Come for the donuts, stay for the data!

Maths in the real world...

5/8/2020

Seth Godin doesn't care about SEO savvy headlines and you shouldn't either. Many of my journalist colleagues disagree and I get it. We are only as clickable as our cleverest headline written to bring more and more eyeballs to the table. I am no Seth Godin but I also prefer headlines that mean something to me not the Google algorithms that bring short term interest. A stray piece of conversation or a snippet from a podcast that catches my attention will also do just nicely.

Look down the list at the last few posts--Go Fund Yourself, A Tabula Rasa in Data Literacy, and Generic but Ambitious--what the hell am I talking about? I don't know. Maybe grab a coffee or tea and take a look. Seth teaches us not to be the thing for everyone--just some people.

If you prefer provocative headlines, I didn't make this for you.

Even if you don't think of yourself as a marketer--you are a marketer. Maybe you are the product or maybe your currency is knowledge or expertise but make no mistake we are all selling something.

Seth teaches us how to be effective. If you are indeed bringing value to the table you need to find your tribe, stand in front and say--here. I made this for you. This particular blog captured my attention. Not because I need something shiny to look at like a buzz worthy headline but because I trust I will learn something.

And when does it get boring? is well worth the quick read. I think Seth and I share short attention spans. In fact, I agree with his method--read along until you get the joke. So why not keep it succinct and to the point?

Almost no one who takes an intro to economics course becomes an economist. One reason might be that within a few days of starting the class, it becomes abstract, formula-based and dull.--Seth Godin

I think the problem might be that we no longer remember how to be curious. If you are a writer or do anything where your stories matter, this is a muscle you need to keep flexing.

I read, listen to podcasts, travel, and follow the threads on anything that interests me.

During a hike up in the mountains I saw the most amazing tree.

I immediately texted my childhood best friend to ask her if she too notices trees that would have been perfect hosts for our childhood selves to explore. I was happy to receive an enthusiastic yes within minutes if not seconds.

I think I surprise many when they find out I am a data analyst. I have always had a wide scope of general interests and this serves me well. Believe it or not, there aren't that many of us that can work the entire end to end strategic data process. We need to know the language, the challenges, and the opportunities. Not everyone needs to be niche driven or highly specific. If you are reading this as one of my data friends or colleagues, here is the perfect podcast for you, Should I Become More Technical or Business Focused in Data Science Career.

I began this post in response to a question I field pretty regularly. How much statistics do I need to become a data scientist/analyst/professional? I can't answer that question. Textbooks cram information into your head for an exam. She/he/they who passes the most exams wins and can be perhaps a statistician, mathematician, computer scientist. You need to ask yourself what the long road looks like for you. Where do you want to work? What do you want to be doing?

I have completed numerous classes in statistics but I am not a statistician. Weirdly I am also a member of the American Statistical Association. I need to be. I need to keep learning and applying rigor and comprehension to technical topics.

Once you get out in the real world and discover your unique flair and contribution, the trick is to remain curious. To use the tools that give "us a chance to understand and to figure things out."

Because a testing regime is in place, particularly now when so many other tropes in the education-industrial complex are disrupted, the textbook authors and administrators work together to skip the ‘fluff’ and go straight to the stuff that’s easy to test.--Seth Godin

For example, when I think about data modeling and trying figure out the shape of my data, I think about linear, sinusoidal, or quadratic equations. These questions jump from the pages of math books into practical applications when you have variables to consider and relationships to determine.

Am I the only one considering positive first derivatives when looking at the COVID-19 curves? All the curves were getting bigger but we needed to consider the rates. They can stay the same, increase slowly, or represent what actually was occurring--the rate was increasing quite rapidly. All applications of the math concepts we were forced to memorize to a test in the absence of how they are applied in the real world.

When my boys were small occasionally one or both of them would complain about being bored. My response rarely wavered. I would tell them, "That doesn't sound like being bored, that sounds like a lack of imagination."

And off we would go to have an adventure.

Speaking of adventures...

