Act. The Program, however, forms a cohesive structure providing a comprehensive spectrum of long term care services throughout the Province. Most of the elements of this system were present prior to 1978, but in a very fragmented form. The philosophy on which the Program is based rests mainly on the premise that people are responsible for caring for themselves and their - 4 -families as long as they are capable of doing so (13). The Long Term Care Program represents a continuum of services for persons unable to live independently because of health related conditions. It provides for a range of services, from Personal Care level at which clients are not in need of ski l led nursing care, to Extended Care services where clients are vir tual ly bed ridden. It is designed to promote the highest level of independence for its beneficiaries, with the aim of accessibil i ty for a l l needy citizens in their own communities. Care is provided at home when i t is feasible to do so. The Long Term Care Program has a one-door entry procedure through the Health Unit office, which processes the referrals. These may come from the applicant himself or from his family, his physician or the fac i l i ty in which he currently resides. The referrals are registered chronologically and are then assessed as to the best course of action for each individual applicant. In some cases no further action wil l be required but for others there wi l l be a thorough investigation and a decision as to the level of care needed. In these cases the standard assessment of each applicant considers functional, social and medical conditions which is recorded on a four-part assessment instrument, the Long Term Care Assessment Form (LTC 1) (14). When the applicant is deemed to require Extended Care, responsibility for the assessment procedure is shared jo int ly with Central Registry of Hospital Programs Division of the Ministry of Health. The assessment procedure establishes both the level, (Personal/ Intermediate/Extended) and the type of care (Home/Facility), required by - 5 -the applicant. A full description of the c r i te r ia for each level of care and some characteristics typical of applicants at each level are included i n Append ix A. An applicant assessed as e l ig ib le for Extended Care benefits may receive these services in the home in the form of adequate Home Support, if this is safe, or he may be waitlisted for admission to a public Extended Care Unit. Alternatively, such an applicant may be placed in a designated Private Hospital on an interim basis if the waiting period for the desired Extended Care Unit is very long. Differences in e l i g i b i l i t y c r i te r ia may influence placement of Extended Care cl ients . Any individual who is a Canadian Citizen or a Landed Immigrant and has resided in the Province for at least twelve consecutive months is entitled to become a beneficiary of the Long Term Care Program, but applicants for Extended Care Units become e l ig ib le after three months residency. Extended Care Units provide 24 hours of professional nursing supervision in addition to a multi-disciplinary staff of physiotherapists, occupational therapists, social workers, dieticians, pharmacists, and attending physicians. They provide an active program for those who require long term institutionalization but not the services of an Acute or Rehabilitation hospital (15). Medical e l i g i b i l i t y c r i te r ia are based mainly on the ambulatory ab i l i ty of the c l ien t . In addition to the conventional f a c i l i t i e s described above, Vancouver provides a new type of service through its two Geriatric Assessment and - 6 -Treatment Centres at Mount Saint Joseph's Hospital and Banfield Pavil ion. In order to properly care for the elderly who suffer from multiple pathologies, these centres provide an inpatient unit, a Day Hospital program, and an Outreach component with full diagnostic capabilit ies operating out of the attached Acute Care hospital. Although the applications and waitl ist ing for admission to Extended Care Units are processed by the Central Registry of Hospital Programs Division, which ensures that wai t l is t seniority is purely chronological and Province-wide, the majority of applicants wish to enter a f ac i l i t y in their, own municipality of residence. The Lower Mainland constitutes a unified geographical entity for Extended Care admissions, with a relatively free flow of applicants across municipal boundaries within the G.V.R.D. (22$ of admissions), but very l i t t l e movement across regional d i s t r i c t boundaries (2$) (16). The Regional Hospital Dis t r ic t also has the mandate for hospital construction, including Extended Care Units. 2. Organization of the Greater Vancouver  Regional Hospital Dis t r ic t The Greater Vancouver Regional Hospital Dis t r ic t is charged with planning and establishing hospital f ac i l i t i e s for the region. Construction costs of Extended Care Units are met by the Hospital Dis t r ic t and Hospital Programs Division on a 60/40 cost-sharing basis (8). However, operation of Extended Care Units is the sole responsibility of Hospital Programs Div is ion. - 7 -The purpose of a regional hospital d i s t r i c t is to establish, construct, enlarge, and maintain hospitals and hospital f ac i l i t i e s and to grant aid for such establishments' construction, enlargement, and maintenance. The d is t r ic t may also raise in any year, by making provisions in its budget, an amount not exceeding the product of one quarter of a mi I I on the assessed value of land. The Regional Hospital Advisory Committee of the Dis t r ic t . . . sha l l , when requested by the Board, review the hospital projects proposed by the boards of management of the hospitals in the Dis t r ic t and recommend regional programs for the establishment and improvement of hospitals and hospital f ac i l i t i e s in the Dis t r ic t for presentation to the Board for approval. The Hospital Advisory Committee determines pr ior i t ies for the location of additional beds and related diagnostic/treatment f a c i l i t i e s , and studies and promotes, among hospitals, changing patterns of care and greater cooperation in pooling services. The committee further reviews a l l capital grants-in-aid from the 1/4 mill fund. The terms of reference for the G.V.R.D. Board's Hospital Committee state that the Committee wi l l consider recommendations made by the Hospital Advisory Committee from a pol i t ica l and overall financial point of view. The committee then makes recommendations to the Board for total financing commitments and approvals for specific hospital projects (8). - 8 -3. Statement of the Problem The purpose of this thesis is to outline a methodology for projecting future Extended Care bed requirements for the geographical entity G.V.R.D. The simulation model developed for this purpose u t i l izes information which is not usually incorporated in such projections, but which is c r i t i c a l in ensuring their maximum accuracy. Length of stay and length of wait information are two of the most important data items in projecting bed requirements (16); by themselves the number of existing beds and the number of patients on waiting l i s t s give incomplete information on which to base projections. The f i r s t chapter of this thesis is an Introduction outlining the setting in which the study takes place, and the following chapter contains a review of the literature concerning the extent of the aging phenomenon in our society and the various approaches to meeting the perceived needs of the elderly. Chapter three describes the method used in this study - i e, the simulation model - and outlines the data requirements along with a flow diagram of the logistics of the Extended Care system in Bri t ish Columbia. Chapter four presents the results of the computer simulation experiments and in Chapter five, the findings are compared with the alternative methods in common use and suggestions are made for improvements in future research in this area. - 9 -CHAPTER I I LITERATURE REVIEW 1. The Greying of Canada More than 75$ of the elderly in Canada are afflicted by some type of chronic i l lness . Although (in 1976) they constituted only 9% of the population, they accounted for 15$ of all physician services and 35$ of a l l patient days in hospitals - this is expected to increase to 45$ by the year 2001 (17). Perhaps we are entering a period when there wil l be social disadvantage in increasing the average l i fe span because of the onerous cost of providing for the larger number of seniors. On the other hand, i t may become possible to increase - even to age 200 - the healthful and productive years by investing in basic studies of the aging process, by eliminating major k i l l e r s such as circulatory and neoplastic diseases, by pharmaceutical, dietary, and immunological manipulations, by reducing body temperature, and even by re-instituting repressed gene function (18). Improvements in medical science must not simply prolong l i f e ; they must also add health to the resulting years. There must be more emphasis on prevention of i l lness, as i t may not be economically viable in terms of resource consumption to offer certain expensive health care resources to an elderly individual. Di f f icu l t decisions must be made in regard to what kinds of medical f ac i l i t i e s should be made available. - 10 -Currently, Canada institutionalizes more of its elderly than any other Western nation. Although most of the elderly in Canada maintain their own households: 64.3$ own their own homes compared to 61.8$ of the under 65's; 8.7$ live in some form of collective housing, compared to only 1$ of the population under 65 years of age. The elderly do, however, represent 45.2$ of a I I persons living in collective dwellings, which include nursing homes ( 1 9 ) . In 1976, for example, 8$ of the elderly were in institutions in Canada compared to 6.3$ in the U.S. , and 5.1$ in the U.K. (20). Dependency ratios measure the relative burden on the work force of increasing dependent segments of the population, and are an important factor in calculating future health care needs. The total dependency ratio measures the size of total dependent population (those 0 - 1 9 years plus those over 65 years) relative to the size of the working population, and the old age dependency ratio gives an index of the generational balance between retirement age and working age (17,18). In the year 2031 the dependency ratio for the 65+ group is expected to be greater than the dependency ratio for the young, the 0-19 group. The total dependency ratio wi l l gradually decrease from the present ratio until the year 2011, and then i t wi l l slowly rise again (21). For the G.V.R.D. the old age dependency ratio in 1976 was 18.1$, in 1981 i t was 19.1$, and i t is projected to increase to 19.8$ by 1986. The highest ratio among individual municipalities withing the G.V.R.D. was 30.1$ for New Westminster in 1981. - 11 -Another factor which must be included in the new health care equation is the ratio of elderly women to elderly men, which has increased substantially since the beginning of the century. In 1901 there were 1050 men to every 1000 women, while in the 1976 Census there were only 777 men to every 1000 women. The greater increase in l i f e expectancy among women and the unequal sex distribution of immigrants between the years 1931 and 1948 account for the change in this rat io. Almost half of a l l elderly women are widowed while widowers account for 15$ of the elderly men. Only 39$ of women are married and live with their spouses, but 74$ of the men fa l l into this category. The sex ratio of elderly persons has important implications for planning f a c i l i t i e s . The proportions of beds allocated to each sex wi l l affect the respective length of time women and men wi l l have to wait for a vacant bed. Changes in the structural composition of the population affect both demand for health care services and the productive capacity of the economy. The relative burden of health sector costs on the total economy has an affect on public planning for future levels of service provision. Perhaps there is a cost-threshold beyond which public support for the current direction of costly institutional emphasis of care for the aged wi l l wane. Beyond this threshold, i t may be necessary to consider more innovative types of care. There are estimates which calculate the cost of caring for the elderly at three times the cost of caring for the young (22). It may become - 12 -necessary to encourage changes in attitudes towards the elderly and to foster an emphasis on family and community responsibility in order to reduce the expectation that a l l support should be derived from government programs. Somehow, the value system of the HEALTH CARE ORGANIZATION wi l l have to be revised so that the care of the chronically i l l wi l l be seen as equally rewarding as the cure of acute conditions. The need for this revision of the value system is already pressing and wil l become more so as the percentage of the aged in Canada's population increases (23). 2. Approaches to Meeting the Problems of the Elderly The current emphasis in Canada on institutional care for our elderly citizens and the future projected ut i l iza t ion patterns of the elderly population have enormous cost implications. The historical focus on health services as provider of care for the elderly may not be entirely appropriate for the growing numbers of elderly in most industrialized nations. Perhaps long term care for the aged is a social problem of which health is but one component (18). A. Development of Long Term Care Programs in Four Industrialized Nations The following is a brief description of the development of health services for the elderly, often as a component of the provision of social security programs. - 13 -i ) Great Bri tai n: The elderly of Great Britain make up 13.5$ of the entire population, and comprise a substantial proportion of the needy (24). The Bri t i sh have his tor ical ly had a commitment to provide social and health services within the community, with institutional care as a last resort only. There is an inherent belief in Britain that the elderly are happiest in their own homes in the familiar community where they have their roots. There has always been a shortage of chronic care beds, but conviction persists that care in the home is less costly than care in f ac i l i t i e s (although this contention has never been unequivocally established). The development of a National Health Service in 1946 occurred as a t r ipar t i te orgranization of General Health Services, Hospital Services, and Local Health Authority Services. It is noteworthy that the hospital-based specialists developed strong hospital-based programs with emphasis on the use of high-level technology in diagnostic assessments and supported by a well developed home-care program in the community. However, under the original National Health Service the link between these two services and those elderly individuals who had l i t t l e rehabilitative potential and could not be cared for in the home, i . e . , those who needed more custodia I-type long term care, was very weak. Under the 1974 reorganization of the National Health Service, the three divisions were united under one authority, and - 14 -planning for an organization of services took place at a local d i s t r i c t level with the involvement of a l l health professionals. The National Health Service has the highest degree of central planning of any health service in the industrialized world. However, the major di f f icul ty in this system has been cost-containment, which can only be solved by restricting access to the system (25). The dominance of the geriatrician in the system means that the services provided for the elderly seem very medically-oriented. The Bri t ish system has queuing as a way of l i f e , with a tradition of everyone patiently waiting for one's turn for its limited resources. This is not generally acceptable in North America. i i) Sweden: Sweden has been able to concentrate on domestic social programs in the 170 year absence of war. But with universal old age pension and vir tual ly free medical care, the almost 14$ of the population who are elderly constitute a heavy tax burden (26). Health services'are administered locally through county councils which are responsible by law to provide health care for their population (27). Nursing home care has doubled between 1960 and 1972, while acute care beds have only increased 10$ in the same time period. The country has 20 geriatric ward beds in acute care hospitals per 100,000 population, mainly for diagnostic purposes. These comprise 10$ of a l l - 15 -Long Term Care beds. Active rehabilitation nursing home beds make up 30$ of the total long term care bed complement and the remaining 60$ are in chronic maintenance nursing homes (26). In spite of very generous long term care resource allocation in this country, waiting l i s t s for f ac i l i t y placements are long. The Swedish experience is very similar to the situation in the 6.V.R.D.; 30$ of the wait listed clients occupy Acute Care beds, mainly in medicaI/surgicaI wards, 34$ are in their own homes and the rest are in other f a c i l i t i e s . Fully 20$ were inappropriately placed in a long term care f ac i l i t y and i t was estimated that 3.5$ could be discharged home. The lengths of stay distribution in this chronic care system showed that 20$ of the clients stayed 10-19 years, 11$ stayed 5-9 years, 29$ stayed 3-4 years, and 58$ less than 2 years. i i i ) Un ited States The depression of the 1930's was a terrifying shock that altered the American view of social security and welfare somewhat, since there had never been a unified social-welfare tradition in the United States. "This economic c r i s i s had produced people in need despite industry and individual in i t i a t ive . " (24) The New Deal planners envisioned the assumption of responsibility for public welfare by government. Thus, the Social Security Act of 1935 established a precedent for future Medicare legislation. Health care - 16 -for the aged in America was defined as financial assistance in meeting the costs of medical services delivered under existing arrangements but did not address alternative means of caring for the aged. The 1965 Medicare and Medicaid amendments to the Social Security Act provided a compromise between those who wanted less government interference in health care and those who wanted wider population coverage. Medicaid requires a means test and therefore follows a social welfare tradit ion, with services completely free of charge. Medicare, in contrast, is a universal program which requires the beneficiary, i . e . , anyone over the age of 65, to part ial ly pay for the services. Prior