In the News: New ‘More Beds’ Legislation Adds Up To Worse Care

September 9, 2022 | Hamilton, ON
Contributed by Manaf Zargoush, Associate Professor, Health Policy and Management, DeGroote School of Business

As concerned researchers, we urge the Ontario government to avoid short-sighted policies like Bill 7 that claim to solve health care problems but leave larger structural factors unchanged.

The recent passage of Bill 7, the “More Beds, Better Care Act,” raises serious concerns about availability of responsive care for older adults in Ontario and, more broadly, about systematic discrimination based on age in the provision of health care in the province. This ageist legislation, pushed through without any public consultation, purports to solve Ontario’s health care crisis by shifting responsibility to the most vulnerable individuals in the system: frail older adults.

Currently, hospital patients who no longer need acute care but are unable to look after themselves if discharged are designated as “alternative level of care” (ALC) pending transfer to a long-term care (LTC) home of their (or their family’s) choice. Waiting times for transfer to LTC can last weeks or months, especially for the municipally operated or non-profit sector homes that two-thirds of Ontarians on the wait list for long-term care prioritize.

Bill 7 would permit hospitals to clear out approximately 2,400 of the 6,000 patients currently designated ALC who are waiting for a long-term care placement by transferring these patients to any long-term care home with a bed available. (The other approximately 3,500 ALC patients are waiting for hospice, palliative care, rehabilitation or home care placements.)

Patients and families legitimately fear that this policy will result in transfers of older adults to institutions far from their communities, to homes that are not compatible with their specific care, cultural or linguistic needs, or to homes with a poor record of IPAC and high mortality rates during the first and second waves of the COVID-19 pandemic, many of which are older homes in the for-profit sector.

The issue of distance from family members is particularly troubling since families perform numerous daily tasks such as bathing, feeding, and providing social contact for relatives in long-term care facilities. Moreover, research shows that premature discharge of ALC patients without considering their health complexity and needs leads to their higher chance of readmission and death, which eventually increases health care costs. Few would argue that the ability to exercise choice over one’s living situation (including location) is a fundamental Canadian freedom: why should this right be denied to people simply because they are older?

Furthermore, Bill 7 does not address the interconnected structural factors that have contributed to the current crisis in health care in Ontario. It is staff shortages — particularly with respect to nurses — that are fuelling the emergency room closures in the province’s cities and rural regions. Nurses are leaving the profession because of fatigue, burnout, and low wages. A key factor is the Ontario government’s 2019 legislation that caps wage increases for public sector workers to 1 per cent annually. Nurses have served the public with dedication during the pandemic: they deserve compensation commensurate with the level of physical and mental strain and the risk they take on in the workplace.

A second component of the structural crisis in health care is the inadequate supply of long-term care beds in Ontario. Currently, there are close to 40,000 people on the wait-list for long-term care placement in the province. Clearly, the problem is not simply a lack of hospital beds but an overall lack of beds in long-term care. Premier Ford claims that ALC patients will receive better care in long-term care homes, even those not of their choosing, than in hospitals. But long-term care is suffering from its own staffing crisis: as Ontario’s Long-Term Care COVID-19 Commission reported in 2021, staff shortages, reliance on part-time workers, and high turnover rates have plagued long-term care since well before the pandemic. Improving in-home and community-based support services and providing adequate compensation for the nurses and personal support workers dedicated to caring for our elderly would help to resolve this structural deficit and respect older adults’ right to choose.

The crisis in care for frail older adults is not going to disappear. Ontario is aging: demographic trends suggest that by 2043, almost one-quarter of the province’s population will be over 65. Forcing individuals to relocate without consent or choice is horrific. As concerned researchers, we urge the Ontario government to avoid short-sighted policies like Bill 7 that claim to solve health care problems but leave larger structural factors unchanged.

Instead, we advocate developing and implementing evidence-based, systemic policies and a co-ordinated provincial strategy to provide high-quality health care for all in the future.

Authors:

  • Ellen Badone, Professor Emerita, Anthropology and Religious Studies, McMaster University
  • Pamela Baxter, Associate Professor, School of Nursing, McMaster University
  • Cal Biruk, Associate Professor, Anthropology, McMaster University
  • Sheila Boamah, Assistant Professor, School of Nursing, McMaster University
  • Dawn Bowdish, Professor and University Scholar, Canada Research Chair in Aging and Immunity, Faculty of Health Sciences, McMaster University
  • Andrew Costa, Schlegel Research Chair in Clinical Epidemiology and Aging, McMaster University
  • Nicole Dalmer, Assistant Professor, Health, Aging and Society, Associate Director, Gilbrea Centre for Studies in Aging, McMaster University
  • James Dunn, Professor, Health, Aging and Society, Sen. William McMaster Chair in Urban Health Equity, McMaster University
  • Evelyne Durocher, Assistant Professor, School of Rehabilitation Science, Faculty of Health Sciences, McMaster University
  • Meredith Griffin, Associate Professor, Health, Aging and Society, Associate Director, Gilbrea Centre for Studies in Aging, McMaster University
  • Michelle Howard, Associate Professor, Family Medicine, Faculty of Health Sciences, McMaster University
  • Sharon Kaasalainen, Gladys Sharpe Chair in Nursing, McMaster University
  • Henry Siu, Associate Professor, Family Medicine, Faculty of Health Sciences, McMaster University
  • Manaf Zargoush, Associate Professor, Health Policy and Management, DeGroote School of Business, McMaster University
  • Rachel Zhou, Professor, Health, Aging and Society, Institute on Globalization and the Human Condition, McMaster University

Read the full article in the Hamilton Spectator.

Manaf Zargoush

Manaf Zargoush

Associate Professor, Health Policy & Management

Dr. Manaf Zargoush is an associate professor of Health Policy & Management at the DeGroote School of Business, McMaster University. He holds a Ph.D. in Healthcare Operations and Information Management (McGill University, Montreal, Canada), a Ph.D. in Decision Science and Statistics (ESSEC Business School, Paris, France), M.Phil. in Decision Sciences(ESSEC Business School, Paris, France), M.Sc. in Socio-Economic Systems Engineering (Sharif University of Technology, Tehran, Iran), and B.Sc. in Mechanical Engineering (Jundi-Shapoor University, Ahvaz, Iran). His main areas of research expertise and interests include using Data Science (machine learning, artificial intelligence, statistical modeling) for descriptive and predictive analytics and optimization (stochastic dynamic optimization, Markov and Semi-Markov Decision Processes, Partially Observable Markov Decision Processes) for prescriptive analytics of a wide range of health-related problems, such as medical decision-making, and healthcare operations management. His current main projects are chronic disease (particularly hypertension and diabetes) management and aging research (e.g., ALC in Canada and predicting the trajectory of disabilities among older adults). He is also interested in physicians’ learning in uncertain environments as well as causal analytics using machine learning and big data.

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