Can Math Models Predict—and Prevent—the Next Suicide?
Mental health is, in some ways, the black sheep of public health: chronically underfunded, undervalued, and overlooked. One reason has to do with the social stigma surrounding mental health. After all, it was long considered gauche to seek treatment for issues like depression (while tuberculosis is a much sexier malady). An even bigger reason has to do with the complexity of deciding where to channel mental health resources—a process known as psychiatric epidemiology.
As computer engineers continue to churn out more and more powerful algorithms, complicated systems models have become a cornerstone of epidemiology. These programs take in massive amounts of data and combine it with a specific set of parameters to map out public health concerns, identifying which geographic regions or demographics are most at risk from a given malady.
Most epidemiological models have been restricted to infectious diseases, forecasting the spread of illnesses like malaria and COVID. But some researchers believe that it’s time to apply these tools to mental health services too. These scientists aim to create models to predict where issues like severe depression and suicide are most likely to crop up—and which interventions are most effective. By doing this, they hope to correct some of the mental health funding disparity.
The challenge: getting policy makers, psychiatrists, and the general public on board.
This is the issue that occupies most of Jo-An Occhipinti’s time. As a data scientist at the University of Sydney in Australia, she was inspired after working on models to help control malaria outbreaks in the Southwest Pacific. Applying the same approach to mental health policy seemed like the logical next step to her. “I don’t think people realize how central mental health is to a good functioning of society,” Occhipinti told the Daily Beast.
However, for many, COVID-19 has thrust mental health back into the conversation. A recent survey from the World Health Organization found that the pandemic triggered a 25 percent increase in depression and anxiety diagnoses worldwide. With this rise in awareness comes renewed interest in investing in mental health as public health. But it can be difficult to know where to start for public health institutions.
“Everything seems to be important for mental health, which makes it challenging—and fun—to study,” Daniel Eisenberg, a mental health policy specialist at the University of Michigan, told the Daily Beast. Socioeconomic status, brain chemistry, family ties, and overall physical health are just a few factors that can put a person at higher or lower risk of depression. Because of this, some epidemiologists and clinicians believe that mental health is simply too multifaceted to study using models.
However, Occhipinti doesn’t see this complexity as a barrier to effective systems modeling—it just means that the models need to take such factors into consideration. To do this, Occhipinti and her team work closely with people who have personal experience with the issues they’re modeling. She recalls one instance in 2017, when her team was working on a model for suicide risk in the hopes of developing more supportive infrastructure for suicide prevention and recovery. There was just one problem: The model wasn’t working. No matter how much data they fed it, the results didn't seem to line up with reality. “We just didn’t know what was missing,” Occhipinti recalled. “We thought we had everything.”
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So they asked for input from people with lived experience. Things clicked when someone pointed out that they were missing an element called the “black hole,” a percentage of patients who receive immediate but inadequate care and bounce off of long-term treatment. Accounting for that variable, Occhipinti said, changed the model completely. Suddenly, the simulation and the real world aligned. “Once we put that missing piece in, the model just fell into place. It was really quite amazing.”
Of course, effective models only matter if you can use them to shape policy. “We might have these fancy models and so on, but we don’t want them to sit in an ivory tower,” Patricia Mabry, a systems scientist at the nonprofit HeathPartners Institute, told The Daily Beast. “We want them to be used.”
Mabry works closely with policymakers from the National Health Institute to the Food and Drug Administration to develop public health guidelines based on detailed scientific models. This type of work is crucial, said health economist Steven Dehmer, because it allows scientists to project the impact of a health crisis without having to put the public in danger. “Especially when you need to make decisions in the absence of other data, it can really be helpful,” he told the Daily Beast.
Dehmer, who also works with the HealthPartners Institute, builds models based on a combination of clinical data and personal experience to help predict everything from the prevalence of lung cancer associated with smoking to the impact of high sodium in fast food on heart attack risk. Like mental health, these models rely heavily on sociological factors and individual behavior, and they can be fine-tuned based on ongoing input. Dehmer and Mabry see potential in using models to inform mental health policy as well—provided that the intended recipients buy in.
“It can be hard to convince a patient to make a change, or do something that they don’t believe in,” Mabry said. “You’re not going to be able to do it with just ‘Oh, trust me.’” That’s where approaches like Occhipinti’s, which take community input into consideration, really shine.
A recent pilot program in Melbourne, Australia, used Occhipinti’s dynamic models to identify individuals at high risk of death by suicide (participants had at least one prior suicide attempt). The program provided 11 intervention options, ranging from removing potentially dangerous objects from the participants’ homes, to screening for substance abuse, to setting up community support networks. Not a single one of the 40 participants died for the duration of the trial. Bolstered by this success, the Australian government has since invested an additional $2.3 billion in suicide prevention.
Even when good mental health services are available and public trust is high, however, some folks may not take advantage. “A lot of people are just not assigning enough priority to their mental health,” Eisenberg said. Others may not have enough free time or money to spend on mental health care, while still others may be deterred by stigma (real or imagined).
But by engaging with people’s lived experience at every step of the process, Mabry and Occhipinti hope that confidence in public health initiatives and awareness of their importance will continue to grow. Right now, pilot programs similar to Melbourne’s are being run in the U.K. and the U.S.—though, whether they result in more government funding remains to be seen.
“This process is as much a human process as it is a technical one,” said Occhipinti. “We need to get the policymakers to see the value of this sort of work.”
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