May 15th we are having a free lunch and learn about demographic data. Register at link Getting comfortable with demographic data.

Data after coronavirus...what survives?

4/4/2020

I rely heavily on art to teach data literacy. To truly be data literate you must ask questions--and question answers.

As a data journalist, I am well aware of pre-attentive attributes and how to focus attention on the bits and baubles. You my friend, need to see the sleight of hand.

How many of us have been furloughed? Conferences cancelled and work placed on pause?

What is missing? Who is missing from the conversations?

Who funded the study? Who benefits from the data pointing this way and not that way?

It is often the independent journalist or artist that finds the tender point--and keeps pressing.

Will they be here when the dust settles? Read the interesting article in Vulture by art critic Jerry Saltz.

I see parallels between larger platforms for news dissemination and corporate art, both in danger of eclipsing the curious, the restless, and the vigilant.

The Last Days of the Art World … and Perhaps the First Days of a New One Life after the coronavirus will be very different.

This optimism has all always made me sure that the art world could, and would, survive anything. But last week, that optimism started to die. Even an art-lover lifer like me has to admit much of the art world infrastructure feels like it’s already in the balance. Some of it may be gone even now. In three months, or six months, or — God forbid — 12 or 18 (there has never been a vaccine for a coronavirus)? There will be galleries on the other side of this chasm, and museums, and artists making work, of course. But I worry that such a sundering will only exacerbate the inequalities that more and more dominate this universe, with megagalleries and art stars surviving and the gap between them and everyone else only widening, rendering the scrappier artists and galleries something close to invisible.--Jerry Saltz

Behind the scenes within our data communities we are being cautioned--rightfully so I might add. For once, data is plentiful and mostly ready for primetime. Professionals with the skills to source and clean data for analysis are left behind as data are cast into the miasma for interpretation.

Epidemiology has a unique vocabulary and science to explain how we talk about what is known and what is yet to be discovered. My college text book, Epidemiology for Public Health Practice, defines epidemiology as the following, "a discipline that describes, quantifies, and postulates causal mechanisms for health phenomena in populations."

A later version (5th edition) says the following, "Epidemiology is concerned with the occurrence, distribution, and determinants of "health-related states or events" (e.g., health and diseases,, morbidity, injuries, disability, and mortality in populations). Epidemiologic studies are applied to the control of health problems in populations. The key aspects of this definition are determinants, distribution, population, and health phenomena (e.g., morbidity and mortality)."

I don't know about you but the data we are viewing is often not comparable (confirmed cases vs. actual cases, number of deaths vs. case fatality rates) and most importantly we have no best guess for denominators when testing is sporadic and inconsistent. Limited tests meant subjective determinations of who gets tested. We have already seen the distortion as more data is collected.

What we need are less graphics showing the world is on fire and more data collection from asymptomatic members of the population to measure how the virus moves across the entire population. Less data on race and more on structural determinants that underly data collection efforts. It is sad but easy to count mortality rates but we are failing on morbidity attributed to this novel virus.

There are a few sounding the alarm to be better stewards of data and to yell questions through the horror we are experiencing. I leave the data collection to them and look to those of us continuing our data centric work through it all to spot the outliers and begin looking for the tell-tale signs of wider data.

Over the last decade or so, the art world in peril has seemed to lose the ability to adapt. Or, rather, it now seems able to adapt only in one way, no matter the circumstances: by growing larger and busier. Expansion and more were the answers to everything.

I don’t think that response would be healthy in this climate. And so, in that spirit, I want to speak loudly for what art has always been — something done against the rules of advanced capitalism. Art isn’t about professionalism, efficiency, insurance, and safety; it’s about eccentricity, risk, resistance, and adaptation.--Jerry Saltz

The bolded text in the quote above is my own. Substitute data for art--"it’s about eccentricity, risk, resistance, and adaptation." Adaptation based on accumulation and analyses of new information.