to this amendment there was l i t t l e change in the ut i l iza t ion rate of health resources by the elderly although since 1935 the proportion of elderly in the population had grown substantially. Perhaps one explanation for the failure of ut i l izat ion rates to match the growth in the elderly population was that the trends of health care were towards services not appropriate for the elderly population and often located so as to minimize accessibi l i ty . At the same time, the cost of services was growing rapidly (28). Medicare has had the effect of increasing and expanding the acute care capacity of hospitals. Nursing home care is not covered unless i t is a Skilled Nursing Faci l i ty and then only for the limited time specified by the Act. Instead, acute hospital stays have been encouraged. - 17 -iv) The Canadian Situation: Daniel Baum characterizes the Canadian way as consisting of more institutions, and he describes the Canadian attitude towards the eIderly in this way: In a very real sense, they are encapsulated and warehoused for death. They are removed from the community, and the community accordingly does not have to see either old age or death (29). Canada has, as mentioned earl ier , a higher rate of insti tutionalizing its elderly than almost any other industrialized country. One of the most important explanations for this is the bias in the payment mechanism of our health insurance scheme (20). Home care services were not insured s imu I taneou I sy with hospital care as was done, for example, in Br i ta in . Health professionals also tend to foster inst i tut ionalization. Additional factors favouring institutionalization of the elderly include severe climate and rural geography. Great distances and lack of health services in rural areas, coupled with almost non-existent transportation systems, necessitated hospitalization of the aged, particularly in winter time (30). Canada has also had, h is tor ica l ly , a general tendency to institutionalize a l l its "deviants". - 18 -B. Sociology of Institutionalization Popular attitudes towards institutionalization have been heavily influenced by Erwin Goffmans' theory of the 'sociology of total ins t i tu t ions ' , based mainly on data gathered from mental institutions. The basic tenet of the theory is the pathology of bureaucracy of organization, whereby . . . the institution actively participates in reducing the resident to a total lack of power. This ultimately results in iatrogenic diseases of institutional l i fe ; dependency, depersonalization, and lack of self-esteem . . . and eventually the disruption of the individual's personal economy of action (31). However, theories concerning insti tutionalizing the elderly should perhaps start with the non-institutionaIized elderly, who often experience poverty, isolation, and physical d i sab i l i ty . If one compares the mentally disabled institutionalized to the healthy, middle-class, non-institutionalized elderly popuI at ion, • one arrives at different conclusions than would be obtained from a comparison with the mentally disabled elderly who are not hospitalized. Perhaps the institutionalization of the elderly does not represent such a radical disruption of their previous l i festyle and consumption pattern as has been assumed. Perhaps the dominance by staff and subordination of residents in institutions only reflect their social relationships in the larger society, where the elderly have become a - 19 -proletariat and a dependent group, who, like children, have very l i t t l e opportunity to participate in society and influence their own condition. In a secondary study of the data collected by the 'Aging in Manitoba' study team, i t was found that the most important predictors of perceived well-being were: perceived health, autonomy in choosing residence, contact with close friends, and perceived future economic well-being (32). Institutions for the elderly are rarely designed to meet the needs in these areas. During the early period of industrialization, the policy with respect to the elderly was to discourage dependency on the public payroll , and the 'poor-houses' were made as unpleasant as possible to make individuals volunteer for the labour force. Post World War II policy, on the other hand, has been one of re l ief and social insurance, and the med i ca I i zat ion of institutions has made public assistance to the elderly acceptable to the middle-class. Institutional care of elderly parents has become part of the bureaucratic organization of the middle-class l i fe cycle, so that affluent children can shift the responsibility of care for their aged and ai l ing parents to the state (32). These days i t is in vogue to c r i t i c i z e the institutionalization of any group. Such cr i t ic ism usually attacks the very nature of institutional l i fe i tself rather than the 'quality of care' in institutions. Even governments have been eager to fuel such opinions in their attempts at swaying the public towards the less costly, non-institutional care for those in need. - 20 -Projections of future institutional u t i l iza t ion patterns by the elderly have reached ch i l l ing conclusions. As noted ear l ier , the over 65 group is estimated to account for 50$ increase in the number of patient days by 1986 and a doubling by 2001 (2). The proportion of total patient days represented by the elderly was 35$ in 1971 and i t is estimated that i t wi l l account for 39$ in 1986 and 43$ in 2001. If current trends continue, at the turn of the century, approximately half of a l l health dollars wi l l be consumed by the aged, due mostly to increases in institutional costs. A series of simulations were run to test outcomes in terms of costs as a fraction of total output and cost-per-capita in a complete economic-demographic system (33). Canadian hospital services data for 1969 were used together with Ontario data on physician services for 1971 in predicting various cost-outcomes as a result of different population changes. The total health care function was described as: H+= Where: H = total health care expenditure h = health care cost per capita N = population f = fe r t i I i ty factor i = sex j = age t = time Several simulations were conducted based on different input assumptions, such as changes in f e r t i l i t y rates and migration patterns. High f e r t i l i t y rates result in a larger proportion of young people, the resulting overall higher dependency ratio means that health care costs account for a larger fraction of the total output. A low f e r t i l i t y pattern results in a higher proportion of old persons in the long run with a slight increase in the fraction of total output allocated to health. The low f e r t i l i t y rates of 1969 also result in more than a 10$ increase per capita costs of health care. Net migration equal to 1$ of the domestic population results in a fa l l in average age and a rise in dependency ratio. The long term effect is to increase the fraction of total output alloted to health care and to decrease per capita health care costs. The pattern which emerges from these simulations suggests that large increases in health care costs over a single decade are more l ikely to be the result of changes in quality of services provided and service mix, rather than a result of population changes. However, changes in the population structure do affect costs of health services and changes in f e r t i l i t y rates have more profound effects than changes in migration rates. C. Current Issues in Geriatric Care Provision (i) A Problem: Acute Care Bed-Blocking: One of the most high-profiled problems of institutionalization involves 'bed-blocking' by elderly long-stay patients in acute care - 22 -wards (34). When members of the public are informed that new admissions to hospitals are delayed or elective surgery cancelled because of bed-blocking by long-stay patients, they perceive this as a real threat to their own health (35). Health professionals practicing in the acute care setting see such bed-blocking as a waste of acute care resources and not as a rewarding care experience. Long waiting times and long waiting l i s t s for admissions to Extended Care beds also contribute to the enormous pressure for more Extended Care hospital construction to reduce the burden of the long-stay patient on the acute care hospital. In Manitoba, this problem has been studied extensively for Metro Winnipeg acute care hospitals (36). The Winnipeg area experienced a major expansion of long term care f ac i l i t i e s in the 1970's, with 200 new beds opened during a five-year period. Insured home care programs were also expanded. Yet, acute care hospital u t i l iza t ion by the elderly, particularly the over-75 group, increased during this period. The increase was particularly marked in the very-long stays, i e . , the 90+ day stays, and was not due to any changes in illness mix or multiple pathologies as the samples were standardized on the Laspeyres-type case mix index (37). The authors concluded that the major factor in causing this acute care back-up was the transfer process of patients to nursing home. There is also concern about the fate of these 'bed-blocking' patients in holding wards, where they may deteriorate both mentally - 23 -and physically due to lack of proper rehabilitative programming. The negative attitudes of hospital personnel may further damage their fragile self-esteem and leave them lonely and isolated (36). The Hospital Programs Division continuously monitors long-stay patients in acute care beds in a l l hospitals in Bri t ish Columbia. During 1980, approximately 11$ of a l l acute beds in the G.V.R.D. were occupied by patients no longer in need of acute treatment (38). The majority, 60$, were assessed as Extended Care e l ig ib le . Other surveys during 1980 found the levels to be 13.5$ (39), and 16$ (40). A survey conducted by the G.V.R.D. in September, 1980, found that, on average, Long Term Care patients occupied Acute Care beds for 70.7 days, again with patients who were Extended Care e l ig ib le staying the longest, approximatIey 80.1 days (16). The various approaches which have been suggested, and sometimes attempted, to alleviate this situation are discussed in Chapter V. ( i i ) A Solution: Day Hospital: Future hospital u t i l izat ion predictions and corresponding cost-projections have demonstrated the need to re-examine the present emphasis on institutional care, and to change the orientation of the Canadian health care system away from hospitals as primary care givers for the elderly. Many different approaches have been postulated: Day Care and Day Hospital services, respite admissions, and preventive care in different forms. - 24 -His tor ical ly , Day Hospital services developed in Britain i n i t i a l l y in the f ie ld of psychiatry and later in geriatrics (41-43). This type of hospital service is slowly gaining ground on the North American continent and is designed to serve two types of patients: long term care patients who attend as- an alternative to insti tutionalization, and short term patients on acute care hospital replacement programs (44,45). The major function of a Day Hospital is to maintain the level of functioning of the elderly population and thereby delay, or even prevent, future hospitalization, as well as prolong their stay in their own home or reduce the number of days spent in acute care hospitals (46-48). One of the most successful Senior Day Health Centres in the United States is the demonstration project On Lok in San Francisco's Chinatown. On Lok provides the usual array of hospital services as well as an Outreach program. The clientele represents a very narrow medical spectrum; the Centre does not accept clients who are totaly non-ambulatory or exhibit behavioural problems, nor clients who can function on their own (49). Much debate has been centred on the methodologies of cost comparisons between Day Hospitals and inpatient nursing homes (50-52). One approach to the problem would be to randomly assign Day Care applicants to treatment and no-treatment groups and later record dates of hospital admission and death. It might be expected that the treated group would have more years of l i fe but fewer years of - 25 -hospitalized l i f e , on average (53). Such a study design has been ut i l ized in the National Centre for Health Services Research (54). The results of these studies wi l l not, however, become available for several years. Locally, there are two Day Hospitals in operation at the present time. The Banfield Pavilion Day Hospital opened on March 18, 1980, and has a Day Hospital component of 20 spaces (15 on-going and 5 early assessments) and an Outreach program, while the inpatient function is coordinated with the Banfield inpatient unit. Mount Saint Joseph has a l l three components and opened its Day Care function with 15 spaces October, 1979, and an inpatient capacity of 20 beds in January, 1980. It also has an Outreach program. The e l i g i b i l i t y requirements are very s t r i c t at this unit, which wi l l not admit anyone who does not exhibit psychological disturbances. D. Methods Used to Project Future Bed Requirements (i) Assessment of need: The needs of a segment of the population are often defined in terms of resources already present, and planning for future services becomes a simple extrapolation exercise from present u t i l iza t ion rates to parallel projected population increases. Large scale epidemiological surveys of a sample of the target population are - 26 -costly and time-consuming, so that rational planning must be attempted based on use of existing data (55,56). Analysis of the characteristics of applicants to Long Term Care f ac i l i t i e s in Kingston, Ontario (57), reveals that these elderly to fa l l into the following categories: - 15$ to special 'demented' homes - 18$ to Extended Care hospitals - 23$ to Intermediate Care f ac i l i t i e s - 11$ to Personal Care homes - 33$ to Home Care services The application rate was 2.7$ of the over-65 population. However, the actual admission rate was considerably lower. Areas of expressed need among elderly persons include transporta-t ion, housing, health care, and home care. However, these needs are usually described by the staff and professionals who often come from different socio-economic strata than their clients and tend to name the services they are responsible for as the most important to their elderly cl ients , as well as to overestimate the need for these services (58). An extensive field survey of the elderly population and the resources available to meet them was undertaken in 1971 in Manitoba (59). Needs of the aged were assessed in nine areas on a one-to-five point scale designating highest and lowest need intensity level. From this survey instrument a need profile was constructed and a mirror - 27 -profile constructed of the resources' ab i l i ty to meet these needs (60,62). The survey attempted to relate these two in such a way as to enable identification of discrepencies, overlaps, and alternatives at local , regional, and provincial levels. Both f ac i l i t y populations and those living independently at home were sampled. A profile techique was developed to produce a graphical display of histograms based on mean responses. The needs profile was fitted with the resource profile at comparable aggregate levels and the degree of f i t was determined. The survey instrument solicited individual responses as to perceived degrees of need rather than attempting to establish an objective measure of need, or functional level as is possible with the Bri t ish Columbia assessment instrument LTC 1 (14). ( i i ) Linear Extropolat ion Methods: In order to allow for the variation in the use of health services by the elderly, an attempt was made to derive an age/sex related formula for a more accurate calculation of geriatric bed requirements (63). The calculations were based on the assumption that only those who require hospitalization were waitlisted and subsequently admitted: i e . , that the demand accurately reflected medical need in the Dis t r i c t . The number of inpatients and those on waiting l i s t s were classified by sex and age groups (65-74, 75-84, 85+), and a bed rate - 28 -per 1000 population at-risk in each sex/age group was calculated (number of inpatients plus waitlisted applicants). The projected population increases for each group were multiplied by the bed rate and summed, and a total projected bed rate was calculated for each year projected. It is noteworthy that this formula for estimating bed requirement does not take into account either length of stay in the hospital or length of wait. The formula is based entirely on present ut i l iza t ion patterns. a) Toronto Formula: Based on a cross-sectional survey of a l l Long Term Care f ac i l i t i e s in the Metro Toronto Region, the Hospital Council proposed a method for calculating future geriatric bed requirements (64). This formula was based on the aggregated age group of the over-65 years of age. Waiting l i s t data were not available, so i t was assumed that 10$ of a l l patients waitlisted resided at home while 90$ were inappropriately placed patients in various institutions and thus waitlisted for transfer. Calculation of projected demand was based on participation rates and adjusted demand. Demand = participation rate x population projection Adjusted Demand Participation Rate = Catchment Area Population - 29 -Adjusted Current Inappropri- Current Patients Demand = Number of - ately Placed + Patients + at Home Inpatients Patients to be Transferred (calculated for each level of care). Again, length of stay and length of wait are not considered in the calculation, and the estimate is based on actual u t i l i za t ion . Ottawa Formula: Yet another study of needs for long term health services for the elderly, this time in the Ottawa-Car Ieton area (65,66), produced recommendations based on very inadequate data. Projected u t i l iza t ion for various types of Long Term Care beds was calculated as: T 0 beds X 1 + J] population - To population Tg population Where: TQ = the current year T-| = the projected year The projected ut i l iza t ion was then compared to the Provincial guidelines for bed allocations and recommendations were made according to resulting surplus or shortage (67). This method is particularly surprising in view of the fact that this study group had available to them data on number of applicants waitlisted and length of wait. Data were available by sex and level of care. It is typical for a l l planning of geriatric long term health - 30 -services to be based on simple ut i l iza t ion data, sometimes added to wai t l i s t data, and at best partitioned by sex and finer age groups (63). Bed requirements are then projected as a bed rate per 1000 population over 65 year of age. In 1975, a