Long-term Psychological and Occupational Effects of Providing Hospital Healthcare during SARS Outbreak

Robert Maunder, William J. Lancee, Kenneth Balderson, Jocelyn Bennett, Bjug Borgundvaag, Susan Evans, Christopher Fernandes, David S. Goldbloom, Mona Gupta, Jonathan Hunter, Linda M. Hall, Lynn Nagle, Clare Pain, Sonia Peczeniuk, Glenna Raymond, Nancy Read, Sean B. Rourke, Rosalie Steinberg, Thomas E. Stewart, Susan VanDeVelde‐Coke, Georgina Veldhorst, Donald Wasylenki•View original

OverviewBalancedalloy voice

Picture two thousand and three for a moment. Hospitals in Toronto are in full defensive posture against a virus no one quite understands. Masks, quarantine, and whole units flipped into isolation mode overnight. For the people working those wards, the adrenaline fades long before the memories do. So the question Maunder and colleagues asked was simple yet tough: when the dust settled—more than a year later—what had stuck? And why did it stick for some people and places more than others? They used a natural experiment that almost never happens in real life. Toronto hospitals treated patients with severe acute respiratory syndrome, while Hamilton hospitals, less than an hour down the highway and inside the same provincial health system, did not. Same rules, same precautions, but different exposure. Thirteen to twenty-six months after the last SARS patient left the hospital—on average, about nineteen months—Maunder's team surveyed healthcare workers across thirteen sites, nine in Toronto and four in Hamilton. Out of one thousand nine hundred eighty-four people invited, seven hundred sixty-nine completed the main survey. That's a thirty-nine percent response rate—not perfect, but enough to see patterns. The contrast in exposure was stark. In Toronto, about seven in ten participants said they'd had contact with SARS patients, and nearly half had experienced quarantine. In Hamilton, quarantine was rare—about two in a hundred. The staffing mix differed a bit too: Hamilton's sample skewed more toward nurses. To check who they were hearing from, the team even did a separate, short representativeness survey with two hundred fifty-eight Toronto workers. People who had cared for SARS patients were more likely to take part in the main study. Yet, in terms of age, job type, years in healthcare, and how significant the SARS impact felt, participants and nonparticipants looked similar. That matters because it helps us separate "who answered" from "what actually happened." What did they measure? Three key outcomes. First, the outcomes you care about if you're running a hospital or trying to sleep after a hard shift: posttraumatic stress, general psychological distress, and burnout. They used standard tools—Impact of Event Scale for trauma, Kessler-10 for distress, and the emotional exhaustion component of the Maslach Burnout Inventory for burnout. They did not stop there. They also asked about the job itself after SARS: did people pull back from face-to-face patient care, cut their hours, or change their behavior in ways that interfered with work? Second, the potential buffers and tripwires in the system: did staff feel they got adequate training, protection, and support? Did they feel stigma or interpersonal avoidance? How intense was job stress? And third, the personal habits and styles that sometimes help and sometimes don't. These include coping strategies—problem-solving versus escape—and attachment insecurity, particularly attachment anxiety, which is a tendency to worry about being rejected or unsupported. The analytic plan was straightforward but careful. Think of it as building a map from exposure to outcome. They compared Toronto and Hamilton on those outcomes and looked at how mediators—coping style, training and support, job stress, stigma, and attachment—related to the outcomes. Then they fit regression models to see how much of the variation in distress and burnout those mediators could explain. It's not an experiment, so causality is inferred, not proven. But the Toronto-Hamilton pairing, inside the same system at the same time, gives the comparisons more bite than most surveys of this kind. Here's the headline: more than a year after the outbreak, Toronto's healthcare workers were doing worse. Burnout was reported by about thirty percent in Toronto compared with nineteen percent in Hamilton. General psychological distress ran at forty-five percent in Toronto versus thirty percent in Hamilton. Posttraumatic stress symptoms were more common too—roughly fourteen percent versus eight percent. These aren't small differences. They are the kind of shifts that, if you're a chief nursing officer or a residency director, you feel on the floor and see in scheduling grids. And those psychological differences bled into how the work got done. Toronto workers were about twice as likely to say they'd reduced face-to-face patient contact—about seventeen percent compared with eight percent. Stress-linked behaviors ticked up: roughly one in five in Toronto reported increases in smoking, drinking, or other habits that get in the way at work. That compared with about one in twelve in Hamilton. Absenteeism told the same story. About twenty-two percent of Toronto respondents reported missing four or more shifts due to stress or illness, versus roughly thirteen percent in Hamilton. Work hours were cut more often in Toronto too. When you add that up at the ward level, it looks like thinner staffing, more floating, and more handoffs. That's not just a wellness problem; it's a care delivery problem. When you zoom out to combinations of problems, the burden shows clusters. In Toronto, about sixty-eight percent reported more than one adverse outcome; in Hamilton, about fifty percent did. For more than two problems at once, it was forty-four percent versus twenty-three percent. That's not just a tail