I bristled listening to NPR asking for data siloed by race. What are we measuring? If you want to examine biology then examine biology--not race. If you want to examine social inequalities give them a name--not race.

The heart surrenders everything to the moment. The mind judges and holds back. Ram Dass

Go fund yourself...

3/19/2020

Things are often okay--until they are not. I abruptly vacated my month long idyllic trip to an island off the coast of North Carolina. Not because I felt imminent danger but I knew something was off. Rumors were swirling about potential COVID19 positive inhabitants hiding out on the island, the state shutting down the ferry service, or even worse--folks high up on legislative ladder trying to hang on to the farce as long as feasible.

The island had been battered by hurricanes and now North Carolina is coping with visitation by a plague.

Done and done. We would be stranded.

I was employed by a top Pharmaceutical company during 9/11 so although there was the right amount of fear and reverence--ginned up by being stowed under staircases until the final plane was accounted for--this feels different. We are all in the same boat, but no idea where we are headed.

Those of us with epidemiology chops know when we are being fed pollyanna inspired bunk. It isn't good news that there are few diagnosed cases if we have no idea how many people have been tested. Or how many tests are available. Or the limits of an already strained healthcare system. Or the plans for managing the onslaught of morbidity arriving within the next 2 weeks...

The Johns Hopkins COVID19 Resource Interactive Map
(copyright 2020 Johns Hopkins University, all rights reserved)
Hit chevron >> below for legend

View larger map

Dozens of speaking engagements have been cancelled, ultra-marathon event, projects put on hold, our town is pretty much folded up and put away until we get to the other side of the pandemic. It feels like running to the top of a hill that you never reach--your heart is pounding and you are anticipating the rush of climbing the pinnacle but alas--nope.

There are dozens of gofundme campaigns swirling around my inbox. For the first time ever, many of us are going to have to learn to say no.

We are scared perhaps or maybe just cautious. Will projects reschedule? What about all the conferences demanding time for speaking or workshops?

The new paradigm may sprinkle caution in the mix--limiting large gatherings for the foreseeable future.

Its time to unpack the bags for awhile. I always keep a "go bag" of sorts ready to drive up to D.C. or head to the ocean. It is a weird feeling. I suggest leaning into the normal.

My family gathered on our porch yesterday--in the middle of the afternoon--for a slow lunch. My husband, sons Harrison and Ryland, along with Harrison's. girlfriend Kestyn enjoyed something I haven't offered in quite a long time. A handmade chopped rotisserie chicken salad, sliced fresh challah, crudités, butternut squash soup, and iced green tea. And my undivided attention. Although I am building a few online workshops to help us all level-up during this down time, the urgency isn't there.

Let's stay in touch--here is an archived webinar I did sharing insights on where to find data resources.

Let me know what you think...

I am listening.

A tabula rasa in data literacy...

2/17/2020

February is still close enough to our promises of doing better, reinventing our lives, and becoming new and shiny that perhaps all is not lost.

Are You Asking Too Much of Your Chief Data Officer?

The Harvard Business Review describes the role of the Chief Data Officer by articulating 7 distinct jobs. Those of us wearing consultant hats manage quite a bit of this beneath the hood while attempting to convince clients that there are limits to how piecemeal they can be in their goal to have a data infrastructure within their organizations.

1. Chief Data and Analytics Officer (oversight role but in the absence of a functional team might be too much too soon)
2. Data Entrepreneur (squeezing value out of data)
3.Data Developer (process automation or warehouse development)
4. Data Defender (security)
5.Data Architect (integration/creating better data environments)
6. Data Governance (data oversight and access)
7. Data Ethicist (preventing misuse of data assets)

The most common response from clients confronted with their accessible data is a sense of being overwhelmed. Where to start. How to make sense of the legacy data types and "warehousing" to best inform real time estimates or insights.

All engagements begin with a bit of data advocacy and education. Before we were subjected to a cacophony of data "insights" and blaring headlines touting the efficacy of this or that--we had research. A carefully articulated hypothesis perhaps would invite exploration from the bench or the clinical research findings. Good luck keeping up with the publication funnel in our modern times.