Swedish survey of a l l Long Term Care f ac i l i t i e s provided exhaustive information on every possible topic, including sex/age distribution, length of stay in institutions, number of applicants waitlisted and place of residence of waitlisted c l ients . Length of wait was not available (27). However, when recommendations were made, bed requirements were not calculated on the basis of length of stay (and length of wait); neither was age-distribution or sex considered in the planning formula. Manitoba Method: The field survey of the elderly population in Manitoba in 1971 produced rather different and very interesting s tat is t ics (60-62). The survey sampled both the general population of elderly persons and those l iving in residential f a c i l i t i e s . The following information characterizes the functional needs of the aged in the Metro Winnipeg area: - 31 -Genera I Population Functional Need $ FaciIity Population $ 1.3 8.4 0.4 3.9 8.5 3.3 5.5 needed help getting in and out of bed needed help getting out of doors needed help with feeding needed help with washing needed help cutting toe-nails needed help with taking medication received nursing care $ 18.0 26.0 8.7 39.1 59.4 46.0 47.4 Furthermore, in a typical month in 1971, of those 65 years of age and over: - 9.5$ lived in a f ac i l i ty - 17.4$ attended a club - 4.0$ received service from an organization 9.1$ received service from government health agency - 4.1$ received service from non-government health agency - 10.1$ received service from government social agency - 2.8$ received service from non-government social agency - 3.2$ received service from 'other' agency (eg., recreational, educationaI). Metro Winnipeg also had s ta t is t ics on number of persons waitlisted for each level of care and average estimate of length of wait for f ac i l i t y placement, as well as degree of inappropriate placement in acute beds (68,69). Although length of stay and length of wait s ta t is t ics are not incorporated in planning bed requirements, the survey data gives a good data base for estimating level of need for future services. The provincial guidelines for bed allocations are 90 'Personal Care Home' beds per 1000 population 70 years and over, plus 1.2 Extended Treatment and Rehabilitation beds per 1000 general population. It is recognized that these suggested provincial bed rates may not be equally applicable in urban and rural areas. - 32 -Bri t ish Columbia - Long Term Care Formula: The suggested bed allocation guidelines for geriatric health services for Bri t i sh Columbia take into consideration the differential use of health services by the very old age group, particularly those aged 85+ (70). The proportion of the aged assessed to be in need of services, partly based on a survey of Long Term Care Admissions in one urban and one rural Health Dis t r ic t (71), were as follows: - 5$ of the 65 - 74 group - 20$ of the 75 - 84 group - 50$ of the 85 + group The fraction of the population in each group who would need care would then be: - 5% of the 65 - 74 pop. = X - 20$ of the 75 - 84 pop. = Y - 50$ of the 85 + pop. = Z Total 65 + needing care = W Of these (W) fully 50$ could manage with the aid of Home Support, 30$ would need institutional care at Intermediate Level, and 20$ at Extended Care Level. Therefore, the calculation of required health services for the over-65 group is : Home Support : 50$ x W = a$ of the 65+ Intermediate Care : 30$ x W = b$ of the 65+ Extended Care : 20$ x W = c$ of the 65+ Total proportion needing care = d$ of the 65+ ( i e . , 'W') - 33 -Note that this does not give a breakdown of proportion in each level for the finer age-groups. The overall percentages arrived at by this calculation were 6.8$ of the 65+ age requiring Home Support, 4$ requiring Intermediate Care placement, and 2.7$ requiring Extended Care admission. Br i t i sh Columbia - Hospital Programs Division Formula: Hospital Programs Research Division estimates future Extended Care bed requirements by calculating the number of beds needed on the basis of applicants waitlisted, number of admissions per month, and the proportion of refusals from the waiting l i s t (72). Number on Waiting Lis t = A % refusals from this l i s t = B Number of waitlisted patients admitted = C Number of admissions per month = D Length of 'suitable' wait (months) = X Number of beds required immediately = Z C = A - B and Z = C - XD Therefore, total number of beds required are: Total Beds = current bed complement + Z From this a bed rate per 1000 65+ age population is calculated and applied to projected future population increases to give the estimated number of Extended Care Beds needed. The following is an example with actual numbers: - 34 -A = waiting l i s t in November 1979 B = % refusals = 30$ C = number of admitted waitlisted patients D = number of admissions per month X = length of suitable wait = 3 months The number of beds required immediately: Z = C - XD = 1291 - 387 - 3 x 75 = 679 The current bed complement was : 2446 immediately required : 679 Total beds needed : 3125 Population 65+ (1979) = 122,465 3125 Bed rate = x 1,000 = 25.5 per 1,000 65+ population 122,465 The 1986 population projection used in the calculation was: 65+ = 140,627 multipled by the 25.5 per 1,000 bed rate = 3586 total beds required. By 1979, i t was assumed the G.V.R.D. would need a total of 3586 Extended Care beds, of which only 2446 already exist, and by subtract ion,therefore, 1140 new beds would be required . = 1291 = 387 = 904 75 Summary of Linear Methods Limitations: Without employing the more complex simulation techniques, forecasting future bed requirements can only incorporate a few static variables such as number of patients occupying existing beds, number of patients on waiting l i s t s , and number of projected elderly in the population at a future point in time. Not included in linear-type forecasting methods are the dynamic variables describing the flow in and out of the system including - 35 -also the transfer across the system. Such variables would include length of wait, application rate, length of stay, and discharge rate, as well as length of stay of transfer patients. In order to incorporate such variables and to describe their complex interrelationships, more sophisticated forecasting techniques are required. Simulation modeling makes i t possible to describe these intricate relationships and to project future outcomes from complex systems with large numbers of variables. (iii)SimuIation Techniques: A systematic approach to planning in the health care field is more easily made possible through applying the methodologies of systems analysis. Mathematical modeling can thus forecast demands on the system and the resources available as well as the resulting shortages and social impact of such shortages. A multi-purpose simulation project in the Greater Vancouver Regional Dis t r ic t brought together many seemingly unrelated fields (73). The Health Group constituted one component of this macro-modeling group. Three directions for modeling were evident: a) Qualitative modeling: This technique describes the effect of various health pol icies . •Ten variables were interrelated in pairs in a Delphi-like manner. The results from such simulations showed the health care system to be an unstable one. - 36 -b) Descriptive modeling: This technique describes the existing system and provides a useful technique in short-run planning. The variables here were based on historical patterns of referral , diagnostic and therapeutic behaviours of physicians. c) Prescriptive modeling: This technique considers prescribed norms for regulating health care resources required to treat certain illnesses. The model is interactive and allows priorization of patient treatments and some resource substitutions. The maximum time-frame for a descriptive model is approximately ten years, as major technological discoveries and changes in c l in ica l practice and social behaviour may take place (74). It is also important here to choose the optimum time-scale for the model. For example, a year is a convenient time span from an avaiIabiIity-of-data perspective, but i t is too long to reflect immediate effects of resource shortages. A more suitable time-frame of a week to a month, which would be more representative of time delays between service requests and treatments, is unrealistic as no data are available. The prescriptive model is based on four sub-system components: Disease Generator Pr ior i ty Streaming Resource AI location for Treatment Evaluation of System Shortages (75) - 37 -Demand for health services is greatly influenced by the awareness of their ava i lab i l i ty . However, u t i l iza t ion may be distorted by an existing shortage in f ac i l i t i e s causing back-logs and long waiting periods. The data requirements for this type of model i ncIude: - population projections by age and sex - disease incidence rates (aggregated disease categories) - pr iori ty distribution of treatement of each category - health resources (physician types, nurses, beds) The specific data items included in this model for estimation of bed requirements were: rates of hospitalization per 100,000 population, and average length of stay and incidence rates per 100,000 population. The number of days per incident for each disease category was derived. The number of bed days available per year was calculated by multiplying the category. If a resource shortage exists, substitution of alternative resources takes place. If this is not permissible, a higher priority is assigned in the next time period. Pr ior i ty assignment ranks disease categories and is based on a probability distribution for each disease, so that X% are of class 1, y% of class 2, Z% of class 3, W$ of class 4. Untreated cases either take on a higher priori ty in the next time period, or they result in a lower priori ty if death or spontaneous recovery takes place (76). - 38 -Evaluation of the performance of the health system as modelled suffers from lack of a relevant measure of the outcome of resource deployment. In other words, the costs expended are not related to the value of the benefit accrued to the individual from these expended resources. The prescriptive model was run under four different assumptions of population growths (zero and normal and resources growth (no growth and 2% growth), and in a l l cases the system eventually broke down due to severe resource shortages (75). The zero population growth assumption results in a shift in age structure of the population with the resulting morbidity shift from acute to chronic diseases and a corresponding shift in resource u t i l i za t ion . The decreased f e r t i l i t y rate means decreased obstetrical v i s i t s and decreased paediatric care. The long range effect would be a change in the resource mix of medical speciaIties. The Present Model As health sector problems are becoming increasingly complex, an approach which provides system objectives to the policy-making process becomes increasingly important for problem-solving in the health sector, as i t has been for a long time in the private and industrial sector. Simulation techniques have also been applied in the determination of optimal size of a hospital. Relevant variables for such a - 39 -technique include number of beds, occupancy and average daily census of the hospital (78). In this manner, the optimal number of beds can be calculated in order to achieve maximum occupancy in a given hospital unit, eg., a medical/surgicaI unit (79). Simulation modeling has been ut i l ized to describe various hospital sub-systems such as an outpatient c l i n i c , the c l in ica l laboratory, the surgical unit, and the maternity suite (80). A variety of parameters can be studied in this manner, eg., occupancy rates, admission rates, and varying the lengths of stay. Such modeling techniques are designed to allow the most cost-effective and efficient use of any hospital department. However, modeling exercises on a scale between the macro and the hospital sub-system, have been very scarce. Modeling techniques to determine bed allocation forecasting have not been reported in the literature, at least not for the projection of Chronic Care/Long Term Care bed requirements. The present study is an attempt at describing the Extended Care system with simulation modeling techniques. The model is mostly descriptive in nature. It is a short run forecasting method designed to predict the effects on the length of waiting time of different methods of intervention. The interventions in this situation consist of varying numbers of new Extended Care beds added to the system and different levels of demand, i e . , application rates. - 40 -Any behavioural changes among the target population or their care givers are not included in this model, as no quantitative information is available on this variable. It is however, recognized that additional "supply" of services does generate additional demand and these changes may be considerable. - 41 -CHAPTER I I I METHODS A short and simplified flow diagram of the movement of patients through the Br i t i sh Columbia Extended Care system is given in Figure 1. A more detailed version of this diagram is included in Appendix B. The shorter diagram forms the basis of the simulation model described in this study. The individual steps shown here make up the input to the model and describe the data items and their inter-relationships in the system. The shorter version was chosen as the model on the basis of avai labi l i ty and accuracy of data. However, in some instances, the more detailed data were not of consequence for the simulation model and were therefore omitted. 1. Simulation Model Flow Diagram The definitions and the steps in the flow diagram are: Target Population: The elderly make a greater demand on Extended Care than do younger people (2). The 'very-old' group, ie . , those persons who are over 85 years of age, are the heaviest users of services (16). The Greater Vancouver Regional Dis t r ic t population was therefore partitioned into four groups for purposes of this model: those under 65 years, 65-74 years, 75-84 years, and those over 85 years of age. These finer age groups were also used in the population projections to calculate estimated need/demand for services and to reflect more accurately future bed requ i rements. - 42 -Figure 1. Flow Diagram of Movement of Extended Care Cl ients. - 43 -A recently developed population estimation and projection model (4) which takes very recent migration changes into account was used to project age-specific population increases for the municipalities of the Lower Mainland. The model is of the Cohort-Survival component variety, but is not sex specific. Ut i I i zation rates: Morbidity rates are not available in Bri t i sh Columbia. Ut i l iza t ion data are very distorted substitutes for morbibity data, as long waiting l i s t s exist for Extended Care beds. Uti l izat ion information from the census of Extended Care Units and Private Hospitals in the Lower Mainland provided proportions of clients in each age and sex group. Census information for Extended Care Units was collected by survey questionnaire, and for Private Hospitals from the Client Analysis Report of the Long Term Care Program. Appl ication rates: This flow variable is the main input to the model. This information is available by hospital, annually, but not by age and sex grouping. Age and sex specific application rates are estimated on the basis of the age and sex proportions from ut i l iza t ion census. These application rates were then projected to 1986 according to the estimated population increases for each age group (population projections were not available by sex). For a l l their limitations, the application rates are the best available estimates of demand on the system. Active queue: At the time of application for Extended Care benefits the applicant may indicate if he wishes to be waitlisted on the 'active' or the 'on-hold' l i s t s . In 1979, approximately one third chose the on-hold - 44 -status and very few of these applicants later changed their waiting status to active in order to f inal ly be admitted to an Extended Care Unit. First choice: Al l ac t ive- l i s t applicants give two choices of placement and are waitlisted simultaneously for both hospitals. The wait l is ts are s t r i c t ly chronological, so that either a f i r s t choice or a second choice bed may be offered f i r s t , depending on which hospital has a vacancy. Waitlist data are not available in machine-readable form by age group and sex. Pre-Admission L i s t : When a hospital requests, Hospital Programs Division sends a pre-admission l i s t containing the chronologically most senior names waitlisted for that hospital. The Pre-admission l i s t is composed of both f i r s t choice and second choice l i s t s . Data specific to age and sex were not available for this study. The inflow to the queue consists of a l l those applicants who chose to be waitlisted on the active l i s t plus the small proportion of on-hold apapl icants who later 'activate' their status (approximately four percent of a l l new admissions). The outflow from the queue consists of those applicants who are admitted plus those who 'drop out' for a variety of reasons. A small percentage of applicants {3.1%) are rejected by the hospitals as unsuitable for admission because they may require continuous oxygen or have severe psychiatric symptoms, for example. These patients may either be admitted to another Extended Care Unit which can manage such patients, or they may be transferred to a more appropriate f a c i l i t y , such as an acute care hospital or a psychiatric unit. This group was of l i t t l e consequence - 45 -for the model and was therefore included with those applicants who had moved out of the region in the 'other' category. Non-admitted patients: Of the applicants listed on the pre-admission l i s t , 53$ refuse the bed when i t is offered. There are a number of reasons for th is : - dead: 10.4$ have died during the waiting time. This proportion wi l l increase with waiting time; - non-eligible: 13.9$ are no longer e l ig ib le for Extended Care benefits because their condition has improved. This proportion may also increase with the length of waiting time; - on-hold: 9.2$ choose to go to the on-hold waiting l i s t at this point in time; - second choice: 15.7$ refuse the bed on the grounds that i t was their second choice faci. l i ty, and they prefer to wait for a bed in their f i r s t choice unit; - other: 3.9$ have either moved out of the area or have been rejected by the hospital as unsuitable residents. A l l these data were collected by survey questionnaire from the Extended Care Units in the Lower Mainland. Admissions: On the average, 47$ of the patients listed on the Pre-admission l i s t are eventually admitted