of severe cases driving the averages; it's a broader swell. Now, the part that makes this more than a city-versus-city scoreboard: what explained the differences? Two forces kept showing up across analyses. On the personal side, maladaptive coping—things like avoidance, self-blame, or confrontational responses—tracked with worse outcomes. On the organizational side, how adequate people felt their training, protection, and support had been acted like a buffer. Depending on the outcome, those two together explained a meaningful chunk of what separated people who were struggling from those who were managing—on the order of one-fifth to one-third of the variation. In the models for psychological distress, attachment anxiety added vulnerability, while longer experience in healthcare had a protective effect. That last point rings true to many clinicians. With years comes a thicker skin and a deeper toolkit, not for avoiding strain, but for metabolizing it. There's a subtle, important thread here about time. In Toronto, the longer people felt a heightened sense of risk after the outbreak ended, the worse they did. That duration of perceived risk correlated with maladaptive coping—a Spearman correlation coefficient of 0.28. It also correlated with lower perceived adequacy of training, protection, and support—a coefficient of 0.27. It also tracked with piling on of problems: as that feeling of danger lingered, the count of adverse outcomes rose, with a coefficient of 0.23. In plain terms, when the system didn't feel solid and personal coping ran toward avoidance, people stayed in crisis mode long after the crisis. And staying in crisis mode has a cost. Other elements were measured and mattered too. Job stress and stigma or interpersonal avoidance aren't just background noise. But in the stepwise models Maunder's team emphasized, the biggest, most consistent levers were coping style and perceived organizational support. Together, they didn't explain everything—no surprise in a messy, human world—but they explained enough to be actionable. It's worth spending a minute on the caveats, because they shape how far we can generalize. The main survey drew a thirty-nine percent response, and people who had cared for SARS patients were more likely to answer. That tilts the Toronto sample toward higher exposure. The data are self-reported; there's no way to independently verify, say, the exact number of fit-testing sessions someone experienced or the true adequacy of personal protective equipment. The measures of coping and attachment are snapshots, not lab tests. This was observational work, not a randomized trial of support programs. Even so, the team built guardrails. That representativeness check in Toronto suggested participants and nonparticipants were similar on core demographics and on their overall sense of SARS's impact. A subset of one hundred eighty-seven workers completed a deeper second survey on mediators. They were older and more experienced—average age forty-five versus forty-one, about twenty-one years in healthcare versus sixteen—but otherwise looked like the broader sample on job type, city, and exposure. Limitations don't disappear with that context, but the results don't evaporate either. So what does all this add up to? First, exposure mattered. Working in hospitals that treated SARS patients left a psychological and occupational footprint that was still visible a year and a half later. Second, that footprint wasn't just a product of the virus or the news cycle. It was shaped, in measurable ways, by how people coped and how well the workplace prepared and supported them. That's encouraging, because those are levers you can move before the next emergency. Training and protection aren't just infection-control checkboxes; they're psychological armor. Coaching people away from avoidance and blame toward problem-solving and seeking support isn't just soft skills; it's risk reduction. There's also a lesson about experience. Maunder and colleagues saw a protective effect of longer time in healthcare for psychological distress. That suggests a role for mentorship, not just in clinical techniques, but in how to face protracted uncertainty. The person who's been through a few storms can lend you some ballast in the next one. Finally, the occupational ripple effects deserve attention. If a city like Toronto saw roughly a fifty percent rise in distress and a doubling of people pulling back from aspects of clinical practice, and if that persisted for more than a year, the planning horizon has to stretch beyond the acute phase. Recovery isn't just about ventilators and backlogs. It's about staffing patterns, mental health resources, and the daily friction of getting the work done when a meaningful minority of your workforce is depleted. Maunder's study doesn't claim to have all the answers. It doesn't need to. It took a rare moment in a quasi-experimental context and used it to show, with numbers and nuance, that the long tail of an outbreak isn't inevitable. It bends with preparation and support, and it sharpens with avoidance and insecurity. So, the next time we talk about readiness, it can't just be about stockpiles and surge plans. It has to be about training people well enough that they feel protected and supporting them in ways they can feel on the unit. It also has to be about teaching the coping habits that help you put the crisis down when it's time to go home. That's not speculation; that's the through line of what Maunder and colleagues found. The speculation is brief: build those buffers early, before the alarms sound. Because when the alarms do sound, the data say the effects will last longer than the news cycle. And the things we do—or fail to do—in the heat of the moment will echo for years, where it matters most, in the minds and on the floors of the people who show up.