I blame the cult of innovation where burgeoning pipelines and escalating profits have become a false profit of value. I can tell you with an excess of confidence that physicians at the point of care are exhausted. They are asked to garner insights from a fun house mirror of spurious findings, bogus endpoints, and suspicious trajectories of profit woven through the healthcare landscape.

The sheer volume and abundant distortion of claims made in the absence of unbiased and careful review mislead and confuse many healthcare professionals attempting to decide which treatment is best for which patients.

I think we all need a tabula rasa or clean slate. Let's start with what we do know and work from there.

Think, "less Eureka" and more "what is that?"

I am often reminded of continuing medical education where writers are encouraged to step in line to join the lucrative field of writing need assessments for educational funding.

A pharmaceutical company will pay a premium for educational interventions that pave the way for their class of drugs or often more boldly--their specific drug.

There are not many eureka moments left in healthcare. We need to question the questions and challenge status quo answers.

Join me, won't you?

Generic but ambitious...

1/11/2020

January can be restorative following the chaos of the holidays but often equal parts annoying and cloying as well. You can't swing a pencil without hitting tomes about how to boost your sales in 2020, new books abound that expand the listicle mindset, or other distractions to separate you from your money.

I am going to whisper a little secret, it won't work. What works is hard work. Really hard work. You must consume more than you create. Read, listen, and watch everything. Jerry Saltz described the ordinary brilliantly--"generic but ambitious". I follow Jerry for many reasons--most importantly are his lessons on observation. Yes, I could read the stacks of books falling like manna from the heavens about data visualization. But that is too narrow of a focus. I like edges from other perspectives. I subscribe to ARTFORUM to learn how to consume information. My reality will never be yours, what worked for me will never work the same way for you, but the hard work will.

I recommend listening to Jerry's interview on the Longform Podcast. He became an art critic in his 40s from a career as a truck driver...his humility and cleverness is and should be industry agnostic. Let me know what you think.

A poem by Wallace Stevens describes my thoughts exactly. This excerpt shares the awareness that 20 men will have 20 unique experiences and will perceive the bridge AND the village uniquely. Read the poem in its entirety here.

Metaphors of a Magnifico by Wallace Stevens (1879-1955)
Twenty men crossing a bridge,
Into a village,
Are twenty men crossing twenty bridges,
Into twenty villages...

A recent article in the New England Journal of Medicine, and reported by the New York Times reminded me of those vapid real-estate reality shows. A couple looks at a perfectly serviceable home in need of a bit of sweat equity but declare the house a failure--"Oh I could not live with that wallpaper, can you show us something else?".

In the case of the medical literature I don't know whom to blame first, the shoddy media coverage or the confusing study design. The wallpaper can be changed folks.

The article in the New York Times doesn't seem to like the wallpaper and can't see beyond the hype to actually look at the merits or opportunities to query the data in a way that might be meaningful.

I am not blaming the brilliant author because let's face it--he didn't create the headline. But I am blaming the metric--avoiding repeat hospitalization or hospital readmission rates. Maybe we should look earnestly into better measures for this highly edited study population.

The study has quite robust inclusion/exclusion criteria for starters.

Inclusion criteria:
at least one hospital admission at any of four Camden-area hospital systems in the 6 months before the index admission,
at least two chronic conditions;
and at least two of the following traits or conditions:
use of at least five active outpatient medications,
difficulty accessing services,
lack of social support,
coexisting mental health condition
active drug habit, and homelessness.

Exclusion criteria:
uninsured
cognitive impairment
oncologic care or had been admitted for a surgical procedure for an acute health problem, for mental health care (with no coexisting physical health conditions), or for complications of a progressive chronic disease for which limited treatments were available.

Assuming that most readers of the article are simply going to think the program was unsuccessful, here are a few tools (below) I am pulling into the data literacy workshop so we can continue to question questions while also questioning answers. Follow along for insights from the workshop. I don't want to reveal to much here as discovery and discussion will be in "real time."

