Abstract
Katherine Hoffman is a biostatistician in the pulmonary and critical care team of a New York City hospital, who found herself part of the Covid-19 response when the outbreak first hit in March 2020. This is her story
When I received my master's degree in biostatistics from the University of Michigan almost 3 years ago, I possessed various skills. I could plot high-dimensional data and code complex statistical models, but more importantly, I knew how to consider many angles of a problem, choose the best approach, methodically analyse data, and present the final evidence as clearly and objectively as possible.
As I excitedly made plans to leave my small Michigan hometown for biomedical research in New York City, I had no inkling that there would soon be a time when I would need skills for which I was not explicitly trained. I did not realise I might one day need to provide immediate, incomplete answers using rapidly accumulating information on a new and unknown disease; did not foresee I would be asked to do this as bodies piled up in mobile morgues near my apartment faster than I could download new data. Yet that is exactly what happened, less than 2 years after I graduated, when New York became one of the early epicentres of the Covid-19 pandemic in the spring of 2020. As a biostatistician for a team of pulmonary and critical care physicians at a Manhattan hospital, I was part of the first wave of scientists to work on emergency response efforts.
I found these months of my life – filled with so much sadness and so little sleep – difficult to articulate even once the number of cases decreased to a manageable level in the summer. Like trying to recount a nightmare moments after you have been shaken awake, my description of reality, if spoken aloud at all, sounded nonsensical, dramatised, even untrue. The words of a New York City pulmonary and critical care fellow, Dr Colleen Farrell, echoed in my mind: “I find myself instead longing for the horror and devastation of this crisis to be seen and acknowledged for what it really is. I don't want to be soothed so much as believed”(bit.ly/38qMhvx).
While I knew my experience paled in comparison with that of those on the frontline, I decided to write about it. I share it because I believe that if you were “on the sidelines” of Covid-19 response efforts, as I was, pieces of my story are likely to resonate. Even though your own experience will have variations, you will understand the chronology of escalation, desperation, and exhaustion. If you were not involved, I hope you might live it, feel it, if only for a few minutes and through the eyes of one young statistician in her tiny Manhattan apartment.
There were and are so many of us: thousands of scientists crunching numbers and creating models and pipetting molecules to contribute to treatments and vaccines and decisions on resource allocation. We tried our best. Even when it was not enough – and it was, so often, not enough – we gave it our all, and our work carried its own emotional burden.
My story begins at the desk of my first job as a biostatistician at Weill Cornell Medicine. It is over a year before Covid-19 reshaped life as we knew it.
15 December 2018. My coworker is moving to California. She's a statistician for a group of pulmonary and critical care physicians at our New York City hospital, and I'm a statistician who's trying not to do too many things wrong, only 3 months into my first job out of school. “I think you'd be good with this research team,” she tells me. “There's some really interesting studies on lung diseases.” I nod, because that's what you do when you've been at your job for 3 months.
I take over her projects and start learning organ failure scoring systems, criteria for acute respiratory distress syndrome, and the differences between invasive and non-invasive mechanical ventilation. My close friend does cutting-edge cancer statistics, and I feel a bit resentful. Nobody ever wants to hear about the controversial definitions of sepsis at family parties.
As the months pass by, I slowly build my mental encyclopedia and begin to embrace my role as a pulmonary and critical care biostatistician. I do not consider – indeed, I could not have comprehended – how valuable this domain knowledge would soon become.
5 March 2020. A full year and 3 months later, I wake up very sick. It is the kind of sick where you cannot do anything but curl up on your bathroom floor and let being sick consume you. Too sick to read, too sick to sleep. I spike a fever and can hardly move for 2 days before I hobble to the doctor's office and nearly faint mid-exam. The doctor insists I stay until I drink an entire bottle of water. “Is there anyone to check on you at home?” he asks, concerned. No, no, I'll be fine.
By the end of the week my fever breaks and I'm back to work. It's early March, so “coronavirus?!” is everyone's first question. They're all joking, except the pulmonologists I work with. Nothing respiratory, I assure them. One isn't convinced. “Some young people don't feel short of breath. It is possible to have Covid-19 with no respiratory symptoms at all,” she tells me. Months later, I'll read that as the headline of various news articles, but at the time, no testing is available to me.