to the Extended Care Unit (16). In this group are also included those applicants who are offered a bed in their second choice hospital and who accept this bed. These patients are admitted to their second choice hospital but may choose to transfer when a - 46 -vacancy occurs in their f i r s t choice unit. New admissions account for 74$ and re-admissions for 26$ of a l l admissions for Extended Care Units. The age and sex specific census for Extended Care Units and Private Hospitals as of September 1980 were applied to the population projection to estimate future appl ication rates. Length of stay s ta t is t ics were col lected from a sample of seven Extended Care Units (See Appendix C). Detailed information for patients discharged was gathered to obtain an estimate of the total length of stay as the sum of a l l admissions for each patient. Not included in this sum was the length of stay in another Extended Care Unit for patients who had transferred, but this portion was estimated according to the method outlined in data input. Discharges: Sixty percent of discharges are at time of death. Only one fourth of the live discharges do not return to the same Extended Care Unit. They may go home, to another type of institution or to another Extended Care Unit (usually the f i r s t choice unit) . Therefore, only 70$ of a l l discharges wi l l free up a bed in a particular f a c i l i t y . The remaining 30$ of discharges are to an Acute Care hospital. These patients wi l l most l ikely return to the same Extended Care Unit and wi l l therefore not free a bed. Extended Care Unit patients who are admitted to an Acute Care ward may fa l l into any one of three discharge disposition categories: deceased in Acute Care Unit, improved by treatment and discharged home; re-admitted to the same Extended Care Unit after they have recovered from the acute i l lness . If their length of stay in Acute Care - 47 -was less than ten days, the Extended Care Unit bed wi l l have been reserved and they are re-admitted promptly. If, on the other hand, the acute episode was longer than ten days, the patient wi l l have lost the Extended Care Unit bed and wi l l return to the top of the Pre-Admission Lis t while waiting in the Acute Care ward. The detailed flow through Acute Care is not of consequence for the simulation model and has therefore been omitted. A summary of the pertinent variables, their limitations for this simulation and suggested ideal data formats for future use are listed in Figure 2. - 48 -INPUT VARIABLE LIMITATIONS OF DATA (in this simulation) IDEAL DATA FORMATS Init ial beds Ini t ial queue the number of existing beds is often greater than the number of rated beds and excludes S.T.A. beds. not available regularly, not by age group, sex or location. existing beds = rated beds. monthly data, by age, sex and loca-t ion. Ini t ia l totaI wa i t Length of wait to date Occupancy rate PopuI at ion  Projection Census Applications only annual data, very inaccu-rate, on-hold, transfers. not available. inaccurate - does not account for 48 hr lag time, nor 10 day absences. only by school d i s t r i c t , not by sex. u t i l iza t ion census rather than application census. annual, aggregate, not accurate. Current, by age, sex, and location, date of application. distribution of cur-rent waiting time. i ncIude 10 day absence, 48 hour lag t ime. by mun i c i pa I i ty , by sex. appl ication and uti I ization census by age and sex monthly data by age and sex On-hold status semi-annual, aggregate not accurate. month Iy, by age and sex and date of on-hold status Pre-Admiss ion List Length of stay not accurate, sporadic admission-separation data and not total length continuously avaiI -ab I e total aggregate time for each case, transfer times I nappropr i ate  pIacements not included in this model. model based on appl-ication rate input NOTE: The underlined variables are the most commonly used data for projecting future bed requirements in other studies reported. Figure 2. Input Variables, Their Limitations and Suggested Ideal Format for Use in the Simulation Model. - 49 -2. Components The FORTRAN simulation used in this study is a dynamic modeling of the inter-relationships of the variables for which data are available and accessible in the Br i t i sh Columbia Extended Care system. The model describes the flow of patients through the system, but does incorporate static variables as well when indicated. The model is an iterative one, in which some of the output variables from one time run become the input variables for the next time period (queue length, number of beds, length of wait . The time-slice chosen in this model is one month; but most data were in annual format, so the model is rather a pseudo-monthly model (data were simply divided by 12). The basic blocks of the model are: - Number of Extended Care Unit Beds - Waiting Periods - Application Rates - Length of Stay - Population Estimates The model allows an estimation of changes in admission rates as the length of wait changes and therefore the proportion of patients who are admitted from the Pre-Admission List changes. On the other hand, the model does not allow for estimation of changes in the length of stay in an Extended Care Unit as a function of changes in waiting times and proportion of patients - 50 -Two possible situations are incorporated into the model: Case 1 - the time required to exhaust the queue is greater than the time-slice of the Simulation Model (in this case, one month), and Case 2 - the time required to exhaust the queue is less than the t i e-sl ice. Both situations can have either an increasing or a decreasing queue. A) Input Data - data* included are: « Ini t ial beds: The number of Extended Care Unit beds was 2466 for the G.V.R.D. in September 1980. The data were collected from the Extended Care Units by survey questionnaire to include only those beds which were used full-time, for regular admissions. An additional 40 beds came on-stream while this model was being developed to give a total number i n i t i a l l y of 2506 Extended Care Unit beds in the d i s t r i c t . Added beds: The number of new beds added to the total complement varied with each experimental run as specified in (3) Options. New active applicants : The number of new applicants added to the active waiting l i s t formed the input variable for a simulation. The number is based on two-thirds of a l l applicants being listed on the active l i s t directly and is projected according to population increases for each age group. In i t i a l l y , there were 139 per month, on the average, in 1979. * The indicated items (*) are data which i t was necessary to compute or est imate. Calculation of the number of new appIicants per month was based on the percentage of u t i l iza t ion by each age group of Extended Care and Private Hospital beds. A coefficient was calculated from the 1979 application rate data (Hospital Programs Division) and this coefficient was multiplied by the projected population in each age group and summed to give a total application rate for each projected year. 'Activated' on-hold applicants: This small proportion (4% of a l l new admissions, N=^ was added to the above. InfIow: The total inflow to the queue is therefore the sum of the new applicants and 'activated' on-hold applicants. The infIow to the queue was calculated as: INFLO = NEWAPP + HOLD Where NEWAPP = new applicants per month HOLD = applicants activated from on-hold waiting l i s t Waiting I is t : The average number of applicants i n i t i a l l y on this l i s t (N = 1186) was supplied by Hospital Programs Division. Waiting time to date*: This time period was calculated from data collected by survey questionnaire regarding patients admitted to the units. This waiting time was arbi t rar i ly set at half of total wait, and the value of the total wait was 9 months; therefore, wait-to-date was 4.5 months. Expected Length of Stay*: The expected length of stay (ELOS) was estimated from data collected from the units. It was estimated by adding weighted proportions of time for each number of v i s i t s according to the following formula. - 52 -ELOS = A + rB + r z C + r JD + T Where: A LOS for f i r s t v i s i t B LOS for second v i s i t C LOS for third v i s i t D LOS for fourth and later v i s i t s r proportional coefficient (.3537) r is the likelihood that a patient who has spent ' X ' time during one v i s i t wi l l return for another v i s i t . This coefficient was calculated to be .3537 for Extended Care v i s i t s ie . , of the patients who had one v i s i t , 36.8$ returned for a second v i s i t . Of these, 32.6$ came for a third v i s i t and 36.7$ of the latter came for a fourth v i s i t or more v i s i t . The overall coefficient r was therefore estimated to be .3537. And T = number of patients who transfer (5$ of a l l new admissions) was calculated as: The overall ELOS was estimated to be 30.3 months (ie. ELOS = 761.8 + .3537 x 310.3 + (.3537)2 x 187.9 + (.3537)3 x 179.2 + (Z^lii) x .05 = 922 days L 0 S 1 s t v i s i t L0ST = x .05 2 2 Discharges*: The number of 'permanent' discharges (Dx) per month was derived from occupancy rates, ELOS and the number of beds available in any given year, and was calculated to be 79.8 discharges per month. - 53 -The number of discharges per month was calculated as follows: average number of beds in the year x occupancy rate Dx = /12 = 79.8 ELOS 12 New admissions: This number must be the same as the number of 'permanent' discharges per month from above as a l l beds are occupied; i t was calculated in the same manner as the number of discharges. Non-admitted applications*: The proportion of patients from the pre-admission l i s t who are not admitted includes 10.4$ who die and 13.9$ who are ineligible (a further 9.2$ go to the on-hold l i s t and 3.9$ are rejected, for a total of 37.4$ - but the 15.7$ who prefer their f i r s t choice hospital are not removed from the queue). The proportion of adjusted admissions, the variable ZZ, which varies with the length of waiting time, was calculated as follows: 10.4$ = .01155 die for every month of waiting 9 months 13.9$ = .01544 become e l ig ib le for every month of waiting 9 months so that the number of ineligible applicants multiplied by the total length of wait is ZZ = 2 Y (.01155 + . 01544). - 54 -The proportion of non-admitted applicants taken off the I ist each month in constant proportion is: v v _(ZZ + .092 + .039) XX This is the total proportion of those who are taken off the l i s t in constant proportions regardless of the length of wait, i e . , 9.2$ on-holds + 3.9$ rejected. Therefore, the total outflow from the queue becomes: OTFLO = Admissions (1.0+ XX) (47$ are admitted) Calculations of rates of movements included the monthly flow of patients and average flow of patients through the queue: MOFLO = INFLO - OTFLO AVFLO = I N F L ° \ ° T F L ° Calculations of partial waiting times for the intermediate variables B, C, D, XAVG, G, F, and INITW are included under (B) Output Data below, in relation to the final output variables to better i l lustrate the rationale for each variable. B. Output Data 'Final 1 number of beds: For each time run the number of i n i t i a l beds for that run plus the number of beds added during the run of the model - 55 -equals the number of current beds at the end of the run. This ' f i n a l ' number of beds becomes the ' i n i t i a l ' number of beds for the next time period of the model. The same appl ies to the 1 f i naI' queue Ienqth for each time period: FlNQ = INITQ + MOFLO Exhaust time (B) is the time required, B, to exhaust or deplete the total queue D_ INITQ D-OTFLO Two cases are possible; Case 1, when the time, B, required to exhaust the queue is greater than the time interval of the model and CASE 2, when the time required to deplete the queue is less than the time i ntervaI. CASE 1: (B > 1) - Exhaust time (B) is greater than one month Waiting time: The average waiting time for those who left the queue during the month is LEDFTW and is two times the average wait-to-date, Y. LEFTW = 2 Y The average wait for new applicants entering the queue during the month (INFLO) is 0.5 months. Intermediate variables: The average wait of the last applicant in the queue at the beginning of the month is F = 0 and at the end of the month F = F + 1.0. The average wait of the applicant at the top of the queue at the end of the month is G = 2Y, and the average wait of a l l applicants who G + F are st iI I in the queue at the end of the month is XAVG = - 56 -Wait-to-date: The weighted average wait at the beginning of the next month for those applicants who are s t i l l in the queue is: Y = XAVG (INITQ - OTFLO) + INFLO x 0.5 INI TO - OTFLO t INFLO CASE 2: (B < 1) - Exhaust time (B) is less than one month Intermediate variables: The average total length of wait for those applicants who are already in the queue at the beginning of the month but who left the queue during the months is INITW = 2Y (or Y + Y, ie average wait-to-date at the beginning of the month, Y, plus average wait during the month, Y ) . The length of time, part of B, required to f i l l 1-B outflow is c = OTFLO - INITQ INFLO The average wait in the queue for those applicants who entered the queue and left the queue during the same month is: = (1.0 - C + B) 2 Wait-to-date: The average length of wait in the queue for those applicants who entered the queue during the month and remained in the queue at the end of the month is: y = 1.0 - C 2 Total wait: The average length of wait for those applicants who left the queue during the month is: , r rx , , - (D (1-B) x OTFLO) + (INITW x B x OTFLO) OTFLO Table 1 l i s t s the variables of the model and describes the sensi t ivi ty of the model to each of the variables. An example, (C.) with hypothetical numbers is included to i l lustrate the steps in calculating the various waiting times and queue lengths. TABLE 1. SENSITIVITY OF MODEL TO VARIABLES OF INTEREST Variable Sensitivity* Population projection ++ ELOS +++ Beds + Application rate +++ Queue Length ++ Waiting time ++ Holds +++ Rejections + Occupancy rate + + = low sensit ivity ++ = medium sensit ivity +++ = high sensit ivity - 58 -C. Example: (the line numbers referred to are the line numbers of the computer program l is t ing found in Appendix E) Time-slice = 1 month - line 55 Number of i n i t i a l beds = 2000 - line 8 Ini t ial queue length = 1000 applicants - line 31 New applicants = 100 applicants per month - line 44 •Activated' from on-hold = 5 applicants per month - line 45 Expected length of stay in the Extended Care Unit = 20 months -Iine 62 Occupancy rate = 90 percent - line 63 Y (wait-to-date) = 5 months - line 31 2 Y (total wait) = 10 months - line 159 Added beds = 500 (in one month) - line 21 Discharges/month = —*—^2/12 = 112.5 discharges per month 20/12 - Iine 63 New admissions/month = 112.5 (must be the same as the discharges) - Iine 65 ZZ = 2 x 5 (.02255 + .01544) = .2699, i . e . , the proportion of applicants who are e l ig ib le - line 75 XX = ('2699 + .092 + .039) = g 5 3 .47 i . e . , the number of applicants taken from the pre-admission l i s t - I ine 88 - 59 -The outflow from the P . A . L . , OTFLO, is then: OTFLO = Admissions (1.0 + XX) = 112.5 (1.0 + .853) = 208.5 - I ine 89 The INFLO to the queue = 105 i e . , new applicants + holds - line 92 M0FL0 = 105 - 208.5 = -103.5 i e . , inflow minus outflow - line 96 FINQ = INITQ t M0FL0 = 1000 - 103.5 = 896.5 applicants at the end of the simulation - line 100 This example fa l ls into the Case 1 group, as the time required to exhaust the queue is greater than one month. 1000 i e . , B > 1 or B = 2 Q Q 5= 4.80 months - line 114 The average wait for those left the queue during the month is LEFTW = 10 months - Iine 121 AVFLO = 1 0 5 + 2 ° 8 - 5 = 156.8 2 ( ie . , the average flow) - line 127 F = 1 month ( i e . , the wait of applicant who was last in queue at the start of the month now at the end of the month) - line 131 G = 10 months ( i e . , wait of applicant f i r s t in line at the end of the month) - line 134 - 60 -XAVG = — = 5.5 months 2 i . e . , wait of a l l applicants left over - line 140 Y = (5.5 (1000 - 208.5) + 105 x .5) = ^ g m o n + h s 1000 - 208.5 + 105 i . e . , wait at the start of next month for those on the queue -I ine 142 Y (wait-to-date) =4.9 months at the end of the f i r s t month. In the next simulation, LEFTW wi l l be 9.8 months (2 x 4.9), i . e . , the total length of wait before admission to the Extended Care Unit bed. 3. Options A number of options or input assumptions are possible with the model. Different input scenarios wi l l generate different outcomes in terms of queue length and waiting times. The following options were tested in this study: A. the current application rate wi l l remain constant (based on current proportional u t i l iza t ion and current demand). B. a reduced application rate, eg., 10$ lower rate. This situation might arise i f a l l placements were to appropriate f ac i l i t i e s and there were no delays in transfers. - 61 -C. a further reduction in the appl ication rate eg., by 20$, might occur i f alternative services were available and substituted an D. increased application rate, eg., 10$ increase, might occur i f there were a change in behaviour of the elderly and their families so that demand for Extended Care Unit placement increased. E. an increase in the number of 'activated' on-hold waitlisted applicants, might occur if the Private Hospitals gradually close down. F. different time intervals for additions of beds f eg . , a l l new beds would come on-stream at once, or x number of beds per year). 4. Technical Program Specificatin The program was written in FORTRAN. It is 170 lines long. For a typical computer experiment costs are approximately as follows: CPU time used .473 $ .11 CPU stor VMI .316 age-min $ .01 Wait Stor VMI .489 page-hr $ .01 Lines printed 456.0 $ .09 Pages printed 9.0 $ .45 Approximate cost of this run is $ .68 An example of a typical output of this model is enclosed in Appendix D. - 62 -CHAPTER IV RESULTS A series of computer simulations were conducted to describe the relationship between additional beds and reductions in waiting times, and queues. Several 'baseline' simulations were also run to verify the behaviour of the model. The Greater Vancouver Regional Hospital Dis t r ic t Board had already approved 200 additional Extended Care beds for the Region to be distributed as foI Iows: + 40 beds at St. Michael's, May 1981 + 54 beds at Surrey Memorial, September 1982 - 50 beds at Shaughnessy, January 1983 + 125 beds at Lions Gate, September 1983 + 31 beds at Peace Arch Dis t r i c t , March 1984 These 200 additional beds are included in a l l simulations, except where i nd i cated. 