State Level Excess Mortality Updates – Oct 6th, 2021

I can’t believe another month has gone by, but here we are! I am back to update state level excess mortality data from the CDC website, pulled on 06OCT21. See previous posts for more details about this data.

First up though, here’s an interesting gif someone made that shows the spread of COVID cases over time by region. Definitely shows some interesting seasonality, and also some interesting data anomalies.

Excess Mortality – How bad has it been?

As I’ve talked to a few people about state level data over the past few months, one of the things I’ve noticed is that some people’s perceptions of the pandemic do not match their individual state. I started wondering if this has anything to do with when the peak excess mortality is, and how long the states spend at high levels of excess mortality. Using the same CDC data I’ve been using, I decided to pull the number of weeks each state has a mortality rate >50% above their average. The data goes back to 2017, so we can see that this phenomena only happened three times between January of 2017 and March 28th, 2020: once to Puerto Rico in September 2017 (Hurricane Maria), and twice in Wyoming (October 2018 and January 2020). I’m not totally clear what happened those weeks.

So this happened 3 times in a little over 3 years. How often has it occurred since the end of March 2020? A total of 363 times in 45 states. The only 5 states that haven’t reached that level since the pandemic began are Alaska, Hawaii, Maine, New Hampshire and Oregon. The US as a whole spent 6 weeks in that range, with 25 states exceeding the national average. Here are those states, and how many weeks they spent at that level (so far):

State# of weeks at >50% excess mortality
Texas19
Mississippi17
DC16
Alabama, Arizona14
Nevada, North Dakota13
Oklahoma12
Georgia, Louisiana, Montana, South Dakota, Tennessee10
Arkansas, California, Florida, 9
Indiana, New Mexico, New York City (city only)8
Iowa, Michigan, New Jersey, Pennsylvania, New York (excluding city7

Just a note on NYC vs NY: only one of those weeks wasn’t overlapping. If we raise the bar and look at only states that have at least one week where they had DOUBLE the number of deaths they usually do, we find only 9 states have hit that bar:

State# of weeks at >100% excess mortality
New York City (city only)7
New Jersey, South Dakota5
California, Connecticut, Massachusetts3
Florida, New York (excluding city), North Dakota2
DC1

Another note on NYC vs NY: the 2 weeks for NY are also in the 7 week stretch for NYC. Not clear why the CDC reports these separately.

Excess Mortality Over Average Updates

First up, here’s the whole US. It’s worth noting that when I did this graph a month ago, the lowest value was 554 excess deaths/million. Now it’s 739 excess deaths/million. The brightest red a month ago was 4107/million, now it’s 4624/million. The greens and the reds mean more than before:

So who were the top movers this month? Let’s see:

StateExcess Deaths Above Average/Million 2/1/20-10/6/21 (change from 9/8)Change from 9/8 rank
Mississippi4624 (+516)No change
Alabama4000 (+559)+1
Louisiana3801 (+534)+3
Arkansas3404 (+379)No change
DC3749 (+97)-3
Arizona3597 (+251)-1
South Carolina3453 (+326)No change
Tennessee3381 (+326)+3
Florida3365 (+538)+3
New York3177 (+91)-2

Note: the NY data here is all of NY, state and city combined. Seems incredible that New York may actually fall out of the top 10 for excess mortality since the pandemic started. To note: there were 4 states that saw substantial gains but are not yet at top 10 level. These were: Georgia (+471, 14th place), Oklahoma (+442, 13th place), Peurto Rico (+407, 37th place) and Kentucky (+390, 17th place).

Excess Mortality Over Upper Bound by State

Okay, here are the states that most exceeded 2 standard deviations from the mean mortality:

And now the top 10:
StateExcess Deaths Over Upper Bound (change from 9/8)Change from 9/8 rank
Mississippi3302 (+443)No change
Alabama3004 (+502)+1
Arizona2659 (+210)+1
Florida2647 (+499)+5
New York2646 (+56)-3
Arkansas2582 (+324)No change
Louisiana2574 (+449)+3
Texas2549 (+345)-1
South Carolina2471 (+280)-1
New Jersey2452 (+52)-5

To note, there are again 4 states who had a top 10 gain in excess mortality, but didn’t make the overall top 10. These are: Tennessee (+483, 11th place), Georgia (+420, 12th place), Oklahoma (+380, 14th place), Kentucky (+327, 21st place).