17 March 2020. Barely 2 weeks pass before the number of confirmed Covid-19 cases explodes in New York City. Restaurants are instructed to close the day before St Patrick's Day, my birthday. I can't meet up with my friends anymore, so I cook macaroni and cheese and run to Central Park to watch the sun set behind skyscrapers. My grandparents call me, and they make Happy Birthday sound like a hymn from a Catholic mass and I laugh, and it is the only part of my day that feels like every other birthday.
While I'm leaving the park, my mum texts me that she hopes I had a good day. Any other year it would be strange for her to nearly miss my birthday, but this year she is working long hours. She's a nursing director back in Michigan and her hospital is already preparing for their own impending Covid-19 outbreak. The preparations will not be in vain.
As I jog home, I pass a sign asking former health-care workers to volunteer to take care of NYC Covid-19 patients. Before I began my career in biostatistics, I worked at a hospital caring for acutely ill patients, so I sign up without hesitation. My misguided logic is that the rising numbers of Covid-19 cases will make my critical care collaborators too busy to pursue their research, and this seems like the best way for me to help as the world descends into chaos. While I fill out the online contact form, I wonder what it will feel like to take care of patients again. I look up YouTube videos to refresh myself on drawing blood and inserting IVs.
How absolutely crazy that I thought my biostatistics training wouldn't be useful.
22 March 2020. I'm a pulmonary and critical care team's statistician, so naturally I am one of the first analysts at my hospital pulled into Covid-19 work. It starts with a text on a Sunday – the first of many – from a pulmonologist: “Where's the description of our ICU [intensive care unit] database, Kat?” Our informatics team is using the structure of the ICU database I work with as part of a Covid-19 tracking repository for our entire hospital. Within days, I am told to drop all of my other research projects for Covid-19 work.
The first request for me is straightforward: summarise the laboratory results from our first 300 Covid-19 patients. Three hundred patients at our hospital! That's insane, I think to myself. It seems only a week ago the news reports said there were 300 people in the entire city with Covid-19. I begin working through issues linking the databases, identifying missing information, and explaining critical care jargon to other analysts. Each morning I pull new data and watch the files grow exponentially larger.
There are countless questions flooding in from all over the hospital. Most of them revolve around “who will get intubated, and when?” My hospital, like so many other hospitals in NYC, is on track to run out of ventilators soon. My attendance becomes mandatory at multiple “risk prediction” meetings each week. I find myself in charge of extensive data cleaning and then writing code for models to answer vague and terrifying questions: we need to figure out which patients will “crash,” who can be transferred, and, if we run out of ventilators, who has the best chance to survive.
I am a junior researcher, previously unconcerned with hospital operations, suddenly confronted with the task of providing rapid answers for potentially immediate decision making. I accept my new role with the utmost seriousness. My days, normally spent coding with double monitors at a proper desk, suddenly fill with online meetings from 8:30 a.m. until 5:00 p.m. from a laptop at my kitchen table. Each night after the meetings end, I take advantage of the relative quiet to code into the early hours of the morning.
For several weeks I use the long, uninterrupted hours of weekends to work, waking up with the sun and continuing on until at least 11 p.m., with few breaks in between. On some nights I send my mum “good morning” texts at 5 a.m. “Are you waking up early or have you not slept yet?” is always her first question. The next is, “No fever? No cough?” She is worried about me, living in the international epicenter of this pandemic, but I'm just as worried about her, working at a hospital every day. She informs me that my dad is sleeping in my old bedroom in case she brings the virus home.
Hospitals around the city begin to call me, wanting to know if I can still help care for Covid-19 patients, as I had previously volunteered to. I tell them: “I want to, but I can't, I'm so sorry, I'm helping with Covid data now.” It sounds and feels inconsequential.
4 April 2020. My best friend and her sister are also nurses in Michigan. I videocall her to check in. She and her sister's units have become “hot floors”: every room is filled with a Covid-19 patient. They were living with their parents, another sister, uncle, and cousins, but both have moved to rental accommodation for the foreseeable future. “It's so crazy here, Kitty,” she tells me in a defeated voice. At the time, Michigan's case trajectory is second only to New York's.
One of her nursing friends has been hospitalised with Covid-19 and is on 6 litres of oxygen. I can't help but think about the prediction models I've been working on. I mentally run his characteristics through them. I know what my models would estimate his probability of intubation to be.
I listen to her talk about the N-95 masks they've been given. “Remember how they used to say those were one-time use?” she asks me. I do. “They started telling us they were good for the whole day, and then they said they'd be good for the whole week, and now they're saying we might have to start sharing.” I wonder what data analyst, perhaps just like me, is crunching those numbers and feeding the information to hospital administration. “The virus is so terrible. I've never done so much post-mortem care, zipped so many body bags…” Her voice drifts off.