CMS defines readmission rates within the 30 day time-frame in order to capture events most likely associated with the independent admission. I am assuming then that a 180 day time-frame is a bit noisier.

"Readmission and death rates are measured within 30 days, because readmissions and deaths after a longer time period may have less to do with the care gotten in the hospital and more to do with other complicating illnesses, patients’ own behavior, or care provided to patients after hospital discharge."--Medicare.gov

Hospital Compare datasets
Dissecting racial bias in an algorithm used to manage the health of populations
What does race have to do with it (link to discussion of bias)
Graphical presentation of confounding in directed acyclic graphs
Health Care Hotspotting--A Randomized, Controlled Trial
Supplement to: Finkelstein A, Zhou A, Taubman S, Doyle J. Health care hotspotting — a randomized, controlled trial. N Engl J Med 2020;382:152-62. DOI: 10.1056/NEJMsa1906848
Trouble accessing clinical research articles? Try Sci-Hub...

A recent study, Dissecting racial bias in an algorithm used to manage the health of populations sheds some light on how to reform how we evaluate interventions targeted toward social determinants of health.

The bias introduced in an algorithm widely used in hospital systems revealed that black patients were considerably sicker when compared to white patients at the 97th percentile metric--a qualifier for being referred for additional supports. Although the use of healthcare costs appears to be an effective measure to indicate interventions successful in managing patient outcomes--the racial biases are evident.

When the algorithm score is replaces by number of comorbid conditions vs. medical expenditure a more equitable referral pattern is observed at the 97th percentile.

I have reviewed the supplemental data from the "Hot-spotting" article and will be reviewing in more detail in future posts. I don't want to "spoil" the end for folks enrolled in the workshop! But stay-tuned...

The authors are planning additional analyses and I anticipate insights regarding improvements in a wider variety of patient populations. Often being ambitious is not the only goal--we need to be curious--not generic.

Future data workshop "buckets" to highlight Envision2030

The writer as an explorer...

12/31/2019

The writer is an explorer. Every step is an advance into new land--Ralph Waldo Emerson

I wanted to create an end-of-year tome that would encourage and dare I say inspire. But like everything in the last few months--the best of intentions are strewn across the laundry room floor--or quite possibly that is actual laundry.

I am not a big fan of listicles or self-adorned professionals insisting they know best or can help you with your journey. There are no shortcuts period. Do the work. If that doesn't yield results, keep doing the work. I probably could have stretched that into an e-book of a decent length but I prefer getting to the joke quickly and succinctly.

The reason I ran over 1000 miles last year is because most people don't. I get up before dawn--most people don't. I don't eat meat and rarely drink. I don't read fiction. I meditate while swimming a slow silent mile. We all have little levers and oddities that yield an advantage in how we live our professional lives or even personal lives for that matter.

I also don't accept advertising or have a "real" job. I watch people with platforms tell us how to improve or create our work from the security of a 9 to 5 gig that pays the bills. I am basically unemployed or unemployable by most metrics but I am booked through March 2020. See what I did there?

I write books, I speak from the podium, I teach data workshops, and as of 2020 I will be fading away much of the contract work. Why? It's time to take the training wheels off of the bicycle. I have been learning. Enrolled in bootcamps, online executive education programs, and teaching data literacy. I learned to code in Python and dusted off my R code expertise. If you are going to call yourself an analyst you better have the skills to get the job done.

My expertise is in defining data questions, sourcing data, data modeling, curating insights, and weaving a compelling narrative. I am not promising a definitive answer but I am promising that I can hold up my end of the conversation.

Here is a list of some of the best decisions I made in 2019

Podcasts
1619--create an environment where you can question why you include race as a checkbox on your surveys or data collection.