As always, let me know if there are any questions and I’ll be back in a few weeks! Given seasonality, I’m going to try to keep this up monthly. I’d also ideally like to see if some states start to regress at all. There is a lot of commentary that COVID mostly killed people who were going to die anyway, but so far that is not what we are seeing. If that’s true, at some point some states excess mortality should start to decrease below the norm. So far I’m only seeing slight decreases for Connecticut, Rhode Island and Minnesota, but those are small and could be adjustments.

State Level Excess Mortality Updates – Sept 8, 2021

More Explanation and Some Links

Well hello again folks! When last we left off about 4 weeks ago, I had updated the state level mortality data provided by the CDC for 2/1/2020 – 8/11/2021. Today I’m updating through 9/8/2021, about 4 extra weeks. All the caveats from my prior post still apply, so go there for any more explanation.

First though, I wanted to clarify some things from my prior post. I find excess mortality data interesting because every state counts COVID deaths differently. There are varying reasons for this, some more valid than others. There are also lots of theories about what the non-COVID excess deaths are. I like looking at state level data because it forces us to think more critically about what those deaths might be and to avoid making sweeping generalizations. In the national press, only the biggest 4 states (California, Texas, Florida and New York) seem to get any air time. Other states may occasionally be cherry picked if something interesting is going on, but otherwise they are mostly ignored.

There is some good work going on with excess mortality, both in trying to estimate it and trying to track it. First up, some good analysis of the 2020 death data, including racial breakdown. While the early phase of the pandemic (when it hit NYC hard) was very skewed towards black and Hispanic deaths, it appears things got far more even as we got towards the winter. For example, here’s the excess death incidence rate for those > 65 years old. Bars are quarters of the year 2020:

Next up is an interesting link (explanation here, site here) to someone trying to catalogue excess mortality in real time, with the concerning hypothesis that we may be seeing an uptick in other kinds of deaths too. Now there are two competing hypotheses here: people could have put off getting treated for other medical conditions due to the pandemic, or people could be more susceptible to other medical conditions after having COVID. Actually, those aren’t competing. It could be both. We know that with the flu there is a well established link between getting the flu and subsequently having a heart attack, and there’s no reason COVID-19 couldn’t act similarly. We also know that in many places hospitals are full and it makes sense people may put off care. We will know more as the data comes in I’m sure, but it’s unfortunate. On that happy note, on to the next updates!

Excess Mortality Over Average by State

I made a more multi colored graph this time:

Now here are the updates for the top 10:
StateExcess deaths above average/million 2/1/20 – 9/8/21 (change from 8/11)Change from 8/11 rank
Mississippi4108 (+473) No change
District of Columbia 3652 (+111) No change
Alabama 3441 (+320) +1 spots
Arkansas 3404 (+392) +2 spots
Arizona 3346 (+208) -2 spots
Louisiana 3267 (+166) -1 spots
South Carolina 3127 (+238) +1 spot
New York 3086 (+76) -1 spot
New Jersey 2894 (+33) No change
Nevada2842 (+281)+3 spots

Mississippi’s struggling here guys.

Excess Mortality Over Upper Bound by State

Okay, here’s the updated numbers for deaths only falling outside the upper bound:

And here are the top 10:
StateExcess deaths over upper bound 2/1/20-9/8/21 (change from 8/11)Change from 8/11 rank
Mississippi2859 (+379)+1 spot
New York2590 (+39)-1 spot
Alabama2502 (+229)+2 spots
Arizona2449 (+155)no change
New Jersey2400 (+5)-2 spots
Arkansas2258 (+333)+4 spots
Texas2204 (+181)-1 spot
South Carolina2191 (+190)no change
Florida2148 (+454)+8 spots
Louisiana2125 (+107)-3 spots

There was more motion on this ranking than I expected to see, which is sad because it means there are multiple places where we are seeing truly unusual death tolls.

States of Interest

Since everyone’s always interested in the Big 4, here they are. Change from 8/11 in parentheses:

Excess Deaths Over Upper Bound/MillionExcess Deaths Over Average/MillionRank in Excess Deaths Over AverageRank in Excess Deaths Over Upper Bound
New York2590 (+39)3860 (+76)82
Florida2148 (+454)2827 (+488)129
Texas2204 (+181)2723 (+216)157
California1761 (+50)2285 (+92)3020

And because I’m always interested in my state and those of similar size, here they are:

Excess Deaths Over Upper Bound/Million
Excess Deaths Over Average/Million
Rank in Excess Deaths Over AverageRank in Excess Deaths Over Upper Bound
Arizona2449 (+155)3346 (+208)54
Massachusetts1240 (-9)1642 (+25)4034
Tennessee1904 (+117)2660 (+193)1311

Age Adjustments?

