Most who from and other common mental disorders don’t get treatment.

Often because they can’t. In many communities, there are few (if any) providers to pay, even if you could afford the bill. More than 3 billion of us live in a country where there is only 1 psychiatrist for every 200,000 people.¹ Psychologists are in even shorter supply.

This is a big deal. Depression is the leading cause of disability worldwide, and neuropsychiatric disorders more broadly account for nearly 1 in 5 years lived with disability globally.² More than 300 million people worldwide are thought to suffer from depression or anxiety.³

Depression affects everyone, but pregnant women and new moms are a special group.

When a woman experiences mood and anxiety symptoms during pregnancy or the year following delivery, we call it perinatal depression. Estimates of prevalence vary by study and setting, but generally perinatal depression is thought to impact about 1 in 10 women in high-income countries and about 2 in 10 women everywhere else.⁴ Untreated depression carries with it increased risk for suicide and a host of bad outcomes for kids. And as the following chart reaffirms, most cases go untreated.

Drop-off in care among women with perinatal depression in mostly high-income(!) settings. (Source: Adapted from Gavin et al. (2015). Is population-based identification of perinatal depression and anxiety desirable? In Milgrom & Gemmill (Eds.) Identifying Perinatal Depression and Anxiety. Wiley: Sussex.)

So here we have a situation of immense mental health needs and, in many places, almost no way to meet these needs. What is the solution? Training more providers and lowering social and economic barriers to treatment would be a good start, but it won’t be enough.

One promising idea is to train ordinary folks to deliver basic care.

It’s called task shifting or task sharing. For instance, psychiatrist Dixon Chibanda and his team trained grandmothers to sit on park benches in Zimbabwe—Friendship Benches—and listen to people suffering from anxiety and depression. The 6-month results were impressive.⁵

Or look at program called Thinking Healthy developed in Pakistan by psychiatrist Atif Rahman and colleagues to treat perinatal depression. In this program, community health workers called Lady Health Workerswomen with high school educations and basic training in prevention and health promotion—were trained to deliver a psychosocial intervention based on the principles and techniques of cognitive behavior therapy. CBT is a common and effective approach for treating common mental disorders worldwide. Rahman reported that the Thinking Healthy program halved the rate of depression 6-months post-intervention in a randomized controlled trial,⁶ though a follow-up study conducted about 7 years later suggests that the effects might be short-lived.⁷

But how do we go from studies of 100s to programs that reach 1,000,000s?

There is no one approach that will close the yawning gap between mental health needs and resources, but I believe technology will play a role in scale up. Here’s why.

Compared to other great public health implementation challenges that society has tackled in a very off-line way, such as the eradication of Small Pox, mental health treatment is relatively high touch—meaning a lot of repeated contact with the same clients. For instance, the Thinking Healthy program has a Lady Health Worker visit a single client at her home 15 times. At scale, this would be harder in many ways than delivering one-off vaccinations (which are already hard to do!). You have to deal with scheduling, transportation, supervision, client stigma, etc. So just as technology helps all complex systems operate more efficiently and effectively, it seems reasonable to assume that technology can help scale up traditional and task-sharing models of care.

Is there a bigger role for technology in mental health treatment? (tldr: yes!)

Some people will tell you that we are on the cusp of some truly amazing advances in hardware, software, and data science that will change how medicine is practiced. That artificial intelligence will usher in a new era in health care.

Other people will tell you:

(The irony in showcasing this tweet is that Dr. Shaw is a bit of an AI enthusiast.)

My view is that it would be foolish to claim AI will solve every problem, but dismissing the potential of AI as empty hype is misguided. AI will impact the way we detect and treat many illnesses.

But what should we think about mental health chatbots?

“The chatbot will see you now.”

This headline is used so often these days that you might think bots are writing the news. But if you can get beyond the headline hype, you’ll find that there is a lot of interesting AI work underway with the potential to create a completely new channel for mental health treatment.⁸

For instance there is Woebot, an automated conversational agent that will talk with you via Facebook Messenger or its apps for iOS and Android. Woebot made the news recently when AI pioneer Andrew Ng announced he joined Woebot’s Board of Directors, a move that should give everyone working on AI+mental health a boost of confidence.

There is also Tess, a creation of Michiel Rauws and the team at X2AI. Tess is a customizable chatbot that’s available 24/7 to talk with you via Facebook Messenger or SMS.

Both chatbots are designed to deliver CBT-inspired mental health support, and both are developing an evidence base. In two different studies with college students in the US, Woebot and Tess demonstrated similar effects on symptoms of depression on the order of 10–20% reductions.⁹ This is encouraging, especially when you consider the low cost to deliver such services. Even with modest impacts, the cost-effectiveness of these digital tools could be quite high relative to traditional or task-shifting models.

So my prior is that chatbots make a lot of sense for mental health treatment, especially in settings where you share your neighborhood psychiatrist with 199,999 other people.

But could chatbots really work in Kenya?

I had been nibbling around this question for a few years before I really even knew what a chatbot was. In the process of trying to develop a screening and referral system for maternal and newborn health a few years back, we observed that Kenyan women reported more depressive symptoms to an automated voice line than they reported to our human study assistants. In a not-so-shocking result, women seemed very comfortable discussing private issues in private. This led us to think about how we could use technology to improve screening and treatment for perinatal depression.

Start with screening

If you glance again at the chart presented earlier, you’ll see the basic fact that was smacking us in the face at the time: fewer than 50% of cases of perinatal depression are ever recognized clinically, meaning that most women with symptoms never get screened by a professional. This number comes from high-income settings, so the situation is likely a lot worse in places like Kenya.

One reason for a lack of screening is a lack of validated tools to detect depression. Most of today’s commonly used instruments were developed in high-income settings, and research shows that simply running items through Google Translate does not guarantee their validity for measuring depression. It takes work to demonstrate that a questionnaire can discriminate between cases and non-cases and thus have value as a screening instrument, so that’s where we started—with a validation study.

But as many people will tell you, screening without access to treatment does not improve outcomes. This is why most countries do not have policies of universal screening for perinatal depression.¹⁰ There are few options for treatment. We wanted to help change this for women in Kenya and beyond.

Next, find a program and a platform to adapt

We looked at the early success of Thinking Healthy and thought that this CBT-based program could be a natural fit for Kenya. In 2015, the WHO made a general version of the curriculum and incorporated it into the mhGAP evidence-based toolkit to promote wide-scale adaptation and use.

Thinking Healthy also seemed like a natural fit for X2’s Tess platform. Tess can work over SMS¹¹ and is built for CBT interventions like Thinking Healthy. X2 was already working to expand access to treatment around the world, and the company was not afraid of hard challenges like adapting the platform for Syrian refugees in Lebanon. This made our choice simple.

So we reached out to Michiel at X2AI…

| Part 2 : User-centered design and testing |





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