Akimbo
I can't provide links because they are migrating everything to their new platform. But you can search by these titles:
Friction
Systems Thinking
You Should Write a Book

LSE Public Lectures and Events
Ordinal Citizenship
Narrative Economics
Learning from Data: the art of statistics

Comfort the afflicted, afflict the comfortable

12/19/2019

Approaching the close of a busy year of travel and public speaking I am reminded of a gap in my narrative. I lack an elevator speech. Now don't get me wrong, I can certainly share my latest data insights or a particularly interactive workshop that I loved giving but if I had to say one thing--just one thing--what would it be?

I want it to be unique and free of attachment. A sort of "cosmic giggle" that recognizes the futility of stringing together the right order of words that will resonate with someone. I seriously have mediated on this "hinderance" and only discovered its lack of relevance.

How does "recovering medical writer that got tired of focusing on client profit motives instead of point of care decisions that matter, so studied applied analytics and data visualization" sound?

I don't like how the typical descriptors hang flat in the air, "data analyst", "data visualization professional", "insight analyst" and why pick one over the other? I am reminded of the five hindrances of buddhism. These negative mental states are what many of us sit with through mindful meditation. Maybe its just me but I see how seamlessly they apply to impeding not only our meditation practice but also how we walk through our business life. Ram Dass speaks of radio channels and frequencies of engagement but I don't want to get to woo woo. If this sort of thing is intriguing to you here is a link to Who Are You.

Here is the wrinkle. We are instructed to use key words and tags that will yield us discoverable. What if you enjoy your work but simultaneously don't want to adapt to the flotsam of "being picked"? Seth Godin says it best, “The next time you catch yourself being average when you feel like quitting, realize that you have only two good choices: Quit or be exceptional. Average is for losers.”

I would also be interested to hear if your knee-jerk response to "So what do you do?" changes as constantly as mine does. It feels so dynamic in real life. Especially if you spend a nontrivial amount of time engaged in ongoing conversations. Talking to peers and colleagues aligns different edges to your thought processes.

A recent twitter post from a colleague described what I do quite succinctly. I write about topics of interest in the data healthcare sphere and people hire me to talk about it. This blog wasn't designed to be that sort of a platform but here we are.

Now, it’s interesting how subtle and yet how formal our identities are, and how much we’re attached to them, because of how much we are used to our cards of identity.

My name, social security number, my zip code, my address, my occupation—a whole set of labels that define who we think we are. When you and I are born, very shortly after we’re born, we go into ‘somebody-training.’ We start to be trained to become somebody.

And we’re trained by other people who know who they are, and they’re going to teach us who we are—very well-meaning, I mean—so that we can function in the world by being somebody.-- Ram Dass 1976

To be honest with you, I have been sitting in this metaphysical realm since hearing about a few devastating diagnoses in my circle of friends. It made me contemplate my own identity. Am I simply my awareness? Can I make choices separate from my "identity"? Does your work life get a free pass simply because we have sold out to things that we think matter?

What are your business goals? Clicks and shares? Monetizing? SEO? No thanks. I want to be able to sit down and write about the tensions or complexities in healthcare, health economics, and health policy. We all know how to Kardashian-ize a headline or create titillating content. Count me out.

I find graphics like the one published by Vox to be mesmerizing and worthy of deeper analyses and consideration. If more of us were data literate and fluent, we would be empowered to question answers and delve a little deeper. That is going to be my 2020. My focus will be to share more tools, shape more data explorations, and guide us all into collectively doing a little better.

My free newsletter will continue to focus on many of these principles. This blog will continue to gather insights and share careful considerations. Although many of us continue to work over the holiday weeks, we share a history with many of the curious that preceded our humble efforts.

The quote that originated the phrase "Comfort the Afflicted, Afflict the Comfortable" is from 1902. A humorist, Finley Peter Dunne would write pronouncements in an Irish dialectical speech according to the Quote Investigator. A rendering from the website states the following in standard spelling...

The newspaper does everything for us. It runs the police force and the banks, commands the militia, controls the legislature, baptizes the young, marries the foolish, comforts the afflicted, afflicts the comfortable, buries the dead and roasts them afterward.