1. Baseline Simulations A. The monthly application rate holds constant at 144.1 (1980 rate) and JTO new beds are added to the system. The waiting time remains almost constant at 9.2 months, but the length of the queue increases to 1364 (see table 2). - 63 -B. The monthly application rate remains constant at 144.1 but the 200 currently approved Extended Care beds are added. The waiting time is reduced to 7.4 months and the length of the queue to 1067 clients (Table 2). C. The monthly application rate is projected to increase from 147.8 in 1981 to 163.6 in 1986. The projected application rate increase is calculated proportional to the projected population increase. When JTO additional beds are added, the waiting time increases to 11.2 months and the length of the queue grows to 1983 (Table 2). D. The monthly application rate increases from 147.8 in 1981 to 163.6 in 1986 and the currently approved 200 new Extended Care beds are added. The length of wait increases only sl ightly to 9.6 months and the queue grows to 1643 (Table 2). Table 2 shows that the 200 approved additional beds for the Region wi l l not be sufficient to offset the increased need/demand resulting from projected increases in the elderly target population of 1986. TABLE 2 WAITING TIMES AND QUEUE LENGTHS UNDER BASELINE SIMULATION CONDITIONS Application 1981 Rate 1986 Total Number of Beds Waiting Time (months) Queue Lengths (clients) A 144.1* same 2506 9.2 1,364 B 144.1 same 2706 7.4 1,067 C 147.8 163.6 2506 1 1 .2 1 ,983 D 147.8 163.6 2707 9.6 1,642 * 1980 application rate (Clients per month) - 64 -2. The Firs t Option The increase in monthly application rates each year is proportional to the age-specific population increase. Under this assumption, varying numbers of Extended Care beds are added to the system, from 100 beds to 1,900 beds. The new beds are added at three different time intervals: January 1984, January 1985, and January 1986, in approximately equal numbers. The resulting waiting periods and queue lengths are listed in Table 3. Included in the number of beds added to the system are the 200 currently approved Extended Care beds. An additional 300 beds wi l l maintain the status quo in terms of waiting periods, i e . , 50 new beds per year wi l l adequately offset the increased need generated by a growing elderly population. Reducing the waiting time to 6 months would require an additional 900 beds, with a resulting queue of 938 cl ients . It would be necessary to open at least 2,100 new Extended Care beds by 1986 to completely eliminate the waiting period. - 65 -' TABLE 3 SIMULATED WAITING PERIODS AND QUEUE LENGTHS AT DIFFERENT BED LEVELS* Number of Beds Added** Total Number of Beds Waiting Period (months) Queue Length (cIients) 300 2,806 9.1 1,540 500 3,006 8.2 1,321 700 3,206 7.2 1,124 900 3,406 6.3 938 1100 3,606 5.2 759 1300 3,806 4.2 594 1500 4,006 3.2 443 1700 4,206 2.2 302 1900 4,406 1 .2 171 2100 4,606 0.4 49 * The application rates increased from 147.8 clients per month in 1981 to 163.6 in 1986. ** The 200 approved beds are included in the number of beds added. The changes in waiting times as a function of additional Extended Care beds are described graphically in Figure 3. It can be seen that the model predicts approximately one year lag time between the addition of beds and the effect on the length of wait. Approximately three years lead time is required between the i n i t i a l planning stage and the actual opening of new Extended Care beds. Therefore, approximately four years are required before the waiting time begins to show a reduction in length. 3. The Second Option A reduced monthly application rate may be accomplished by ensuring that a l l placements are to appropriate f ac i l i t i e s for Extended Care Figure 3. Length of Wait at Different Bed Levels (See Table 3) Legend: ± beds indicate the time distribution of the 200 approved beds + indicates time distribution of simulated | bed additions * Total number of beds added - 67 -patients and that there are no delays in transfers of patients to other f ac i l i t i e s when necessary. Such measures could possibly reduce the 'input' to the system by a maximum of 10$. If alternative services were available as substitutes for Extended Care placements, the 'input' might register a maximum reduction of 20$ (an arbi t rar i ly chosen percentage). The f i r s t part of Table 4 shows the effect on waiting periods and queue lengths when the input demand is reduced. A 10$ reduction results in a waiting period of 6.3 months and a queue length of 953 c l ients . A 20$ reduction results in a drastic decrease of both waiting time, to 3.5 months, and queue length, to 464 c l ients . Under both conditions, 300 new beds were added, which would otherwise have maintained status quo waiting period. The effect on waiting time is already evident after two years, as no "construction lag" is present in this option. 4. The Third Option An increased application rate would be the result of a change in the demand behaviour of the target population and its families and care-givers. Increased expectations in the population as a result of well publicized services could potentially have this effect. A 10$ increase in the 'input' rate for Extended Care f ac i l i t y placements would result in longer waiting time, up to 11.7 months, and - 68 -a longer queue of as many as 2,204 cl ients . Again, 300 beds were added to this simulation (Table 4^. 5. The Fourth Option This simulates the effects of Private Hospital closures on the system. If a l l Private Hospital beds were to close over the next six years, the number of 'activated' on-hold waitlisted applicants would increase to 10 per month, plus one such applicant residing at home. Approximately 55$ of a l l 1,300 Private Hospital beds are occupied by Extended Care e l ig ib le c l ients . Table 4 shows that there would be a longer wait, 10.5 months, and longer queue, 1,861 cl ients , as a result. As in previous options, 300 beds were added in this situation. TABLE 4 SIMULATED WAITING PERIODS AND QUEUE LENGTHS AT DIFFERENT APPLICATION RATES Demand Level Application Rate Final Waiting Final Queue 1981 1986 Period (months) Length (clients) -10$ 133.0 147.2 6.3 953 -20$ 118.2 130.9 3.5 464 +10$ 162.6 180.0 11.7. 2,204 + 1 1$ on-holds 158.8* 174.6 10.5 1,861 ^Includes those activated from the on-hold waiting l i s t . The effect of changing the 'input' rate in Options 3, 4, and 5 above on the length of the waiting period is shown graphically in Figure 4. 12 10 h 8 4 h X r-o z 2 + 40 , —/A- 1 — Jan. Jan. 1981 1982 ••a.... " D — — . + 54 -50 +125+30+31 +35 A+10% -*+Holds Control Status Quo •O-10°/O •°-20% +35 beds added _L Jan. 1983 Jan. 1984 Jan. 1985 Jan. 1986 Dec. 1986 T I M E Figure 4. Length of Wait at Different Application Rates and Total Bed Additions Fixed over Time (see Table 4) Legend: ± beds indicate the time distribution of additonal beds .....A indicates a 10$ increase in application rate — •» — —A indicates an increase in activated on-hold applications A indicates control (status quo) condition o indicates a 10$ reduction in application rate O indicates a 20$ reduction in application rate - 70 -6. Different Intervals Final ly , the response of the model to a different 'pulse' of additional beds is included in Table 5. A l l new beds are added at one point in time and the effect is noticeable after a shorter lead time. Three simulations were conducted: 100 new beds, 900 new beds, 1,900 new beds. During a l l three runs, the fixed 200 approved beds were also i ncluded. Table 5 shows that 100 beds added at once (in January 1984) would result in a s l ight ly shorter waiting time, 8.8 months, and a queue length of 1,496 cl ients . An addition of 900 beds in January 1984 would result in a very much shorter waiting period, only 3.4 months, and a queue length of 546 c l ients . The addition of 1,900 beds in January 1984 reduced the waiting time to zero in November 1985 and depleted the entire queue at the same time (Figure 4). Table 5 describes the fluctuations in waiting times for a l l simulation options described above. - 71 -TABLE 5 EFFECT ON WAITING PERIODS AND QUEUE LENGTHS AT THREE SIMULATED BED ADDITIONS IN JANUARY 1984 S imu1 at ion Additional Total Number Beds Added* of Beds* (January 1984) Waiting Per iod Queue Length 1 100 2,806 8.8 1,496 2 900 3,606 3.4 546 3 1,900 4,606 0.0** 0** * The approved 200 beds are included, as scheduled ** The waiting time and the queue both reached 0 in November 1985 - 72 -TABLE 6 DETAILED WAITING PERIODS (MONTHS) FOR SIMULATION EXPERIMENTS WHEN BEDS ARE ADDED IN THREE INTERVALS, JANUARY 1984, JANUARY 1985, AND JANUARY 1986 Date Number of Beds Added January 1984, January 1985 January 1986* (100)(300)(500)(700)(900)(1100)(1300)(1500)(1700)(1900) WAIT IN MONTHS January 81 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 January 82 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 8.3 January 83 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 January 84 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 9.0 January 85 9.1 8.9 8.6 8.7 8.6 8.4 8.3 8.1 7.9 7.8 January 86 9.1 8.6 8.2 7.7 7.1 6.5 5.9 5.3 4.7 4.0 December 86 9.1 8.2 7.2 6.3 5.2 4.2 3.2 2.2 1 .2 0.4 *Beds added in approximately equal proportions in a l l intervals, e.g. 100 beds added as 35 in January 1984, 35 in January 1985 and 30 in January 1986. - 73 -TABLE 7 DETAILED WAITING PERIODS (MONTHS) FOR SIMULATION EXPERIMENTS WHEN BEDS ARE ADDED IN ONE PULSE IN JANUARY 1984 Date Number of Beds Added January 1984 (100) (900X1900) WAIT IN MONTHS January, 1981 January, 1982 January, 1983 January, 1984 January, 1985 January, 1986 December, 1986 9.0 9.0 8.3 8.3 8.5 8.9 8.9 8.9 7.0 8.8 4.4 8.8 3.4 9.0 8.3 .5 8.5 8.9 3.2 0.0 0.0 - 74 -TABLE 8 DETAILED WAITING PERIODS (MONTHS) FOR SIMULATION EXPERIMENTS UNDER DIFFERENT QUEUE CONDITIONS Date Altered 'Input' Rates -10$ -20$ + 10$ +11 Holds WAIT IN MONTHS January, 1981 9.0 9.0 9.0 9.0 J anuary, 1982 8.3 8.3 8.3 8.3 January, 1983 7.6 6.2 9.2 8.9 January, 1984 7.2 5.0 10.3 9.7 January, 1985 6.8 4.1 11 .0 10.2 J anuary, 1986 6.5 3.6 11 .5 10.4 December , 1986 6.3 3.5 11.7 10.5 12 10 - 8 LL 6 -•+300 X 4 +40 t O+1100 + 54 -50 +125 +31 beds Jan. Jan. 1981 1982 Jan. 1983 i Jan. 1984 Jan. 1985 ..+2100 Jan. 1986 Dec. 1986 M Figure 5. Length of Wait at Different Pulse Rates (see Table 5) Legend: ± beds indicate the time distribution of the 200 approved beds | indicates the single pulse of simulated bed additions * Total number of beds added - 76 -CHAPTER V DISCUSSION An attempt was made to compare a number of different planning methodologies for forecasting institutional service requirements, specifically Extended Care beds, for the elderly population within the Vancouver region. Planning techniques are highly dependent on the accuracy and the avai labi l i ty of data for projections of the effect of options and their l ikely outcomes. The c r i t i c a l assumptions of the study model, including time frame, length of wait, length of stay, and behavioral aspects of demand generation, are discussed below, followed by a discussion of alternative planning methodologies. The applicabil i ty of the study model to long term care is then discussed. The simulation modelling technique provides an important basis for policy formulation and analysis in the area of health services for the elderly. Future research areas and policy implications are also discussed. 1. Cr i t ica l assumptions of the study model A. Time frame The majority of data items collected were in the annual or semi-annual format. This is often the case in health care institutions, as s ta t is t ica l - 77 -ac t iv i t ies are concentrated around budget needs each year in order to be minimally disruptive to the regular patient care routine. A simulation model, however, performs more accurately the narrower the time interval between each iteration. Any simulation model requires several iterations to reach equilibrium, and for this short-term forecasting model (six years) that is not acceptable. A weekly model would, of course, reach this equilibrium even faster, but a weekly time interval would be total ly unrealistic from a data collection point of view. Monthly and weekly models were tested - the data were simply divided by the appropriate number of months or weeks. B. Length of Wait The mean length of wait was chosen as the input to the simulation. It is possible that the mode (most frequesntly occurring) length of wait might have been a more accurate measure of this variable. However, the mean lends i tself to mathematical manipulations more readily. It would also have been more mathematically correct to use a distribution of length of wait to date, and of length of stay as well , rather than the mean of the total length of wait. It was, however, not possible to obtain the wait-data in this format. - 78 -C. Length of Stay The simulation modeling technique used in the present study assumed that the expected length of stay in an Extended Care Unit would remain constant over the forecast period (1981-1986) regardless of any changes in i nputs. This may not be the case, as a number of factors may influence and change the expected length of stay, some of which are discussed below. If the length of wait changes the proportions of deceased and/or improved on the pre-admission l i s t at the time a vacancy occurs wi l l have changed. This may affect the mix of patients who are admitted and subsequently their length of stay. Changes in morbidity patterns among the elderly wi l l have significant effects on the Expected Length of Stay as wi l l new treatment techniques. Priorization of admissions would also change the patient mix of those admitted to an Extended Care Unit. If patients waiting in an acute care bed for admission to an Extended Care Unit are given pr ior i ty , i t is possible that patients may be less deteriorated when admitted to the Extended Care Unit and this may affect the expected length of stay. If there is a change in the proportions of patients who transfer from their second choice Extended Care Unit to their f i r s t choice Unit when a vacancy occurs, i t is also likely that the overall expected length of stay of the units wi l l be altered. - 79 -D. Behavioral Aspects of Demand Generation The present forecasting model is based on an assumption that some of the input variables remain constant over the forecasted time period, i e . , application rates, expected length of stay and age distribution of applicants remain constant. This assumption may not hold, for a variety of reasons. Increased supply of services often induces, or generates, additional demand, since the awareness of services increases among the elderly and their care-giving families. The simulation model can estimate the amount of generated demand after several iterations, so long as a l l input variables are updated each year (or month, i f possible). Therefore, if the application rate increases more than would be expected on the basis of population increases, this additional demand would be attributed to 'generated' demand. This portion of generated demand may not be the entire generated demand, as the i n i t i a l application rate used in the model was an expression of u t i l iza t ion rates and not true morbidity rates, and therefore had i n i t i a l l y a 'generated demand' component built in . However, this latter 'generated demand' portion may be a legitimate expression of need as the elderly population may be aging faster than projected and the phenomenon of the 'thinning' of the family further reduces the informal home support for the elderly person. In order for formal home support systems to be effective, informal support, most often provided by spouses and daughters, is a necessary supplement (81). But as - 80 -the spouses and even the daughters themselves age, the informal support systems are becoming less evident. This may, in turn, contribute to a higher demand as evidenced by an increase in the application rate for admission to Extended Care Units. Changes in expected length of stay, as a result of new treatment techniques, wi l l also affect length of wait and length of the queue. E. Equity of Extended Care Services If equity of service in different municipalities within a region is a planning goal, the municipal bed to population ratio should a l l be identical, if such ratios are based on the differential u t i l iza t ion projected for each 10-year age group. It is a simple matter to calculate the required bed ratio for any given length of wait and then determine the additional number of beds required. This would not apply for a municipality which would provide a regional, specialized service, eg., Assessment and Treatment Centre services. These would be projected separately on a regional need basis. 