I feel guilty, on the sidelines. I see the raccoon eyes – the only part of their faces visible between hair caps and procedure masks – of the physicians I spend all day hopping on and off meetings with, and I desperately want to help. I cannot hold the hand of a Covid-19 patient, but I have all their data at my keystrokes: lab results, vital signs, and procedure codes. I see their inflammatory cytokines spike, I watch their oxygen levels plummet, I can tell you which organs are failing, who's on which experimental drug, and who's just been made Do Not Intubate and Do Not Resuscitate. I follow in horror, almost in real time, the time-stamps of admission, intubation, death. I cannot compare this experience to physically caring for Covid-19 patients, but I feel haunted by it all the same.
I hole up in my tiny studio apartment in Manhattan for days at a time, listening to the wails of ambulances and pings of messages from my computer. I see only one friend with any frequency; we both live alone, 18 blocks from each other. She texts me often, asking to meet in Central Park. She suspects I am not doing well, and she is right. I walk with her all over the Upper East Side a few times a week, each of us donned in our black cotton masks. We try not to talk about Covid-19, but it's hard to avoid when our walks take us past the pop-up ICU tents and refrigerated trucks that stretch entire blocks – the overflow morgues for NYC's dead.
We try to time our walks so that we're outside at 7 p.m., when the city unites to cheer for health-care workers. If I'm not out walking with her, I climb religiously onto my fire escape to clap. Sometimes a man in the apartment across the street sings Sinatra: “I want to wake up in the city that never sleeps… New York, New York!” I've only lived here 2 years, but I miss “the city that never sleeps” so badly that it hurts.
Life continues in this way for me, with no real sense of time or distinguishing events, from mid-March until early May.
10 May 2020. It is Mother's Day, and my 50th straight day of working with Covid-19 data. At 11 p.m., my cell phone goes off. It is an ominous vibration against my kitchen table, where I am perpetually sitting with my laptop whirring. “Hi, honey… I just wanted to let you know that, mmm…” It's my mum, and her voice is cracking. I finish the sentence for her. “Aunt Peggy died?” I ask, sadly. “Yes.” “Okay. Thanks for letting me know.” I stare into the white brick wall in front of my kitchen table for so long that I start seeing multicoloured spots.
My grandfather's eldest sister, my Aunt Peggy, had begun showing telltale symptoms of Covid-19 and tested positive only a few days previously. She'd been without any visitors in her assisted living home for months due to isolation restrictions. She was royalty in our family; the red-lipsticked, always fashionably late, prized guest at every family party. She had an unforgettable, incredibly sweet voice, and I can still hear her words to me last Christmas. “How's New York, Katherine? I'm so proud of you.” She was the first nurse in my family, and she influenced my mum to become a nurse, who influenced me to pursue medical research. The matriarch of our family left us on Mother's Day.
I spend the night trying to find a rental car company that will allow me to drive one-way from New York to Michigan. It can't be done; I am several weeks too late in my exodus from the city. I book a flight instead and leave a few days later on a near-empty plane to spend time with my family. I plan to stay in Michigan for 2 weeks, but I don't leave for 2 months.
20 September 2020. The leaves I watched bud in Central Park during my walks this spring are changing to red and gold. As I write this, I think of countless other ways I could attempt to explain what my tiny corner of the world was like during NYC's outbreak. Most are too personal to ever record. At the same time, it is difficult to share even the memories I have, partially because I know they are incomparable to those of the frontline workers who risked their lives every day.
My experiences of living and working in Manhattan during March, April, and May will stick with me forever. I hope there comes a day that I can meet in real life – mask-free – all the analysts, hospital administrators, physicians, residents, fellows, medical students, and data engineers I conversed with so frequently during the height of the outbreak. At the same time, I hope we never have to work together again. It is a wish that I fear will not come true.
Just this past week I attended a meeting with our informatics team. “It's good to ‘see’ everyone again,” someone said. It's only half true; the circumstances that bring us to meetings together are never good. We discussed data structures for a possible second wave of Covid-19 in NYC as schools and indoor dining reopen. After the call, I felt an immense sadness, despite being in a much better place than when I left the city in May.
At the bottom of my heart, I don't know if I can handle another round of it all. Can you? ■
Disclosure statement
The author declares no conflicts of interest.