If you think of newspapers from 1902 as the 2019 "media" the parallels remain even if often they are tongue in cheek. We have a responsibility to not simply serve our own interests but to dig deeper and offer granularity. The devil is indeed in the details and what better way to afflict the comfortable?

Data. Is there anything they can't do?

11/17/2019

I don't eat as many of them as I used to but I never grow tired of donuts. I brought a few dozen munchkins into a Python workshop I was attending in Midtown and although originally waved off as too indulgent--eventually everyone grabbed a donut--or two.

Donuts. Is there anything they can't do?--Matt Groening

Is it just me or does this mirror data strategy in general? Once thought to be the domain of IT folks or computer science--now we are all empowered to take a nibble.

A popular workshop this year introduces a wide variety of learners to data literacy and visualization. Speaking at the Women in Tech Summit I shared how I came across the title for this year's talk--data talks, meople pumble. I wasn't sure how a talk embedded in a Tableau Story viz would be received by several hundred techy women but come to find out--it was standing room only.

We might not be aware but everyone is a data person. You either create, curate, or consume data. I think it is imperative that we become literate and aware.

The last few public experiences I have had were with women audiences. A powerful discussion with Angela Saini, about her latest book Superior the return of race science was sponsored by 500 Women Scientists NYC, the recent Women in Tech Summit, and next year's Fifth Annual Women’s Economic Development Network Leadership Forum VISION 2025: Tools for the Future.

The energy in these gatherings is tremendous. I think this might be my preferred method of communicating about data although I have an active archive of print articles (and these blog posts) that I access on a regular basis. There is something intoxicating about having conversations about data--that people don't want to be having.

I am talking to you personalized medicine.

It isn't just about the cells--its about the society.

Look. I am excited about new discoveries in medicine. But I can still acknowledge the small molecular discoveries--at least the major ones are likely behind us.

I bristle when I hear the term "innovation" attributed to pharmacology and solutions for complex diseases. True innovation would be paying attention to the upstream causes of the rise of disease chronicity and inevitability.

Point research dollars toward understanding how our environments influence disease instead of beating investors into a venture fund froth at the idea of expanding markets. Those markets and data points are actually people.

qualitative data: the adaptive force

10/29/2019

This video is creepy but also timely and relevant to memetics and epigenetic theories that continue to evolve. I stumbled upon it during something said in a podcast I discuss below. Once you watch it you might simulatenously see how bizarre this is for a leaked internal video by Google but yet, is it really?

Today has been a particularly good day. One of the days where I had a bit of freedom--meaning I didn't have a face-to-face meeting scheduled either in person or remotely. I like keeping my camera closed all day.

There are books to be read and more than a few presentations to finalize. Just like makeup over 50--less is more. I prefer to seed the ideas and then switch over to conversations.

I feel the same way about exploring categorical variables in spaces where we reflexively rely on numerics. What can they tell us about a population, community, or individual?

data & donuts

I see the image of donuts being "bathed" in sprinkles and I see a business. Of course the obvious deliciousness of Holtman's Donuts but I think of donuts as ideas--moments of pleasure--and the sprinkles are the data. Not all of it. Simply the curated and carefully selected information to help start a conversation.

My business model is simple. When I am on the road I am listening to conversations around the halls of the National Press Club, Brookings Institute, and recently attended Applying Big Data to Address the Social Determinants of Health in Oncology: A National Cancer Policy Forum Workshop In Collaboration with the Committee on Applied and Theoretical Statistics.

When I am lucky enough to be working out of my home office I start most days with a morning run with Fred--my. hound. I queue up podcasts from a broad range of interests and arrive home filled with new "sprinkles".

I am waiting to share a full post as I am holding a workshop this week--you can sign up here Big Data on a Less Big Budget (meanwhile, I don't want to give away the goods) and speaking at Women In Tech Summit next week. But the serendipity of my chosen topic from the Tableau Fringe Festival and a common thread of data skill workshops was surprisingly realized in both the National Cancer Policy Forum and this morning's podcast from my 9-mile run.