So on my last post Kyle Watson made an interesting point that there should be some sort of age adjustment if we were going to compare things on the state level. While some of this is sort of inherent to the entire concept of excess mortality (states with older populations likely have more expected deaths in a given year), we would expect a disease like COVID to hit states with older populations harder even if everything else was equal.

Interestingly there was some work done on this by a group using the raw COVID numbers, which also looked at international data. They found that states like Texas actually had a worse pandemic than previously reported due to their young population:

I had to ponder a bit what the fairest way of doing this was though. It turns out the CDC also publishes the numbers by week by age group, so I took a look at the US as a whole from 2015-2021:

So every age group from 45 years on showed a fairly noticeable bump last year. Actually every age group showed an increase in mortality when compared to the previous years, and it wasn’t entirely the groups I expected. It’s hard to see on the graph, but here’s the increase for each age group over the average from 2015-2019:

Age Group% Increase Over 2015-2019 Average
Under 25 years5%
25-44 years33%
45-64 years20%
65-74 years30%
75-84 years27%
85 years and older19%

I was surprised so many of these increases were so close together, it was just the starting numbers that were different. Please note the bin sizes are different however. There are twice as many ages contained in the 45-64 year old group as the 65-74 group, which is how you get a similar number of deaths in the younger age category.

It’s also interesting to note that while the data for this year is obviously still highly incomplete and anything could happen, there’s a chance the 85+ group may not show a large jump for 2021. Almost certainly not as large as last year.

Back to age adjustments though: I couldn’t find a great source to give me state by state age breakdowns matching the ones above, but I did find a breakdown of how many people in each state are over 65. I assumed that excess mortality followed roughly the same pattern as the overall mortality numbers, and adjusted from there. Here are the new leaders for excess deaths above average:

StateAge-Adjusted (albeit crudely) Excess Mortality above Average 2/1/20-9/8/21
DC4330
Mississippi4100
Louisiana3328
Alabama3303
Arkansas3253
Texas3183
Arizona3136
New York3009
South Carolina2915
New Jersey2864

The map overall shows there’s a pretty substantial dropoff between Mississippi, DC and everywhere else:

Now here’s the big 4:

StateAdjusted Rank for Excess Mortality Over Average (previous rank)Adjusted Rank for Excess Mortality Over Upper Bound (previous rank)
New York8 (8)3 (2)
Florida24 (12)15 (9)
Texas6 (15)2 (7)
California22 (30)13 (20)

Interestingly, the states most helped/hurt by this adjustment aren’t necessarily the ones you’d think of. For deaths above the upper bound, 4 states added on more than 150 deaths/million and 4 states lost more than that after the adjustment. The ones that gained deaths were: Texas, DC, Georgia and Utah. The ones that lost the most post-adjustment were Arizona, Florida, Pennsylvania and West Virginia. As mentioned, only Texas and Florida receive much air time nationally, and since this worked out differently for both of them I wouldn’t expect to see much on this any time soon.

As always, let me know if there’s more you want to see! I have a lot left on spreadsheets for individual states. Stay safe out there.

State Level Excess Mortality Data

A Warm Hello!

Well hello there! It’s been a while. Unfortunately I’ve been dealing with some (non-COVID related) health issues that have made reading and writing rather difficult, so blogging has been taking a back seat to things like um, paid employment. You know how it goes. I’ve missed you guys though, and thank you to those who reached out with nice messages asking how I was doing. That was appreciated.

Anyway, for the first time in a long time I recently fell down a rabbit hole of data and started putting together an exceptionally lengthy email with graphs for a small email group, when I realized I may as well just turn this in to a blog post in case any one was still poking around here and might be interested. So here we are.

Some Background About Data That’s Currently Interesting Me

So despite the aforementioned reading/writing troubles, I have of course been interested in the data coming out of the COVID-19 pandemic. I could go on and on about many things, but one of my top fixations is the difference between the state level reported COVID deaths (compiled by the CDC here) and the overall excess mortality across the US compiled by the CDC here.