For the people working those wards, the adrenaline fades long before the memories do. So the question Maunder and colleagues asked was simple yet tough: when the dust settled—more than a year later—what had stuck? And why did it stick for some people and places more than others?

They used a natural experiment that almost never happens in real life. Toronto hospitals treated patients with severe acute respiratory syndrome, while Hamilton hospitals, less than an hour down the highway and inside the same provincial health system, did not. Same rules, same precautions, but different exposure.

Thirteen to twenty-six months after the last SARS patient left the hospital—on average, about nineteen months—Maunder's team surveyed healthcare workers across thirteen sites, nine in Toronto and four in Hamilton. Out of one thousand nine hundred eighty-four people invited, seven hundred sixty-nine completed the main survey. That's a thirty-nine percent response rate—not perfect, but enough to see patterns.

The contrast in exposure was stark. In Toronto, about seven in ten participants said they'd had contact with SARS patients, and nearly half had experienced quarantine. In Hamilton, quarantine was rare—about two in a hundred.

The staffing mix differed a bit too: Hamilton's sample skewed more toward nurses. To check who they were hearing from, the team even did a separate, short representativeness survey with two hundred fifty-eight Toronto workers. People who had cared for SARS patients were more likely to take part in the main study.

Yet, in terms of age, job type, years in healthcare, and how significant the SARS impact felt, participants and nonparticipants looked similar. That matters because it helps us separate "who answered" from "what actually happened."

What did they measure? Three key outcomes. First, the outcomes you care about if you're running a hospital or trying to sleep after a hard shift: posttraumatic stress, general psychological distress, and burnout.

They used standard tools—Impact of Event Scale for trauma, Kessler-10 for distress, and the emotional exhaustion component of the Maslach Burnout Inventory for burnout. They did not stop there. They also asked about the job itself after SARS: did people pull back from face-to-face patient care, cut their hours, or change their behavior in ways that interfered with work?

Second, the potential buffers and tripwires in the system: did staff feel they got adequate training, protection, and support? Did they feel stigma or interpersonal avoidance? How intense was job stress?

And third, the personal habits and styles that sometimes help and sometimes don't. These include coping strategies—problem-solving versus escape—and attachment insecurity, particularly attachment anxiety, which is a tendency to worry about being rejected or unsupported.

The analytic plan was straightforward but careful. Think of it as building a map from exposure to outcome. They compared Toronto and Hamilton on those outcomes and looked at how mediators—coping style, training and support, job stress, stigma, and attachment—related to the outcomes.

Then they fit regression models to see how much of the variation in distress and burnout those mediators could explain. It's not an experiment, so causality is inferred, not proven. But the Toronto-Hamilton pairing, inside the same system at the same time, gives the comparisons more bite than most surveys of this kind.

Here's the headline: more than a year after the outbreak, Toronto's healthcare workers were doing worse. Burnout was reported by about thirty percent in Toronto compared with nineteen percent in Hamilton. General psychological distress ran at forty-five percent in Toronto versus thirty percent in Hamilton.

Posttraumatic stress symptoms were more common too—roughly fourteen percent versus eight percent. These aren't small differences. They are the kind of shifts that, if you're a chief nursing officer or a residency director, you feel on the floor and see in scheduling grids.

And those psychological differences bled into how the work got done. Toronto workers were about twice as likely to say they'd reduced face-to-face patient contact—about seventeen percent compared with eight percent. Stress-linked behaviors ticked up: roughly one in five in Toronto reported increases in smoking, drinking, or other habits that get in the way at work.

That compared with about one in twelve in Hamilton. Absenteeism told the same story. About twenty-two percent of Toronto respondents reported missing four or more shifts due to stress or illness, versus roughly thirteen percent in Hamilton.

Work hours were cut more often in Toronto too. When you add that up at the ward level, it looks like thinner staffing, more floating, and more handoffs. That's not just a wellness problem; it's a care delivery problem.

When you zoom out to combinations of problems, the burden shows clusters. In Toronto, about sixty-eight percent reported more than one adverse outcome; in Hamilton, about fifty percent did. For more than two problems at once, it was forty-four percent versus twenty-three percent.

That's not just a tail of severe cases driving the averages; it's a broader swell.