The LSE Public Lectures are a dynamic series of timely topics where I am able to consider at length discussions around economics and political science serving about global discourse on a wide variety of topics. Today the discussion was around ordinal citizenship or even better--eigencapital.

I imagine this as an application of eigenvalues and vectors--"An eigenvalue is a number, telling you how much variance there is in the data in that direction, in the example above the eigenvalue is a number telling us how spread out the data is on the line."

My undergraduate students have been having a tough time understanding qualitative ordinal and nominal data types. I was gobsmacked and more than just a little interested to listen to a podcast on ordinal citizenship. I found it compelling and a novel foundation to introduce variable selection when measuring and defining relationships like poverty, race, and yes--even citizenship.

"As digital technologies have enabled a broadening of economic and social incorporation, the possibilities for classifying, sorting, slotting and scaling people have also grown and diversified. New ways of measuring and demonstrating merit have sprung up, some better accepted than others. Institutions, both market and state, find themselves compelled to build up and exploit this efficient, proliferating, fine-grained knowledge in order to manage individual claims on resources and opportunities.

This process, she argues, creates new social demands for self-care and individual fitness that possibly erode the universal and solidaristic basis upon which the expansion of citizenship historically thrived."Professor Marion Fourcade--London School of Economics (LSE) Public Lectures & Events.

Although this slide was part of yesterday's discussions it comes to mind for linking all the disparates thoughts informing my analyses this afternoon. In healthcare we talk a lot about value-based care and "quality".

But are we prepared to explore the social risk factors that influence patient outcomes beyond the type of care the patients receive?

Big Data on a Less Big Budget

data as raw material for information*

9/18/2019

When you become data curious you begin to notice the vagueness of descriptors and measures in the clinical and political literature. I see surveys where the very thing they intend to measure lacks a definition. Or headlines lacking clarification of what the numerator and denominator might be in their claim. Alarming reductions and increases in benefits or risks lack context but still our heart rates increase and we lean into the new normal of agitation and fear.

We can become data literate and seek the answers we need. As an analyst, if I am asked to discuss poverty for example, this requires a longer, deeper and often existential conversation.

We have advanced in our understanding of the complexity of poverty. Not unlike how we talk about health and social correlates of health, poverty is not simply one thing. When we analyze a numerical variable and consider those values as representative of our analyses we are being short-sighted. The categorical variables need to be included as well.

"...deprivation comes in many forms and use a new multi-dimensional measure that not
only considers income but also education, electricity, water and sanitation"--New ways of looking at poverty

The U.S. income data determines the poverty level and from there federal aid is indexed and provided for a variety of programs such as SNAP, health insurance, etc... "a lot of power for a statistic that is out of touch with reality" as described by the New York Times back in 2001.

A simple tool within US Census called Abacus will let you examine 2 variables at the US or state level over a specific time on your mobile device!

I rely on the American Community Survey to explore expanded measures of poverty. I prefer my analyses outside of the IPUMS website and rely on Python or Tableau Prep to quickly clean up data from the larger dataset.

You can observe below how the expanded Household-Economic Characteristics contribute additional granularity to discussions of poverty--beyond a simple calculation that allocates a household either above or below an arbitrary income cut-off.

I plan to dig into the census data more and more over the coming months. It is an under-utilized resource in our discussions about healthcare inequality and a granular understanding of the societal poverty line and determinants of health.

*I love the musings of what data is from this website--key concepts in information and knowledge--defining data. I am aware of datum as singular and data as plural but defer to common usage.

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In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.

Question the quality of the evidence. The motivation for disseminating the evidence.

Who stands to benefit the most from its uptake?

Remember the quote by Upton Sinclair...

“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”

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The Last Days of the Art World … and Perhaps the First Days of a New One Life after the coronavirus will be very different.

The Johns Hopkins COVID19 Resource Interactive Map
(copyright 2020 Johns Hopkins University, all rights reserved)
Hit chevron >> below for legend