Essentially the first set of numbers is exactly what it sounds like: the number of people in a state that the state says have died of COVID-19. The second number is a little more interesting. Basically the CDC has years and years worth of data about how many people die each week in 1) the USA as a whole (51k-60k depending on the time of year) and 2) individual states. Thus they can predict each year how many people are going to die in a given week and then say if we are right on track with that number or if we are wildly above that number (95% confidence interval) for both the country as a whole and each state individually.

They published this data prior to COVID as well….if you’ve ever heard someone say we had a “really bad flu year” this data is probably why. If an outbreak of the flu (or anything) pushes the country above the 95% CI for expected deaths, the CDC will generally raise an alert. For example, the flu season in the winter of 2017/2018 pushed us above the 95% CI from December 23rd 2017 – February 3rd 2018. Currently the country has had excess deaths from all cause mortality since March 28th, 2020. We have yet to drop back below the 95% CI for more than a week. The graph for the whole US looks like this (recent weeks trail off as jurisdictions are still reporting):

Since 2/1/20 this comes out to 595,688 deaths above the 95% CI (yellow line) or 758,749 deaths above average.

Now while seeing the entire country interests me, what really interested me about this data is that sometimes the excess mortality data from all causes and the COVID-19 reported death data for a particular state don’t match. That’s something I wanted to look in to.

COVID-19 Deaths vs All Cause Excess Mortality

I first got interested in this topic because the first time I looked at excess mortality data, I noticed that my state (Massachusetts) had a MUCH higher number listed for COVID-19 deaths than it does for excess mortality. Checking the CDC website today, they have us listed at 18,131 deaths, or 236 COVID deaths/100k residents. However, our excess mortality since 2/1/20 is only listed as between 8,780 and 11,369. I started running the numbers because the overall COVID number puts us at 3rd worst in the nation. The lower number would rank us somewhere between #31 and #40.

I Googled a bit and as close as I could find, we’ve changed our counting method twice to better align with federal standards, but don’t appear to have subtracted the “overcounts” back off our total. This article suggests we were overcounting nursing home deaths (take that Cuomo!) until April of 2021 and this article suggests that we also included more “probable” deaths than other states until October 2020.

So given that every state counts COVID deaths differently but (presumably) counts all deaths, how common is it that a states COVID deaths exceed their excess mortality? Which states have the highest “overcounts” and “undercounts” and what does it look like if you just compare excess mortality and remove COVID classifications entirely? Well I’m glad you asked! That’s what I wanted to know too!

The Overcounts

Pulling from the CDC website here through 8/11 and taking their upper and lower guesses for excess mortality and converting to deaths/million, I found 5 states that have reported more COVID deaths (as of today 8/14) than they have excess mortality:

  1. Massachusetts (+1,015/million – #3 ranked)
  2. Rhode Island (+690/million – #5 ranked)
  3. Minnesota (+145/million – #37 ranked)
  4. New Jersey (+143/million – #1 ranked)
  5. Connecticut (+41/million -#9 ranked)

Now it is important to note that not all the death data is in. It is possible that these states are simply really good at reporting COVID deaths and less good at reporting other deaths, or that something else is going on. COVID could be killing people in these states who would have died anyway, and thus it could be failing to add to the excess mortality in the way it is in other states, or some mitigation effort the states took could be reducing other types of mortality in a way that is balancing COVID out. The CDC won’t close out this data for quite some time, but it will be interesting to see what happens when all the accounts are settled.

What is notable here though is that 4 of these 5 states are in the top 10 for worst death counts. If these are truly over-reported, that means the pandemics were not as bad there as commonly believed. Additionally, several of those states had fairly strict lockdowns. If excess mortality is caused by lockdowns, it is not showing up in these states data so far.

The Undercounts

Now undercounting is tricky. The CDC notes that some states have extra process in place to ensure accurate coding of COVID deaths, so it’s possible these states are just behind. It’s also possible that excess mortality in these states is from something other than COVID, so they just wouldn’t have as much COVID death as they would excess mortality. Here’s the list, there were also 5 states here. Well, 4 states and DC:

  1. Washington DC (-294/million – #34 ranked)
  2. Texas (-147/million – #26 ranked)
  3. California (-76/million – #32 ranked)
  4. South Carolina (-51/million – #21 ranked)
  5. Vermont (-33/million -#50 ranked)

To my point about other causes of death, Washington DC in particular would be potentially impacted by a jump in homicides (up 19% last year) and opioid deaths (hit a record in 2020). For the other states, we’ll continue to see what happens as the data trickles in.