Now, the part that makes this more than a city-versus-city scoreboard: what explained the differences? Two forces kept showing up across analyses. On the personal side, maladaptive coping—things like avoidance, self-blame, or confrontational responses—tracked with worse outcomes.

On the organizational side, how adequate people felt their training, protection, and support had been acted like a buffer. Depending on the outcome, those two together explained a meaningful chunk of what separated people who were struggling from those who were managing—on the order of one-fifth to one-third of the variation. In the models for psychological distress, attachment anxiety added vulnerability, while longer experience in healthcare had a protective effect.

That last point rings true to many clinicians. With years comes a thicker skin and a deeper toolkit, not for avoiding strain, but for metabolizing it.

There's a subtle, important thread here about time. In Toronto, the longer people felt a heightened sense of risk after the outbreak ended, the worse they did. That duration of perceived risk correlated with maladaptive coping—a Spearman correlation coefficient of 0.28.

It also correlated with lower perceived adequacy of training, protection, and support—a coefficient of 0.27. It also tracked with piling on of problems: as that feeling of danger lingered, the count of adverse outcomes rose, with a coefficient of 0.23. In plain terms, when the system didn't feel solid and personal coping ran toward avoidance, people stayed in crisis mode long after the crisis. And staying in crisis mode has a cost.

Other elements were measured and mattered too. Job stress and stigma or interpersonal avoidance aren't just background noise. But in the stepwise models Maunder's team emphasized, the biggest, most consistent levers were coping style and perceived organizational support.

Together, they didn't explain everything—no surprise in a messy, human world—but they explained enough to be actionable.

It's worth spending a minute on the caveats, because they shape how far we can generalize. The main survey drew a thirty-nine percent response, and people who had cared for SARS patients were more likely to answer. That tilts the Toronto sample toward higher exposure.

The data are self-reported; there's no way to independently verify, say, the exact number of fit-testing sessions someone experienced or the true adequacy of personal protective equipment. The measures of coping and attachment are snapshots, not lab tests. This was observational work, not a randomized trial of support programs.

Even so, the team built guardrails. That representativeness check in Toronto suggested participants and nonparticipants were similar on core demographics and on their overall sense of SARS's impact. A subset of one hundred eighty-seven workers completed a deeper second survey on mediators.

They were older and more experienced—average age forty-five versus forty-one, about twenty-one years in healthcare versus sixteen—but otherwise looked like the broader sample on job type, city, and exposure. Limitations don't disappear with that context, but the results don't evaporate either.

So what does all this add up to? First, exposure mattered. Working in hospitals that treated SARS patients left a psychological and occupational footprint that was still visible a year and a half later.

Second, that footprint wasn't just a product of the virus or the news cycle. It was shaped, in measurable ways, by how people coped and how well the workplace prepared and supported them. That's encouraging, because those are levers you can move before the next emergency.

Training and protection aren't just infection-control checkboxes; they're psychological armor. Coaching people away from avoidance and blame toward problem-solving and seeking support isn't just soft skills; it's risk reduction.

There's also a lesson about experience. Maunder and colleagues saw a protective effect of longer time in healthcare for psychological distress. That suggests a role for mentorship, not just in clinical techniques, but in how to face protracted uncertainty.

The person who's been through a few storms can lend you some ballast in the next one.

Finally, the occupational ripple effects deserve attention. If a city like Toronto saw roughly a fifty percent rise in distress and a doubling of people pulling back from aspects of clinical practice, and if that persisted for more than a year, the planning horizon has to stretch beyond the acute phase. Recovery isn't just about ventilators and backlogs.

It's about staffing patterns, mental health resources, and the daily friction of getting the work done when a meaningful minority of your workforce is depleted.

Maunder's study doesn't claim to have all the answers. It doesn't need to. It took a rare moment in a quasi-experimental context and used it to show, with numbers and nuance, that the long tail of an outbreak isn't inevitable.

It bends with preparation and support, and it sharpens with avoidance and insecurity. So, the next time we talk about readiness, it can't just be about stockpiles and surge plans. It has to be about training people well enough that they feel protected and supporting them in ways they can feel on the unit.

It also has to be about teaching the coping habits that help you put the crisis down when it's time to go home.

That's not speculation; that's the through line of what Maunder and colleagues found. The speculation is brief: build those buffers early, before the alarms sound. Because when the alarms do sound, the data say the effects will last longer than the news cycle.

And the things we do—or fail to do—in the heat of the moment will echo for years, where it matters most, in the minds and on the floors of the people who show up.

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