2. Alternative Planning Methodologies The pIann i ng purpose of developing the current simulation technique bed requirements was to develop a planning method which would for more - 81 -accurately project these requirements by taking more variables into account and including the dynamic nature of the problem. For comparative purposes, the Greater Vancouver Regional Hospital Dis t r ic t data has been applied to the four other, linear, projection methodologies described in Chapter II . A. Bri t ish Columbia Hospital Programs Division Method The projection method outlined on page 35, when applied to the data collected for this study, resulted in 1,057 additional beds being required by 1986 which would yield a bed to population ratio of 23.6 Extended Care Unit beds per 1,000 population 65 years of age and over. The length of wait, according to the simulation model, would be 5.4 months in 1986 and the total number of beds 3,565 in that year. B. Br i t i sh Columbia Long Term Care Bed Allocation Formula The method suggested by the Long Term Care Program is based on the ut i l iza t ion of institutional services by the elderly in two Health Units, one urban and one semi-rural, • during 1978, described on page 33. Application of this formula to the data at hand for this study would result in the recommendation of 1,788 additional Extended Care Unit beds for a bed to population ratio of 28.9 per 1,000 65+ and an estimated waiting time of two months. - 82 -However, this includes Private Hospital beds currently occupied by Extended Care e l ig ib le c l ients . If such beds are not included in the analysis, an additional 1,084 Extended Care Unit beds would be required with a resulting waiting period of 5.5 months at the same (28.9) total bed ratio per 1000 population over 65 years. C. Ottawa-Car Ieton Bed Projection Formula Use of the method employed to forecast bed requirements in the Ottawa-Car Ieton study, referred to on page 20, resulted in a recommended total of 2,881 beds, or 375 additional beds. The length of wait in this projection would be 8.8 months, and the bed to population ratio 19.1 per 1,000 over 65 years of age. This method does not take into account the Private Hospital beds occupied by Extended Care e l ig ib le c l ients . If those beds were added to this projection, the resulting total number of beds required would be 3,690 of which 1,184 would be new Extended Care Unit beds. The length of wait would become five months and the bed to population ratio 24.4 per 1,000 65+. D. Metro-Toronto Formula The method employed by planners in the Metro-Toronto region would result in a total bed requirement of 4,102 Extended Care Unit beds for the Vancouver region, or 1,596 additional beds, with a resulting waiting period - 83 -of 2.7 months and a bed ratio of 27.3 per 1,000 population over 65. The methodology is described on page 29. E. Study Simulation Model In order to maintain a constant waiting period of 9 months, an additional 300 Extended Care beds were estimated to be required by 1986. The length of the waiting l i s t , or the queue, would increase sl ightly under such conditions. An additional 2,200 beds would be required to eliminate the waiting period completely and to deplete the queue. F. Summary It is apparent that these different planning methodologies result in very different bed estimates. These data are shown in Table 9. With the exception of the simulation mode, a l l methods are linear extrapolation techniques. As the dynamic nature of the variables is not taken into account, i t is not surprising that each method gives very different waiting time results. Al l four methods project the bed requirements on the basis of the over 65 years of age population, without giving any consideration to the differential u t i l iza t ion by the young-old (65-74), the medium-old (75-84), and the old-old (85+). - 84 -The simulation technique used in this study does allow for separate projections for each finer age group in the elderly population, acknowledging that different fractions of the elderly make different contributions to the overall projected Extended Care u t i l iza t ion rates. TABLE 9 COMPARISON OF PROJECTED EFFECTS OF OPTIONS AND OUTCOMES USING DIFFERENT PLANNING METHODS P1ann i ng Methods Add itiona1 Beds Total Number Lenth of of Beds Wait (Months) Bed/PopuI at ion Ratio per 1000 65+ B.C. Hospital Programs D iv ison 1 ,057 3,565 5.4 23.6 Long Term Care Bed A 1 location 1,788 4,294 2.0 28.9 Ottawa-Car 1eton 375 2,881 8.8 19.1 Metro-Canada 1 ,596 4,102 2.7 27.3 Study Model 300 2,806 9.1* 18.6 Study Model 1,500 4,206 2.2** 37.9 *Status quo waiting time **Waiting time set at 2.2 months - 85 -3. Applicabil i ty of model to Long Term Care A. Long-Term Care Services The simulation modelling technique for planning future bed require-ments could very easily be adapted to include a l l of the Long Term Care program. The modifications which would be required to adapt the model to Long Term Care use include allowing three choices of fac i l i ty placement, and a priori ty or emergency admission procedure for by-passing the chronological waiting I is t . The Long Term Care system is a more complex system, providing both institutional care and home-based health services. The model could be adapted to include a home support component as well . The model could be employed to project the 'care-careers' of clients through the system. The proportions of those who deteriorate from level to level in a Markovian fashion versus those who become Extended Care e l igible directly could be estimated and analyzed. B. Alternative Services The present simulation technique only incorporates variables related to institutional service provision, but i t would be possible, as mentioned, to add a home service component. This additional alternative service would - 86 -be assumed to reduce the level of expressed demand for f ac i l i t y placement as indicated in Table 4 and Figure 4. This assumption is not based on any definitive evidence and would depend on how the recipients of home support services are chosen (82). 4. Conclusion and Recommendations A. Study Findings The study model predicted that in order to maintain current waiting periods for admission to Extended Care Units an additional 300 beds would be required by 1986. The waiting period as well as the queue vary with number of additional Extended Care beds added to the system so that 2,200 new beds were added the queue would be depleted entirely and the waiting period eliminated. These results would assume that a l l other factors remain constant. B. Effect of Priorization of Applicants As the health care sector assumes new levels of complexity, the tasks of identifying, evaluating and choosing among many available options become increasingly d i f f i cu l t for the policy maker. Objective planning techniques based on quantitative analysis are emerging as both relevant and practical tools in the f ield of health services planning. - 87 -The simulation model developed in this study, albeit deficient in some areas, describes the logical relationships among the variables which must be included in the forecasting equation for projecting Extended Care beds. It i s , however, important to keep the limitations of such a model in mind when making policy and planning decisions. The model is a very simple description of the Extended Care system and does not allow estimation of waiting periods or queue lengths should certain 'ground rules' of the system change. One such change could possibly be the instituting of a priority-admission system based on assessed need rather than e l i g i b i l i t y based on assessed functional levels and chronological wait l i s t s . If a need-priority system were implemented for admissions to an Extended Care Unit, i t is quite possible that u t i l iza t ion patterns, and therefore the input (application rate) to the system, would change with corresponding .changes in outcome measures as the entire flow of patients through the system would be different. A priority-based admission system can be based not only on a need parameter, but also on other characteristics, for example present residence of applicant. It is possible that patients awaiting admission to an Extended Care Unit in an Acute Care hospital may be given a higher order of pr ior i ty , and this may, in turn, affect the patients waiting in the community, so that the medical characteristics of admitted patients would be altered. - 88 -C. Research possibi l i t ies The area of data requirements for quantitative analysis in chronic care planning needs to be investigated further. It may not be possible, from a practical point of view, to collect data monthly. Information as to age, sex, and residency of the applicants on the waiting l i s t is crucial to more accurate predictions resulting from the mode I. Research into the degree of 'generated' .demand is also of prime importance. Is this demand frivolous or is i t the result of diminishing family support, or the acceleration of the aging phenomenon? The effect on expected length of stay as a result of many possible changes in a variety of variables also needs to be investigated. Will priorization of applicants, changes in the medical mix of patients and advances in treatment technologies result in longer stays in the Extended Care Units? Final ly , an area of future research which has very important policy implications is the gain in l i fe expectancy and in years free of institutionalization as a result of preventive service provisions. D. Policy implications When the nature of observed additional demand, the so-called 'generated' demand, is determined policy decisions wi l l be required. As - 89 -wel l , a true increase in the morbidity among the elderly and a diminishing of available informal support systems may require increased service provision by the public sector in the form of increased institutional supply. An increase in pure demand, however, may on the other hand require a policy action directed to discouraging applications for f ac i l i t y placement and providing alternative services instead. (Day Hospitals, Home Support Services, Volunteer Systems etc.) Increased expected length of stay as a result of improved treatment regimens may also require policies which address the question of the rationale for treatment interventions in general. To what degree should active treatment persist? Final ly , priorization of applicants may have implications for Acute Care f ac i l i t i e s which wi l l demand policy changes in that area as wel l . The present study is an attempt at developing technique for projecting future Extended Care bed requirements. The study model incorporates dynamic variables which would enable the planner to assess and to adjust projections continuously. It would also enable the policy maker to attain a better understanding of the various factors which together exert pressure on f ac i l i t y beds, both Acute Care and Extended Care, in the system. - 90 -TABLE 8 DETAILED WAITING PERIODS (MONTHS) FOR SIMULATION EXPERIMENTS UNDER DIFFERENT QUEUE CONDITIONS Date Number of Beds Added January 1984 (Wait in Months) o January, 1981 9.0 9.0 9.0 9.0 January, 1982 8.3 8.3 8.3 8.3 January, 1983 7.6 6.2 9.2 8.9 January, 1984 7.2 5.0 10.3 9.7 January, 1985 6.8 4.1 11 .0 10.2 January, 1986 6.5 3.6 11.5 10.4 December, 1986 6.3 3.5 11.7 10.5 - 91 -REFERENCES 1. Uyeno, D. Management Science Division Faculty of Commerce, University of Br i t i sh Columbia, March, 1981. Personal Communication. 2. Rombout, M.K. Hospitals and the Elderly - Present and Future Trends. Staff Papers, Long Range Health Planning, Health and Welfare Canada, 1975. 3. Government of Canada. "Population: Demographic Characteristies". 1976 Census of Canada, Ottawa: Stat is t ics Canada, 1978. 4. Province of B.C. Population Forecast for G.V.R.D. from 1981 to 1991  and Estimates from 1977 to 1980. Victor ia : Central Stat is t ics Bureau, Ministry of Industry and Small Business Development, 1981. 5. Government of Canada. Population Projections for Canada and the  Provinces 1976 - 2001 Ottawa: Stat is t ics Canada, 1979. 6. Le Febrre, L . A . , Zsigmond, Z . , Devereaux, M.S. A Prognosis for  Hospitals. The Effects of Population Change on the Need for Hospital  Space. Ottawa: Stat is t ics Canada, 1979. 7. Rombout, M.K. "Health Care Institutions and Canada's Elderly. 1971 -2031". A supplement to: Hospitals and The Elderly: Present and  Future Trends. Long Range Health Planning Branch, Ottawa: Health and Wei fare Canada, 1975. 8. Lambert, P.G. The Fact Book. A Guide to the Structure, Function and Operating Policies of the Greater Vancouver Regional Hospital D i s t r i c t , Vancouver: 1981. 9. Province of B.C. The Health Act, Chapter 161, Victor ia : Queen's Printer revised Statutes 1979. 10. Province of B.C. The Community Care Fac i l i t i e s Act, Chapter 57. Victor ia : Queen's Printer revised Statutes 1979. 11. Province of B.C. The Hospital Act, Chapter 176. Victor ia : Queen's Printer revised Statutes 1979. 12. Province of B.C. The Hospital Insurance Act, Chapter 180. Vic tor ia : Queen's Printer revised Statutes 1979. 13. Province of B.C. Administrative Manual. Long Term Care Program. Victor ia : Ministry of Health, 1979. 14. Province of B.C. LTC 1. Long Term Care Assessment Instrument. Victor ia" : Ministry of Health, 1979. 15. Province of B.C. Hospitals for Extended Care - Guide for Operation of  Extended Care Programs. Victor ia : Hospital Programs Division, Ministry of Health, 1980. 16. Kallstrom, L . , Stump, I.G. Extended Care Study, Bed Requirements 1981 - 1986, Vancouver: Greater Vancouver Regional Hospital D i s t r i c t , 1981. - 92 -17. Powell, B . J . "Economic Implications of an Aging Society in Canada", paper presented at the National Symposium on Aging, Ottawa, 1978. 18. Auerbach, L . , and Gerber A . , "Perceptions 2" Study on Population and  Technology - Implications of the Changing Age Structure of the  Canadian Population, Ottawa: Science Council of Canada, 1976. 19. Government of Canada. Canada's Elderly. Ottawa: Stat is t ics Canada, 1979. 20. Schwenger, C. , and Cross J . "Institutional Care and Institutionaliza-tion of the Elderly in Canada," - Aging in Canada. Social  Perspectives, Ed., V.W. Marshall, Toronto Fitzhenry and Whiteside, p.148, 1980. 21. Treas, J . "The Great American F e r t i l i t y Debate," Gerontologist, 21 (1):98, 1981. 22. Clark, R . L . , and Spengler, J . J . "Changing Demography and Dependency Costs - The Implications of Future Dependency Rations in Aging and Income, Essays on Pol icy Prospects, Ed., Heizogg, B.R. New York: Human Services Press, 1977. 23. Lalonde, M. A New Perspective on the Health of Canadians - A Working  Document, Ottawa: Health and Welfare Canada, 1974. 24. Kane, R . L . , and Kane, R.A. "Long Term Care in 6 Countries" Department of Health, Education and Welfare Publication No. (NIH) 76-1207 U.S. Department of H.E.W., Public Health Service, N . I . H . , 1976. 25. Department of Health and Social Security. "Sharing Resources for Health in England." Report of the Resource Allocation Working Party, London: Her Majesty's Stationary Office, 1976. 26. Government of Sweden. "Long Term Care Survey", Annual Report, Stockholm: Department of Social Services, 1977. 27. L i t t l e , V. "Open Care for the Aged: Swedish Model". Social Work 23:282, 1978. 28. Brelim, H.P. , and Ca l , R.M. "Medical Care for the Aged". From Social  Problem to Federal Program, New York: Praeger, 1980. 29. Baum, D.J . "Warehouses for Death," The Nursing Home Industry, Don M i l l s : Barnes and MacEachern L td . , 1977. 30. Crichton, A . J . Department of Health Care and Epidemiology, Faculty of Medicine, University of Br i t i sh Columbia, 1981. Personal Communication. 31. Myles, J .F . "Insti tutionalizing the Elderly" p. 257, Aging in Canada,  Social Perspectives, Ed., Marshall, V.W., Toronto: Fitzhenry and Whiteside. 1980, p. 257. - 93 -32. Penning, M., and Chappell, N . , Ibid. p. 269 33. Denton, F .T. , and Spencer, B.G. "Health Care Costs When the Population Changes", I bid p. 232. 34. Shapiro, E . , and Roos, N.P. "The Geriatric Long Stay Hospital Patient - Intractable Problem or Policy Dilemma?," Draft Manuscript,1980. 35. Shapiro, E . , Roos, N.P. , and Kavanaph, S. "Long Term Care Patients in Acute Care Beds - Is there a Cure?" Gerontologist, 20 (3):342, 1980. 36. Shapiro, E. "The Reality and the Myth of Geriatr ic Bed-Blocking". Essence (3): 179, 1980. 37. Shapiro, E . , and Roos, N.P. "The Geriatric Long-Stay Hospital Patient: A Canadian Case Study.: J . Health P o l i t i c s , Policy and Law 6(1) : 49, 1981. 38. Province of B.C. "Long Stay Case Report." Victor ia : Hospital Programs Division, Ministry of Health, 1980. 39. Province of B . C . . Telex Survey of Acute Care Hospitals and Extended Care Units. Victor ia : Hospital Programs Division, Ministry of Health, July 16, 1980. 40. Br i t i sh Columbia Health Association, "Alternatives to Long Term Care Patients Occupying Acute Care Beds," Task Force Report, Vancouver: B.C.H.A. , 1980. 41. Doherty, N . , Segal J . , and Hicks, B.C. "Alternatives to Institution-alization for the Aged," Aged Care and Services Research, 1 (1):1, 1978. 42. Brocklehurt, J .C. "The Development and Present Status of Day Hospitals," Age and Aging, 8 (Suppl.) 76, 1979. 43. Kane, R . L . , and Kane, R.A. "Alternatives to Institutional Care of the Elderly - Beyond the Dichotomy," Gerontologist, 20 (3); 249, 1980. 44. Bendall, M.J . "Changing Work Pattern in a Geriatric Unit with Day Care," Age and Aging, 7:229, 1978. 45. Morrison, J . "Geriatric Preventive Health Maintenance," Journal of  American Ger. Soc., 28 (3):133, 1980. 46. Crawford, J . "Towards the Future - Focus on Prevention," Paper presented at Conference on Meeting the Challenge of the Mentally  Impaired Elderly, North Vancouver, 1981. 47. Bennett, A.E. "Cost-Effectiveness of Rehabilitation for the Elderly," Gerontologist, 20 (3):284, 1980. - 94 -48. Oster, C . , and Kibat, W.H., "Evaluation of Multi-disciplinary Care in a Day Care Program for Stroke Patients," Journal of American  Gerontological Society, 23:63, 1975. 49. Levine, E . , 'and Kalish, R.A. , Wexler R. , and Ansak, M.L. "On Lok Senior Day Health Centre," Gerontologist 16:39, 1976. 50. Weissert, W.G. "Costs of Day Care - A Comparison to Nursing Homes," Inquiry, 15:11, 1978. 51. Feingold, J .F . "Nursing Home versus Day Care - The Cost Effectiveness Battle," Nursing Homes, 26:10, 1977. 52. Doherty, N . J . , and Hicks, B.C. "The Use of Cost-Effectiveness Analysis in Day Care,"Gerontologist, Oct. 1975, p. 412. 53. Grimaldi, P .L . "The Costs of Adult Day Care and Nursing Homes - A Dissenting View," Inqu i ry, 16:162, 1979. 54. Weissert, W., Wau, T. , Livieratos, B. "Effects and Costs of Day Care and Homemaker Services for the Chronically III" National Centre for Health Services Research, Department of H.E.W. January, 1979. 55. Berg, R . L . , Browning, F . E . , H i l l , L . G . , and Wenkert, W. "Assessing the Health Care Needs of the Aged," Health Services Research, Spring, 1979, p. 36. 56. Pippin, R.N. "Assessing the Needs of Elderly with Existing Data," Gerontologist, 20(1):65, 1980. 57. Kraus, A.S. "Elderly Applicants to Long Term Care Institutions - I and II ," Journal of Gerontological Society, 24(3): 117 and 165, 1976. 58. Avant, W.R. and Dressel, P . L . , "Perceiving Needs by Staff and Elderly Cl ients ," Gerontologist, 20 (1):71, 1980. 59. Havens, B. "The Potential for Determining the Predictive Validi ty of Assessed Needs" Paper presented at the Canadian Association on  Gerontology Meeting, Halifax N.S. , 1979. 60. Province of Manitoba. "Needs and Resources". Aging in Manitoba,  1971, Volume 1 - Introductory Report, Winnipeg: Department of Health and Social Development, Division of Research, Planning and Program Development, 1973. 61. Province of Manitoba, Ibid Volume IX - Special Data, part A - "The Elderly Population." 62. Province of Manitoba, Ibid Volume II - "Metro Winnipeg Region." - 95 -63. Morris, J . B . , and Cohen, C. "A Formula for Estimating the Bed Requirements for a Department of Geriatric Medicine," Health Bui let in, 30:111 , 1978. 64. Hospital Council of Metropolitan Toronto. Report of the Long Term  Care Needs Committee, Phase 1, Toronto: 1980. 65. Ottawa-Car Ieton Regional Dis t r ic t Health Planning Program. Operational Plan: Gerontology Services in Ottawa-Carlton. Report to  CounciI, Ottawa: 1979. 66. Ottawa-Car Ieton Regional Dis t r i c t Health Council. Care of the Elderly  in Ottawa-Car Ieton to 1983-4. The Final Report of the Gerontology Task  Force, Ottawa: 1980. 67. Ottawa-Car Ieton Regional Dis t r i c t Health Council Planning Program, Ottawa-Car Ieton Bed Situation Review 1977-1982, Ottawa: 1978. 68. Province of Manitoba. "Home Care Program for Manitoba". Working Group on Planning and Program Development, Manitoba, 1974. 69. Thompson, K. Department of Health and Social Development, Manitoba, 1980. Personal Communication. 70. Bonham, G. Long Term Care Bed Allocation Formula. Victor ia : Ministry of Health, 1980. 