Overall, it’s interesting that 40 states COVID deaths counts fell somewhere in between their states upper and lower bound estimate for excess mortality. So how did every state do when compared for excess mortality so far? Let’s check it out?

About the Data

A few things to keep in mind before I show state level graphs:

  1. The data is excess mortality from ALL CAUSES since 2/1/20.
  2. There’s one graph for amount above lower bound (excess above average) and amount above upper bound (excess above 95% CI). I’ll discuss the differences a bit below.
  3. Some of this is estimated. Since every state reports at a different pace, they estimate where states will be at to bring everyone up to the same level. I’ve been watching this for a few months and they rarely have to take many people away, so the estimates look pretty good.
  4. The data is from here. I download the “National and State Estimates of Excess Deaths” csv file and then use the “Total Excess Lower Estimate” and “Total Excess Higher Estimate” (Column J and K on my spreadsheet) for each state.
  5. To convert to per capita, I used the 2020 census numbers for each state. I included Puerto Rico and DC, so all rankings are out of 52.

There’s been a lot of talk about how the pandemic impacted other types of deaths, so it’s notable to see where the highest excess mortality has been.

Excess Mortality Over Average by State

Without further ado, here’s the excess mortality over the average, by state. Sorry about the small font, click on it to embiggen:

So for deaths above average from ALL CAUSES, per capita the top ten states are:
StateExcess Deaths Over Average/Million (2/1/20-8/11/21)
Mississippi3635
District of Columbia3541
Arizona3138
Alabama3121
Louisiana3101
Arkansas3012
New York3010
South Carolina2889
New Jersey2861
South Dakota2708

Now this is just deaths over average. Some states have more yearly variation than others, and thus look a little different if you only take the deaths above the 95%CI interval. That’s next.

Excess Mortality Over Upper Bound by State

Again, click to make that bigger.

As you can see, going over the upper bound mostly evens out the smaller states. This makes sense. For example, Massachusetts and Montana had surprisingly similar excess mortality across the timespan represented. However, Montana is 1/7th the size of Massachusetts. They typically hover around 200 deaths per week statewide, and Massachusetts generally has 1,100-1,200. With 200 deaths, slight differences in reporting (like someone in one hospital forgetting to send the numbers for a week) could skew things quite a bit. That’s less likely over larger populations. So here are the new top 10:

StateExcess Deaths Over Upper Bound/Million (2/1/20-8/11/21)Prior Rank
New York25517
Mississippi24801
New Jersey23959
Arizona22943
Alabama22734
Texas202316
Louisiana20185
South Carolina20018
Pennsylvania194317
Arkansas19256

As expected, the two places with the smallest populations (DC and South Dakota) dropped off this list and were replaced with two much larger places: Texas and and Pennsylvania.

Other States of Interest and Possible Posts Going Forward?

Now throughout the pandemic, it seems everyone gets fixated on some subgroup of “the big four”: California, Florida, New York and Texas. If you want to know how they’re doing, here they are pulled out:

StateExcess Deaths Over Upper Bound/MillionExcess Deaths Over Average/MillionRank in Excess Deaths Over AverageRank in Excess Deaths Over Upper Bound
New York2551301071
Texas20232507166
California171121933017
Florida169423392318

Here are the states I track, as they are all approximately the same size as Massachusetts (around 7 million people):

StateExcess Deaths Over Upper Bound/MillionExcess Deaths Over Average/MillionRank in Excess Deaths Over AverageRank in Excess Deaths Over Upper Bound
Arizona2294313834
Tennessee178726361114
Massachusetts124916173931

If people are interested in particular other states, I’d be happy to post them in the comments as time/health allow. Additionally, the CDC updates this data weekly. Now that I have the explanation typed out and my spreadsheet set up I can fairly easily post updates every few weeks (sans lengthy intro) if there’s interest. Let me know what you all think! Hope everyone is staying well.

New Year’s Resolutions

I don’t often make New Year’s resolutions, but this year I’ve decided to join Gretchen Rubin (of happiness project fame) in resolving to go on a 20 minutes walk every day in 2020. Her theory is that if you aren’t getting much exercise, resolving to get a little bit daily will provide big benefits. She has research on her side on this one, and it doesn’t hurt that walking seems to be the only form of exercise that makes my migraines better rather than worse. We’ll see how this goes.