71. Stark A. J . and Gutman, G. M. , Potts, M.A., and Sun, K.H. "Selected Data Concerning Long Term Care in Br i t i sh Columbia." Presented, 8th  Annual Scientif ic and Educational Meeting, Canadian Association on Gerontology, Halifax: 1979. 72. SelI wood, B. A Formula for Estimating Extended Care Bed Requirements. Ministry of Health, Hospital Programs Division, 1979. 73. Milsum L . , Uyeno, D. , Vertinsky, I . , and W i l l , H. "Health Systems Ecology: An Interactive Model". Paper presented at the Joint National  Conference on Major Systems, Anaheim: October, 1971. 74. Belanger, P . , Hurtubise, A . B . , Laszlo, C.A. , Levine, M.D., Milsum J . H . , Uyeno, D. , Vertinsky, I. "On the Modeling of Large-Seale Health Care Systems" Behavioural Science 19(6):407, 1974. 75. Milsum, J . H . , Uyeno, D. , Vertinsky, I . , W i l l , H. "Vancouver Regional Health Planning Model". Paper presented at the Winter Simulation  Conference, New York City, December 1971. 76. Milsum, J . H . , Strohmaier, R., Uyeno," D. , Vertinsky, I . , W i l l , H. "Urban Health Care Simulation: An Ecological Approach to Systems Analysis and Evaluation". Paper presented at the Fifth Annual  Allerton Conference on Circui t and System Theory, Mont ice I Io, III . October 1971. - 96 -77. Packer, A. "Applying Cost-Effectiveness Concepts to the Community Health System" Operations Research 16:227, 1968. 78. Hancock, W.M., Magerlein, D.B., Storer, R.H. , Martin, J .B. "Parameters Affecting Hospital Occupancy and Implications for Fac i l i ty Sizing" Health Services Research 13:276, 1978. 79. Hancock, W.M., Martin, J . B . , and Storer, R.H. "SimuIation-based Occupancy" Inquiry 15 (1): 25,1978. 80. Fetter, R.B. , and Thompson, J.D. "The Simulation of Hospital Systems" Operations Research 13:689, 1965. 1 81. Dunlop, B.D. "Expanded Home -Based Care for the Impaired Elderly: Solution or Pipe Dream?" American Journal of Public Health 70(5) :514, 1980. - 97 -APPENDIX A E l i g i b i l i t y c r i te r ia for different levels of long term care. - 98 -EIiqibiI ity Cr i ter ia for Personal Care A. Characteristics of Personal Care 1. Personal Care Provides: a. 24 hours a day supervision by non-professional (lay) personnel; b. a protective support environment; c. assistance with the act ivi t ies of daily l iving; d. a planned program of social recreational ac t iv i t i e s . 2. The applicant at this level of care wi l l require minimum of 30 minutes of available individual attention by non-professional (lay) personnel during each 24 hour period. B. Cr i ter ia 1. The following c r i te r ia shall be used to determine the e l i g i b i l i t y of an applicant for Personal Care. a. Communication - the applicant (1) wi l l be able to express needs; but may communicate with diff icul ty because of special d isabi l i t ies or medical problems; and (2) may or may not have serious vision or hearing d isabi I i t i e s . b. Personal Functions - the applicant (1) wi l l be independently mobile with or without mechanical aids; (2) w i l l be able to transfer* without human surveillance; (3) may require minor help to bathe, dress and attend to grooming; (for example, assistance in getting in or out of the bathtub, shower, or help with hair washing, cutting nails , help with zippers, buttons, shoelaces) (4) wi l l be able to to i l e t self with or without reminders; (5) may have rare incontinence; (6) wi l l be able to feed self but may need assisted meal service, such as table setting for a blind person, cutting of food for ar thr i t ic persons; (7) may require special access to toi let/bathroom because of continued use of wheelchair. - 99 -B. 1. c. Mental Functions - the applicant (1) may have full use of mental functions (2) may demonstrate forgetfuIness, mild confusion and/or behaviour patterns that mildly disturb others (rarely wanders, has mild depression, s l ightly withdrawn); (3) may have mild impaired comprehension. d. Medical Problems - the applicant (1) may have medical conditions that are stabilized and do not require daily professional supervision; (2) may require supervision to ensure that health care appointments are made and kept; (3) may require special diets of a simple nature (simple diabetic, low sal t , low calorie, low residue and sugar free, etc.) e. Social Functions - the applicant (1) may require assistance to maintain independence in some act ivi t ies of daily l iv ing . ELIGIBILITY CRITERIA FOR INTERMEDIATE CARE I A. Characteristics of Intermediate Care I 1. Intermediate Care Level I provides: a. 24 hours a day supervision by non professional (lay) personnel; b. daily supervision by health professional staff; Transfer means to move from ar t ic le of furniture or equipment to another. For example, from bed to chair, from one chair to another, from wheelchair to to i le t and return. - 100 -c. necessary assistance with the act ivi t ies of daily living such as dressing, washing, grooming and bathing; d. a protective and supportive environment; e. a planned program of social and recreational ac t iv i t i e s . 2. The applicant at this level of care wi l l require minimum of 75 minutes of available individual attention during each 24 hour period as follows: a. Professional - 15 minutes/day b. Non-professional - 60 minutes/day Cr iter i a 1. The following c r i te r ia shall be used to determine the e l i g i b i l i t y of an applicant for Intermediate Care I: a. Communication - the applicant (1) may have dif f icul ty expressing needs (eg. dysphasia); (2) may be unable to adapt to sensory loss. b. Personal Functions - the applicant (1) wi l l be independently mobile with or without mechanical a i ds; (2) may need specialized aids for independently transferring*; (3) may need moderate amount of assistance with bathing, dressing and grooming; (4) .may require reminder or routine to i l e t to avoid frequent incontinence; (5) may require assistance with toi le t ing to maintain cIeanIi ness; (6) may need some supervision in eating; (7) may need directional assistance; (8) may need occasional enemas. c. Mental Functions- the applicant (1) may be mildly depressed or agitated; (2) may have moderately impaired comprehension (has the ab i l i ty to understand simple instructions, simple number and time concepts); (3) may be unable to express some needs; Transfer means to move from ar t ic le of furniture or equipment to another. For example, from bed to chair, from one chair to another, from wheelchair to to i l e t and return. - 101 -(4) may demonstrate dif f icul ty in orientation as to day, time, place; (5) may have varying degrees of mental defects and deterioration. d. Medical Problems - the applicant (1) wi l l require daily supervision by professional health staff; (2) may require nursing procedures such as: i . supervision of medications; i i . change of surgical dressing; i i i . supervision of catheter and ostomy apparatus. (3) may require supervision for v i s i t s to doctor, dentist, eye special is t , etcetera; (4) may require therapeutic dietary supports (diabetic and other special therapeutic diets); (5) wi l l require regular review by a physcian; (6) may require specialist services from time to time (physiotherapist, occupational therapist, speech therap is t ) ; (7) may require therapeutic services for a psychiatric probI em. e. Social Functions - the applicant (1) wi l l require a protective atmosphere; (2) wi l l require a program of ac t iv i t ies to maximize potentia I; (3) wi l l require programs for social and recreational ac t iv i t ies ; (4) may be attending a workshop, educational course or equ ivaIent. ELIGIBILITY CRITERIA FOR INTERMEDIATE CARE II A. Characteristics of Intermediate Care II 1. The basic characteristics of this level of care are the same as for I ntermediate Care I. - 102 -2. This level of care recognizes a heavier level of care and/or supervision requiring additional care items. 3. The applicant at this level of care wi l l require approximately 100 minutes of available individual attention in each 24 hour period as follows: a. Professional - 30 minutes b. Non-professional - 70 minutes B. Cr i ter ia 1. The cr i te r ia for Intermediate Care II are as for Intermediate Care I. However, the applicant for this level of care a. may need considerable directional assistance, supervision of ac t iv i t ies , etcetera; b. may present management problems due to wandering; c. wi l l present staff d i f f icul t ies or require extra staff time due to impaired comprehension; d. may occasionally misappropriate the property of others; e. may have multiple disabilities/medical problems; f. may have need of more variety and/or extensive professional serv ices; g. may be incontinent of bladder and/or bowel; h. may have severe disability/medical problem; i . may have an indwelling catheter; j . may need assistance with eating; k. may require daily professional supervision of catheters, surgical dressings, colostomy, oxygen therapy, etcetera. ELIGIBILITY CRITERIA FOR INTERMEDIATE CARE III A. Characteristics of Intermediate Care III 1. The basic characteristics of this level of care are the same as for Intermediate Care II. 2. This level of care essentially recognizes the psychogeriatric person who has severe behavioural problems on a continuing basis. However, this level of care may also be used for: a. persons requiring a heavier level of care involving considerably more staff time than at the Intermediate Care II level but who are not e l ig ible for Extended Care; and b. persons who are e l ig ible for Extended Care and are awaiting transfer to an extended care unit. 3. The applicant at this level of care wi l l require at least 120 minutes of individual attention during each 24 hour period as follows: a. Professional - 30 minutes b. Non-professional - 90 minutes - 103 -B. Cr i ter ia 1. The cr i te r ia for Intermediate Care III are as for Intermediate Care II plus the following: a. the applicant may disturb others with such anti-social habits as spit t ing, voiding and defecating in public, and indecent exposure, etcetera; b. this person may exhibit destructive, aggressive or violent behaviour (shouting or screaming); c. this person may continually wander away; or d. this person may endanger own l i f e . ELIGIBILITY CRITERIA FOR EXTENDED CARE A. Characteristics of Extended Care 1. An Extended Care Unit or Hospital provides for: a. around-the-clock supervision by a graduate nurse as well as; b. supervision by various other professional health workers such as; Pharmacist, Diet i t ian , Occupational/Physiotherapist, and Social Worker c. regular medical supervision; d. simple nursing procedures once a day or more often, such as application of surgical dressing, administration of injectable medications, oxygen therapy (low concentration, low flow) or catheter care; e. fulfillment of social needs of the beneficiary; f. a home-I ike environment; g. a program to assist each beneficiary to retain or improve his functional ab i l i ty ; h. ski l led assistance with act ivi t ies of daily l iv ing, such as dressing, washing, grooming and bathing. 2. In Extended Care, the length of time required by the applicant for ski l led and professional health staff services may vary widely but wi l l average over 150 minutes. Professional to non-professional ratio of staff approximately 1:4. B. Cr i te r ia 1. The following c r i te r ia shall be used to determine the e l i g i b i l i t y of an applicant for extended care; The appIi cant -a. wi l l not at the time require the services of an acute, rehabilitation or psychiatric hospital; - 104 -b. w i l l , in order to be mobile, require human assistance and sometimes also the use of mechanical aids such as braces, walkers, grab bars, canes and crutches (but not art icles of furniture) in order to: i . turn and move about in bed; i i . transfer* and walk with safety a distance of at least 15 feet clear space; or i i i . transfer* and operate a wheelchair safely, including use of footpedals and brakes. c. may be mobile without human assistance but wi l l require, for medical reasons, 24 hour-a-day surveillance by professional health care staff; d. may be mobile without human assistance but wil l regularly require the performance of one or more simple specific nursing procedures more often than once daily - for example, giving of injectable medications, the change of surgical dressings, the treatment of pressure sores, the delivery of low-concentration, low-flow oxygen, catheter and ostomy care; or tube-feed ing (except when there is also a tracheostomy). 2. In addition to the above cr i te r ia for e l i g i b i l i t y , applicants may demonstrate the following: a. Communication - the applicant (1) may have diff icul ty expressing needs or be unable to express needs. (2) may be unable to adapt to visual or auditory losses, for example, a blind person who is also confused. b. Personal Functions - the applicant (1) may require a varying amount of assistance with dressing, washing, grooming and bathing. c. Mental Functions - the applicant (1) may or may not be mildly depressed or agitated; (2) may or may not have moderately impaired comprehension (abil i ty to understand only simple instructions, short retention span); d. Medical Problems - in addition to those problems related to el ig i bi I ity Transfer means to move from one ar t ic le of furniture or equipment to another; for example from bed to chair, from one chair to another or from wheelchair to to i l e t etcetera (additional note - the individual who can transfer without human assistance, but needs assistance to walk or use a wheelchair and the individual who can walk or use a wheelchair without human assistance but needs assistance to transfer, are both considered e l ig ible for Extended Care). - 105 -the appIicant (1) w i l l require monthly or more frequent v i s i t s by a physician; (2) may require a therapeutic diet; (3) may require brief periods of individual Physio-or Occupational Therapy; (4) may require professional monitoring and judgement on a continuing basis for a psychiatric problem (may not be available in a l l Extended Care Units); (5) may be mobile without human assistance but exhibits gross fecal or urinary incontinence; (6) may be mobile without human assistance but wi l l require, for medical reasons, 24 hour-a-day surveillance by professional health care staff; (7) may be mobile without human assistance but wi l l regularly require the peformance of one or more simple specific nursing procedures more often than once daily - for example, giving of injectable medications, the change of surgical dressings, the treatment of pressure sores, the delivery of low-concentration, low-flow oxygen, catheter and ostomy care; or tube feeding (except when there is also a tracheostomy). In addition to the above cr i ter ia for e l i g i b i l i t y , applicants may demonstrate the following: a. Communication - the applicant (1) may have dif f icul ty expressing needs or be unable to express needs; b. Personal Functions - the applicant (1) may require a varying amount of assistance with dressing, washing, grooming and bathing. c. Mental Functions - the applicant (1) may or may not be mildly depressed or agitated; (2) may or may not have moderately impaired comprehension (abi l i ty to understand only simple instruction, short retention span); (3) may or may not demonstrate varying degrees of dif f icul ty in orientation as to time, place and persons. d. Medical Problems - in addition to those problems related to e l ig i b i I i ty the appIi cant (1) wi l l require monthly or more frequent v i s i t s by a physician; (2) may require a therapeutic diet; - 106 -(3) may require brief periods of individual Physio-or Occupational Therapy; (4) may require professional monitoring and judgement on a continuing basis for a psychiatric problem (may not be available in a l l Extended Care Units); (5) may be mobile without human assistance but exhibits gross fecal or urinary incontinence. e. Social Functions - the applicant (1) wi l l require a home-like environment (2) wi l l require programs for social and recreational ac t iv i t ies ; (3) may be attending social or educational f ac i l i t i e s outside the Extended Care Unit (including vocational training). Source: Administrative Manual - Long Term Care Program, Bri t ish Columbia Ministry of Health, 1979. - 107 -APPENDIX B Flow Diagram of Movement of Extended Care CIientsydetailed version). - 108 -Population at-risk Morbidity 1 Incidence rates /Application rates Figure 1. Detailed Flow Diagram of Movement of Extended Care Clients. Figure 1. Detailed Flow Diagram of Movement of Extended Care Clients. - 110 -APPENDIX C Distribution of lengths of stay of Extended Care patients numbers and percentages) 30 25 20 15 10 5 h _L 154.5 9 15 21 27 33 39 45 51 57 63 69 75 81 87 ^93 M O N T H S Figure 1 . Distribution of Lengths of Stay of Extended Care Patients. (Number of Patients) F igure 2. Distribution of Lengths of Stay of Extended Care Patients. (Percent of total number of Patients Discharged) - 113 -APPRENDIX D Computer printout of a typical simulation experiment. $COMPILE 0 .40OO000E 02 0.54OOOO0E 02 - 0 . 5 O 0 O 0 0 0 E 02 0 . 1250000E 03 .O.31OOOO0E 02 0 . 0 0 .3000O00E 02 O.350OOO0E 02 O.35OOOO0E 02 BEDS HOLX NEWAX I N I T O . V YRCON BEOS ADDED 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 2 5 0 6 0 0 0 E 04 O.300O0O0E 01 0 .1478O00E 03 O.11B6000E 04 19B0 0 . 0 0 . 0 0 . 0 0 . 0 O 0 O O O 0 0 5 ADDED BEDS/MONTH 21 ADDEO BEDS/MONTH 25 ADDED BEDS/MONTH 33 ADDED BEDS/MONTH 39 ADDED BEDS/MONTH 25 ADDED BEDS/MONTH 37 ADDED BEDS/MONTH 49 ADDED BEDS/MONTH 61 ADDED BEDS/MONTH 0 . 3 0 0 0 0 0 0 E 01 0 . 1515000E 03 0 .4500000E 01 0 .3000000E 01 0 .1544000E 03 0 . 3 0 0 0 0 0 0 E 01 O. 15730O0E 03 0 . 3 0 0 0 0 0 0 E 01 O. 16O30OOE 03 O.3OO0000E 01 0 . 1636000E 03 0 . 0 0 . 0 0 . 0 3000000E 02 0 O 0 0 O 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 - 0 . 5 0 0 0 0 0 0 E 02 0 . 0 0 .3100000E 02 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 . 0 O. 1250000E 03 0 . 0 0 . 0 0 . 0 0.35OOOO0E 02 0 . 0 .4000000E 02 0 . 0 0 . 0 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 0.0 HOLDS 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 .3000000E 01 0 . 3 0 0 0 O 0 0 E 01 0.3O000OOE 01 0 . 3 0 0 0 0 0 0 E 01 0 .300O0O0E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 O 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 O.3OOOO0OE 01 NEW APPS 0. 0 .30OO000E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 O.30O0O0OE 01 O.3OO0OO0E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0.3OOOO00E 01 0 . 3 0 0 0 0 0 0 E 01 0 .30000COE 01 1478OO0E 03 O. 1478OO0E 03 0 . 3 0 0 0 0 0 0 E 01 0 .3000000E 01 0 .3000000E 01 0.3O0OO0OE 01 0 .3000000E 01 0.3000OCOE 01 0 .3000000E 01 0 .3000000E 01 0 .3000000E 01 .300OO00E 01 .3000000E 01 .3OO000OE 01 .300OO00E 01 .300CO00E 01 .3OO0000E 01 .3000000E 01 0 .3000000E 01 0 .3000000E 01 , 3000000E 01 O. 0.3O0OO00E 01 •3000000E 01 .3000000E 01 .3000000E 01 .3000000E 01 .3O0OO00E' 01 .3000000E 01 3000000E 01 3000000E 01 3000000E 01 O 0 .3CO0000E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0.30O0OO0E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 O 0 o o 0 0 0 o o o YEAR NEW BEDS CASE 1 BEDS AT END 14780O0E 03 1515000E 03 1515000E 03 1544000E 03 1544000E 03 157300OE 03 1603000E 03 1603000E 03 1636000E 03 1636000E 03 1981 0 . 0 O .1478000E 03 0 . 1 5 1 5 0 0 0 E 03 O.1515000E 03 0 . 1 5 4 4 0 0 0 E 03 0 . 1 5 7 3 0 0 0 E 03 O.1573000E 03 0 . 1 6 0 3 0 0 0 E 03 0 . 1 6 0 3 0 0 0 E 03 0 . 1 6 3 6 0 0 0 E 03 0 .1636OO0E 03 MONTH O.14780O0E 03 0 .1515000E 03 O.1515000E 03 O. 1544000E 03 O.1573000E 03 O. 15730O0E 03 O. 16O3O00E 03 O. 1603000E 03 O.1636000E 03 1478000E 03 O. 0 . 147800OE 03 1515OO0E 03 1544000E 03 1544000E 03 1573000E 03 157300OE 03 1603O00E 03 1603000E 03 1G360OOE 03 1478000E 03 0 . 1478000E 03 O. 1478000E 03 O. 1515000E 03 O. 1544000E 03 0 . I544000E 03 O. 1573000E 03 0 . 15730O0E 03 0 .1603000E 03 0 . 1636000E 03 0 . 1636O0OE 03 0 . 1515000E 03 O .1515000E 0 3 0 . 1544000E 03 0 . 1 5 4 4 0 0 0 E 03 O. 1573000E 03 O.1573000E 03 O. 1603000E 0 3 0 . 1 6 3 6 0 0 0 E 03 O. 1636000E 0 3 1 BEDS AT START 0 . 2 5 0 6 0 0 0 E 04 AVQ WAIT OF THOSE ON 0 0 . 9 0 0 0 0 0 0 E 01 FINAL 0 LENGTH TH 2 BEDS AT START AVG WAIT OF DEPARTERS YEAR 1981 NEW BEDS 0 . 0 CASE 1 BEOS AT END 0 . 2 5 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 AVG WAIT OF DEPARTERS 0 . 8 8 6 0 7 0 4 E 01 FINAL 0 LENGTH YEAR 1981 MONTH 3 BEDS AT START 0 . 2 5 0 6 0 0 0 E 04 0 .4430352E 01 0 . 1191267E 04 0.25OGOO0E 04 0 .4372292E 01 0 . 1197181E 04 0.250GOOOE 04 0 . 0 0 . 5 4 0 0 0 0 0 E 02 0 . 0 0 . 0 0 . 0 0 . 3 5 0 0 0 0 0 E 02 0 . 0 0 . 0 0 . 0 3000000E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 .30000COE 01 0 .30OO000E 01 0.3OO0O0OE 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 0 . 3 0 0 0 0 0 0 E 01 1478000E 03 O .1478000E 0 3 0 . 1515OO0E 03 O .1S15000E 0 3 0 . 1 5 4 4 0 0 0 E 03 O.1544000E 0 3 0 . 15730O0E 03 0 . 1 6 0 3 0 0 0 E 03 0 . 1603000E 03 0 . 1 6 3 6 0 0 0 E 03 0 . 1 6 3 6 0 0 0 E 03 I NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE t BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT. END AVG WAIT OF YEAR NEW BEDS CASE 1 BEOS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEOS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEOS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR 0 . 0 0 . 2 5 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS O.B744583E 01 FINAL 0 LENGTH 1981 MONTH 4 BEDS AT START 0 . 0 0 .25060OOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 6 4 8 9 9 8 E 01 FINAL 0 LENGTH 1981 MONTH S BEDS AT START 0 . 4 0 0 0 0 0 0 E 02 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON Q DEPARTERS 0 . 8 5 7 1 5 6 4 E 01 FINAL 0 LENGTH 1981 MONTH 6 BEDS AT START 0 . 0 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . B 5 0 8 1 3 9 E 01 FINAL 0 LENGTH 1981 MONTH 7 BEDS AT START 0 . 0 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 4 5 7 1 4 2 E 01 FINAL 0 LENGTH 1981 MONTH 8 BEDS AT START 0 . 0 O.254600OE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 4 1 7 1 3 5 E 01 FINAL 0 LENGTH 1981 MONTH 9 BEDS AT START 0 . 0 0 . 2 S 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . B 3 8 6 8 4 3 E 01 FINAL 0 LENGTH 1981 MONTH 10 BEDS AT START 0 . 0 0 . 2 S 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 6 5 0 9 7 E 01 FINAL 0 LENGTH 1981 MONTH 11 BEDS AT START 0 . 0 0 .2S460O0E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 5 0 8 7 4 E 01 FINAL 0 LENGTH 1981 MONTH 12 BEDS AT START 0 . 0 0 .25460O0E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 4 3 2 S 0 E 01 FINAL 0 LENGTH 1982 MONTH 13 BEDS AT START 0 . 0 0 .2S460O0E 04 AVG WAIT OF THOSE ON 0 DEPARTERS O.B341394E 01 FINAL 0 LENGTH 1982 MONTH 14 BEDS AT START 0 . 0 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 2 3 0 9 9 E 01 FINAL 0 LENGTH 1982 MONTH 15 BEDS AT START 0 . 4 3 2 4 4 9 9 E 01 O.1203637E 04 0 .250600OE 04 0 .4285782E 01 O. 1210537E 04 0.250GO0OE 04 0 . 4 2 5 4 0 6 9 E 01 O.1215S0GE 04 0 .2S46000E 04 0 . 4 2 2 8 5 7 1 E 01 0 .1220776E 04 0.254GO0OE 04 0 .4208568E 01 O. 1226287E 04 0.2546O0OE 04 0 . 4 1 9 3 4 2 1 E 01 O.1231986E 04 0 .2546000E 04 0 . 4 1 8 2 S 4 9 E 01 O.1237829E 04 0.254GOOOE 04 0 . 4 1 7 5 4 3 7 E 01 0 . 1 2 4 3 7 7 5 E 04 0.254GOOOE 04 0 . 4 1 7 1 6 2 5 E 01 0 .1249788E 04 0 .2546000E 04 0 .4170697E 01 0 . 1 2 5 5 8 3 8 E 04 0 . 2 5 4 6 0 0 0 E 04 0 .41S1550E 01 0 .126S596E 04 0 .2546000E 04 0 . 4 1 5 7 4 4 0 E 01 O.1275440E 04 0.254GO0OE 04 I i NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE t BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEOS AT END AVG WAIT OF YEAR 0 . 0 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 1 4 8 S 0 E 01 FINAL 0 LENGTH 1982 MONTH 16 BEDS AT START 0 . 0 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 OEPARTERS 0 . B 3 1 5 4 0 3 E 01 FINAL 0 LENGTH 1982 MONTH 17 BEDS AT START O . O 0 .2S46O0OE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 2 3 4 8 6 E 01 FINAL 0 LENGTH 1982 MONTH 18 BEDS AT START 0 . 0 0 . 2 S 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 3 8 0 9 I E 01 FINAL 0 LENGTH 1982 MONTH 19 BEOS AT START 0 . 0 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT. OF THOSE ON 0 DEPARTERS 0 . B 3 5 8 3 0 5 E 01 FINAL 0 LENGTH 1982 MONTH 20 BEDS AT START 0 . 0 0 . 2 5 4 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 3 8 3 3 2 0 E 01 FINAL 0 LENGTH 1982 MONTH 21 BEDS AT START 0 .540CO00E 02 0 . 2 6 0 0 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 4 1 2 4 2 8 E 01 FINAL 0 LENGTH 1982 MONTH 22 BEDS AT START 0 . 0 0 . 2 6 0 0 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 4 4 2 7 7 6 E 01 FINAL 0 LENGTH 1982 MONTH S3 BEDS AT START 0 . 0 0 . 2 6 0 0 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .8474108E 01 FINAL 0 LENGTH 1982 MONTH 24 BEDS AT START 0 . 0 0 . 2 6 0 0 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . B S 0 6 1 9 1 E 01 FINAL 0 LENGTH 1983 MONTH 25 BEDS AT START . -0.5OOO0O0E 02 0 . 2 S 5 0 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 5 3 8 8 2 4 E 01 FINAL 0 LENGTH 1983 MONTH 26 BEDS AT START 0 . 0 0 . 2 S 5 0 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 5 5 7 8 1 7 E 01 FINAL 0 LENGTH 1983 MONTH 27 BEDS AT START 0 .4157701E 01 O.1285323E 04 0 .2546000E 04 0 .4161743E 01 O.1295204E 04 0 .2546000E 04 0 .4169045E 01 O.1305047E 04 0.25460OOE 04 0 .41791S2E 01 0 .1314B21E 04 0 .2546000E 04 0 .4191660E 01 0 .1324499E 04 0 .2546000E 04 0 .4206214E 01 O.1334058E 04 0 .254600OE 04 0 .422138BE 01 0 .1340403E 04 O.260OOOOE 04 0 .4237054E 01 0 .1346602E 04 0 .260000OE 04 O.4253096E 01 O.13S2649E 04 0 .2G00000E 04 0 .4269412E 01 O.1358541E 04 O.260O0OOE 04 0 .4278909E 01 O.1370036E 04 0 .2550O00E 04 0 .4291371E 01 0 .1381442E 04 0.255OO0OE 04 I CTl I NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE t BEOS AT END AVG WAIT OF YEAR NEW BEOS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR 0 . 0 0 .25500O0E 04 AVG WAIT OF THOSE ON Q DEPARTERS 0 . 8 S 8 2 7 4 3 E 01 FINAL 0 LENGTH 1983 MONTH 28 BEDS AT START 0 . 0 0.2550OO0E 04 AVG WAIT OF THOSE ON 0 DEPARTERS O.B612753E 01 FINAL 0 LENGTH 1983 MONTH 29 BEDS AT START 0 . 0 0 .2550000E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .8G47104E 01 FINAL 0 LENGTH 1983 MONTH 30 BEDS AT START 0 . 0 ' 0 . 2S50000E 04 AVG WAIT OF THOSE ON 0 DEPARTERS O.B685129E 01 FINAL 0 LENGTH 1983 MONTH 31 BEDS AT START 0 . 0 0.255OOOOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 7 2 6 2 4 4 E 01 FINAL 0 LENGTH 1983 MONTH 32 BEDS AT START 0 . 0 0 .2550000E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .87G9936E 01 FINAL 0 LENGTH 1983 MONTH 33 BEDS AT START 0 . 1250OOOE 03 0 .2G7S000E 04 AVG WAIT OF THOSE ON 0 DEPARTERS O.B815748E 01 FINAL 0 LENGTH 1983 MONTH 34 BEDS AT START 0 . 0 0.267SOOOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 8 5 8 5 3 8 E 01 FINAL 0 LENGTH 1983 MONTH 35 BEDS AT START 0 . 0 0.26750OOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 8 8 9 8 5 1 0 E 01 FINAL 0 LENGTH 1983 MONTH 36 BEDS AT START 0 . 0 0 .2675000E 04 AVG WAIT OF THOSE ON g DEPARTERS 0 . 8 9 3 5 8 4 3 E 01 FINAL Q LENGTH 1984 MONTH 37 BEDS AT START 0.300O0O0E 02 0 .2705000E 04 AVG WAIT OF THOSE ON 0 DEPARTERS O.B9707O7E 01 FINAL 0 LENGTH 1984 MONTH 38 BEDS AT START 0 . 0 0 .2705000E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .8986223E 01 F I N A L 0 LENGTH 1984 MONTH 39 BEOS AT START 0 .430G376E 01 • O. 1392729E 04 O.25500O0E 04 0 .4323552E 01 O.1403875E 04 0 .2550000E 04 0 .4342S65E 01 0 .1414857E 04 0 .2550000E 04 0 .43G3122E 01 0 . 1425660E 04 O.2550O0OE 04 0 .43B4968E 01 0 .1436268E 04 0 .2550000E 04 0 .4407874E 01 O.1446669E 04 0 .2550000E 04 0 .4429269E 01 O.1449636E 04 0 .2675000E 04 0 .44492S5E 01 0 .1452391E 04 0 .2G75000E 04 0 .4467921E 01 O.1454947E 04 0 .26750O0E 04 0 .4485353E 01 0 .1457318E 04 0.2G7500OE 04 0 .4493112E 01 O.14G0675E 04 0 .27050O0E 04 0 .4501123E 01 O.1463954E 04 0 .2705000E 04 I I NEW BEOS CASE t BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEOS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE '1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR 0.31OOOO0E 02 O.2736O00E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .9002247E 01 FINAL 0 LENGTH 1984 MONTH 40 BEDS AT START 0 . 0 0 . 2 7 3 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .9017456E 01 FINAL 0 LENGTH 1984 MONTH 41 BEDS AT START 0 . 0 0.273GOOOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .9031891E 01 FINAL 0 LENGTH 1984 MONTH 42 BEDS AT START 0 . 0 O.2736OO0E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .90455B4E 01 FINAL 0 LENGTH 1984 MONTH 43 BEDS AT START 0 . 0 0 .2T3G00OE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 5 8 S 7 7 E 01 FINAL 0 LENGTH 1984 MONTH 44 BEDS AT START 0 . 0 0 . 2 7 3 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 7 0 8 9 8 E 01 FINAL 0 LENGTH 1984 MONTH 45 BEDS AT START 0 . 0 0 .273G000E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .9082579E 01 FINAL 0 LENGTH 1984 MONTH 46 BEOS AT START 0 . 0 0.273SOOOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .909365SE 01 FINAL 0 LENGTH 1984 MONTH 47 BEDS AT START 0 . 0 0 . 2 7 3 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .9104153E 01 FINAL 0 LENGTH 1984 MONTH 48 BEDS AT START 0 . 0 O.273G0O0E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .91141O1E 01 FINAL 0 LENGTH 1985 MONTH 49 BEDS AT START 0 . 3 5 0 0 0 0 0 E 02 0.2771OOOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .912352GE 01 FINAL 0 LENGTH 1985 MONTH 50 BEDS AT START 0 . 0 0 . 2 7 7 1 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 .9114557E 01 FINAL 0 LENGTH 1985 MONTH 51 BEDS AT START 0 .4508728E 01 O.1465352E 04 0 .2736O00E 04 0 .4515945E 01 O.14G6673E 04 O.2736OO0E 04 0 .4522792E 01 O.1467920E 04 0.273GOO0E 04 0 .4529288E 01 O.1469098E 04 0 .273G000E 04 0 .4535449E 01 O.1470210E 04 0 .273G000E 04 0 . 4 5 4 1 2 8 9 E 01 O.1471259E 04 0 .2736000E 04 0 .4546827E 01 0 .1472249E 04 O.273GO00E 04 0 .455207GE 01 O.1473183E 04 O.273G0OOE 04 0 .4557051E 01 O.1474063E 04 0.273GOOOE 04 0 .45G17G3E 01 0 . 1 4 7 4 B 9 2 E 04 0.273GOO0E 04 0 .4557279E 01 O.1476634E 04 0.2771OOOE 04 0 . 4 5 5 3 8 9 9 E 01 O.1478421E 04 0 . 2 7 7 1000E 04 I oo i NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT ENO AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF YEAR NEW BEDS CASE 1 BEDS AT END AVG WAIT OF 0 . 0 0 . 2 8 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 9 7 7 5 4 E 01 FINAL 0 LENGTH 19BG MONTH 64 BEDS AT START 0 . 0 0 .28060OOE 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 9 1 3 6 4 E 01 FINAL 0 LENGTH 1986 MONTH 65 BEDS AT START 0 . 0 0 . 2 B 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 8 7 7 9 0 E 01 FINAL 0 LENGTH 19B6 MONTH 66 BEDS AT START 0 . 0 0 . 2 8 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 8 6 7 2 5 E 01 FINAL 0 LENGTH 1986 MONTH 67 BEDS AT START 0 . 0 0 . 2 8 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 8 7 8 B 5 E • 0 1 FINAL 0 LENGTH ' 1986 MONTH 68 BEDS AT START 0 . 0 0 . 2 8 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 9 1 0 1 3 E 01 FINAL 0 LENGTH 1986 MONTH 69 BEDS AT START 0 . 0 0 . 2 8 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 0 9 5 8 7 7 E 01 FINAL 0 LENGTH 1986 MONTH 70 BEDS AT START 0 . 0 0 . 2 B 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 1 0 2 2 S 9 E 01 FINAL 0 LENGTH 19B6 MONTH 71 BEDS AT START 0 . 0 0 . 2 B 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 1 0 9 9 6 8 E 01 FINAL 0 LENGTH 19B6 MONTH 72 BEDS AT START 0 . 0 0 . 2 8 0 6 0 0 0 E 04 AVG WAIT OF THOSE ON 0 DEPARTERS 0 . 9 1 1 B B 3 0 E 01 FINAL 0 LENGTH 0 .4545GB2E 01 0 .1506072E 04 O.28060O0E 04 0 . 4 S 4 3 B 9 5 E 01 0 .1509240E 04 0 . 2 B 0 6 0 0 0 E 04 0 .4543363E 01 O.1512427E 04 0 .2806O00E 04 0 .4543942E 01 O.1515619E 04 0 .2806000E 04 0 . 4 5 4 5 5 0 6 E 01 0 .1518806E 04 0 . 2 8 0 6 0 0 0 E 04 0 .4547938E 01 O.1521976E 04 0 .28060O0E 04 0 .4551129E 01 O.1525120E 04 0 .28060COE 04 0 .4554984E 01 O.1528231E 04 0 . 2 8 0 6 0 0 0 E 04 0 .4559415E 01 O. 1531303E 04 0 .28060O0E 04 0 .4564341E 01 O.1534328E 04 I I - 120 -APPRENDIX E Listing of Computer Prog - 121 -LISTING OF BMONTHLY AT 12:23:58 ON JUN 18, 1981 FOR CCID=CLOG 1 SCOW I LE 2 C VARIABLES ARE DEFINED AS THEY ARE USED 3 INTEGER YRCON 4 REAL NEWAP(72),HOLD(72),ADBED(72),NEWAX(6),HOLX(6) 5 REAL INFLO, LEFTW,INITW,OTFLO,INITQ,MOFLO 6 C 7 C 8 READ, BEDS 9 C ENSURE THAT THE ADBED ARRAY IS INITIALLY EMPTY 10 DO 52 1=1,72 11 52 ADBED(l)=0. 12 C NEW BEDS CAN BE ADDED TO 13 C THE MODEL, A MONTH VALUED 9999 STOPS THE 14 C SEARCH FOR NEW BEDS TO ADD. 15 C NUMBER OF BEDS IS FOLLOWED BY MONTH WITH 16 C JANUARY 81 BEING MONTH 1, ONE SET OF DATA 17 C PER LINE 18 DO 50 1=1,72 19 READ,ADDED,MO 20 IF (MO.EQ.9999) GO TO 51 21 ADBED(MO)=ADBED(MO)+ADD ED 22 PRINT,ADDED,MO, 'ADDED BEDS/MONTH' 23 50 CONTINUE 24 51 CONTINUE 25 C HOLX IS THE NUMBER OF HOLDS/MO IN YEAR K 26 C NEWAX IS THE NUMBER OF NEW APPLICANTS/MO IN YEAR K 27 READ HOLX 28 READ NEWAX 29 C Y IS THE INITIAL WAIT TO DATE IN Q AT START OF MONTH. 30 C INITQ IS THE INITIAL QUEUE LENGTH AT THE START OF THE FIRST MONTH. 31 READ, INITQ,Y 32 C IG IS A FLAG FOR DEBUG PRINTING IF SET TO 1 33 READ, IG 34 READ, YRCON 35 PRINT, 'BEDS', BEDS 36 PRINT, 'HOLX', HOLX 37 PRINT, 'NEWAX', NEWAX 38 PRINT, 'INITQ, Y ' , INITQ,Y 39 PRINT, 'YRCON', YRCON 40 C CONVERT DATA INTERNALLY FROM YEARLY DATA TO MONTHLY. 41 C NOTE THAT THIS IS A PSEUDO-MONTHLY MODEL 42 DO 15 J=1,72 43 K =(J-1)/12+1 44 NEWAP(J)=NEWAX(K) 45 HOLD(J)=HOLX(K) 46 15 CONTINUE 47 C AT THIS POINT ADD ANY SPECIAL CODE TO CHANGE 48 C ANY OF THE MONTHLY RATES. 49 PRINT, 'BEDS ADDED', ADBED 50 PRINT, 'HOLDS ' , HOLD 51 PRINT, "NEW APPS ',NEWAP 52 C SET UP MONTH COUNTER 53 DO 5 J=l,72 54 C COMPUTE THE YEAR IN WHICH MONTH FALLS 55 l=(J-1)/12+1 56 C COMPUTE YEAR FROM BASE YEAR YRCON 57 IYR=YRC0N+1 58 PRINT, 'YEAR', IYR, 'MONTH',J, 'BEDS AT START ' , BEDS - 122 -LISTING OF BMONTHLY AT 12:23:58 ON JUN 18, 1981 FOR CCID=CLOG 59 PRINT, 'NEW BEDS', ADBED(J) 60 C CONFUTE BEDS AT START OF MONTH 61 BEDS=BEDS+ADBED(J) 62 C ELOS=30.3 MONTHS 63 C DISCH/M0= ((AVG BEDS*.98)/(ELOS/12))/12 64 C COMPUTE THE NUMBER OF ADMISSIONS THIS MONTH 65 ADM1S=((BEDS*.98)/(30.3/12.0))/12.0 66 1F(IG.EQ.1) PRINT, 'ADMISSIONS',ADMIS, ' BEDS ', BEDS 67 C ADJUSTED OF COURSE FOR THOSE WHO ARE REJECTED, ETC. 68 C .01155 OF THOSE SCREENED DIE FOR EACH MONTH THEY ARE IN 69 C THE ACTIVE WAITING LIST, 0.01544 ARE FOUND INELIGIBLE 70 C FOR EACH MONTH ON THE WAITING LIST. 71 C WE CAN THUS ESTIMATE THE PERCENTAGE OF THOSE SCREENED THAT ARE 72 C DECEASED OR FOUND INELIGIBLE BY MULTIPLYING THE SUM OF 73 C THESE PERCENTAGES BY 2Y, THE AVERAGE WAIT OF APPLICANTS ON 74 C SCREENING. 75 ZZ=2.0*Y*(.01155+. 01544) 76 C IT IS ASSUMED THAT THIS VALUE Z IS THE PERCENTAGE OF THOSE 77 C SCREENED THAT ARE EXCLUDED FOR THESE REASONS, BUT THAT 78 C THE OTHER PROPORTIONS ARE NOT AFFECTED, THUS IF THERE 79 C WERE NO QUEUE, THOSE SCREENED OUT DUE TO DEATH, OR BEING 80 C FOUND INELIGIBLE WOULD BE ZERO, NEW ADMISSIONS 81 C WOULD STILL BE 47?, THOSE GOING TO THE ON-HOLD LIST WOULD 82 C STILL BE 9.2$, THOSE MOVED OR REJECTED 3.9$. 83 c THOSE RETURNED TO THE ACTIVE LIST WOULD STILL BE 84 c 1 5. 7£ WHEN VIEWED AS PROPORTIONS. THUS THE PROPORTION 85 c OF SCREENED PEOPLE TAKEN OFF THE LIST RELATIVE TO THE 86 c NUMBER OF ADMITTED TO ECU BEDS WOULD THEN BE 87 c (.092+.039)/.47 88 XX=(ZZ+.092+.039)/.47 89 OTFLO=ADMIS*(1.0+XX) 90 IF (IG.EQ. DPRINT, • XX • , XX, 'OTFLO', OTFLO, 'ZZ', ZZ 91 c COMPUTE MONTHLY INFLO 92 INFLO= HOLD(J)+NEWAP(J) 93 1F(IG.EQ.1) PRINT, 'INFLO', INFLO, 'HOLD', HOLD(J), 94 1 'NEWAP ', NEWAP(J) 95 c QUEUE AT THE END OF MONTH 96 MOFLO=(INFLO - OTFLO) 97 IF (IG.EQ.1) PRINT, 'MOFLO', MOFLO 98 IF (MOFLO.GE.O) GO TO 68 99 A =-MOFLO 100 IF (INITQ.GE.A) F1NQ=1N1TQ + MOFLO 101 IF (INITQ.LT.A) FINQ=0 102 c 103 GO TO 69 104 68 F1NQ=1N1TQ+MOFLO 105 c COMPUTE AVERAGE QUEUE 106 69 AVGQ=(FINQ+INITQ)/2.0 107 c 108 c 109 c WAITING TIME CALCULATIONS 110 111 c c 112 c COMPUTE MONTHS TO EXHAUST INITIAL QUEUE 113 c 114 B=1N1TQ/OTFLO 115 IF (IG.EQ.1) PRINT, 'B'.B 116 IF (B.LT.1.0) GO TO 2 - 123 -LISTING OF BMONTHLY AT 12:23:58 ON JUN 18, 1981 FOR CCID=CLOG 117 C 118 C CASE 1 119 PRINT, 'CASE 1' 120 C AVG WAIT OF THOSE WHO LEFT QUEUE DURING MONTH 121 LEFTW=2*Y 122 C AVG WAIT OF NEW APPS IS 1/2 MONTH 123 C THE NUMBER OF NEW APPLICANTS IS 124 APPS=INFLO 125 IF(IG.EQ.I) PRINT, 'APPS', APPS 126 C AVERAGE FLOW—AN APPROX MEASURE OF RATE OF MOVEMENT, LINE BU 1LDUP 127 AVFLO=(INFLO+OTFLO)/2.0 128 c AVG WAIT OF LAST FELLOW IN Q AT START OF MONTH 129 F=0.0 130 c AT END F CURRENT MONTH 131 F=F+1.0 132 c AVG WAIT F THE GUY WHO ENDS UP AT THE HEAD OF 133 c THE LINE WHEN THE CURRENT MONTH ENDS. 134 G=2.0*Y 138 IF (IG.EQ.1) PRINT, 'AVFLO.F.G', AVFLO.F.G 139 c AVG WAIT F ALL THE FOLKS LEFT OVER 140 XAVG=(G+F)/2.0 141 c WEIGHTED AVG WAIT AT START OF NEXT MONTH FOR THOSE ON Q 142 Y=(XAVG*(INITQ-OTFLO)+APPS*0.5)/(INITQ-OTFLO+APPS) 143 GO TO 6 144 c 145 c CASE 2 146 2 CONTINUE 147 PRINT, 'CASE2' 148 c AVG TIME IN Q FOR THOSE IN INITIALLY BUT WHO LEFT IS 149 c AVG WAIT TO DATE AT START OF MONTH PLUS AVG WAIT 150 c DURING THE MONTH 151 INITW=2.0*Y 152 c PARTIAL MONTH OF INFLO TO MEET 1-B MONTHS OF OUTFLO 153 C=(OTFLO-INITQ)/INFLO 154 c AVG TIME IN QUEUE FOR THOSE WHO JOINED THE Q AND LEFT SAME MONTH 155 D=(1.0-C+B)/2.0 156 c AVERAGE TIME IN Q FOR THOSE WHO JOINED THIS MONTH AND STAYED 157 Y=(1.0-C)/2.0 158 c AVG WAIT FOR ALL THOSE WHO LEFT 159 LEFTW=((D»(1.0-B)*OTFLO)+(INITW*B*OTFLO))/(OTFLO) 160 IF<IG.EQ.1) PRINT, «C», C, ' D ' , D, ' Y ' , Y 161 1, '1NITW', INITW, 'LEFTW', LEFTW 162 c PRINT RESULTS 163 6 PRINT. 'BEDS AT. END' BEDS, 'AVG WAIT OF THOSE ON 0 Y 164 PRINT, 'AVG WAIT OF DEPARTERS', LEFTW, 'FINAL Q LENGTH , FINQ 165 c INITIAL AVG WAIT TO DATE AND INIT. Q LENGTH FOR NEXT MONTH CALCULA-TIONS 166 c STOP AFTER SIX YEARS/72 MONTHS 167 5 INITQ=FINQ 168 STOP 169 END 170 $EXECUTE 

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