This got me thinking about New Year’s resolutions in general, and wondering what the most common ones were. there appears to be a lot of selection bias in the studies, but healthy eating/exercise/weight loss and saving money seem to be the most common in America.

I tried to find some from other countries, and it seems like Germans may put stress reduction and family time at the top of their list. My googling for other European and north and south American countries didn’t turn up much.

I did however, find this blog post from Duolingo, that had some really interesting insights about one particular New Year’s resolution. Duolingo is an app that helps you learn a second language, and they have a distinctive peak in sign ups and account usage just before the first of the year. They discovered that the countries their users normally came from changed a bit around the first of the year:

Apparently users who sign up around the first of the year actually are slightly more likely to continue using the app than those who sign up at other times.

Overall, I’ll admit I was a little surprised that I couldn’t find more research on the subject of New Year’s resolutions. It seems like this would be an interesting study in how priorities change across countries or time. If anyone knows of any good resources that I didn’t find, please let me know! In the meantime, happy new year everyone!

Diversity and Religion Trivia Question

Back when this blog was in its first incarnation, I used to occasionally do some challenge questions. I stumbled across one this week that seemed like a good candidate, and since my computer is still broken I figured I’d throw it out there.

As part of their religious landscape survey, Pew Research has put together a racial diversity ranking of religions and major denominations in the US. Six groups were found to be more diverse than the U.S. general population. What are they?

A few clarifications and one hint to help:

1. The diversity ranking measures the spread across 5 racial groups: White, Black, Asian, Latino and Mixed/Other. A perfect score would be 20% in each category. In other words, a group dominated by one group would not be considered diverse even if that group was a minority group in the US.

2. Pew breaks Christianity down in to major denominations and includes several types of unaffiliated (aka not religious) groups in their survey. If you want to see the groups included, see this page.

3. The survey also only looked at people in the US, so diversity is only based solely on that. Groups may have more diversity in other countries, but only their US members were counted.

4. If you need a hint: 3 of the top 6 groups are Christian or Christian-adjacent* and 3 were other religions or unaffiliated groups.

For the answer, see the list here.

*For purposes of this question, Christian adjacent means that the members of the group might consider themselves Christians, but a majority of Christians in other denominations do not.

If Not Voting Were a Candidate

My computer is still having problems, so another short post today. I saw this graphic on Twitter this week, and thought it was interesting:

Our voting certainly leaves a wide margin of error with regards to public opinion.

What’s interesting of course is that we have no idea how those people would vote if they were forced to, though many people seem to think they know. From experiences in other countries it seems like it might increase support for left leaning policies and higher tax brackets. However in other countries it boosted fringe third parties, and doing away with it increased support for major parties. Other countries have not seen a difference.

Point being, a non-random sample doesn’t always tell you much about what’s not in the sample. Keep that in mind with any initiatives aimed at changing voting requirements.

Health Expenditures and Obesity

So I dropped my laptop 2 weeks ago and the internet connection has been off and on, dying completely yesterday. Until I either fix it or get a new one, posts will be limited to what I can type on my phone without getting aggravated.

This week I came across a post by Random Critical Analysis analyzing the fairly famous “US spends more on healthcare and has lower life expectancy” graphs. As part of this analysis, he graphs life expectancy vs obesity and shows that the US is very well in line with other developed countries given our above average obesity rate.

To further the point, he breaks down the states individually and shows that this holds within our countries as well:

In other words, low obesity Colorado has a life expectancy in with the other developed countries, while higher obesity states are much lower. He also redid the analysis by splitting other countries up in to regions, and found this pattern holds for other countries as well. The post then goes on to build the causal chain, and it’s pretty fascinating. It even throws in maternal mortality, and shows that if we adjust for BMI, we’re right on par there as well.

I obviously suggest reading the whole post, but it’s a good reminder that this factor has been under discussed in the conversation about healthcare. We often say “other countries have figured out how to deliver healthcare more effectively than we have”, but no country has figured out how to do that with a population as obese as ours. In other words, it seems that unless we really start finding some good ways of preventing obesity or facilitating weight loss, it may be hard to ever reduce our costs. Sobering thought.

An Anecdote About Paranoia and Baseline Assumptions

The Assistant Village Idiot has re-posted one of my favorite anecdotes of his. For those not familiar with him, he has 40+ years experience in a state mental hospital. It’s short, so I’ll repost it in its entirety here (source):

A paranoid patient of ours had taken the book 1984 out of the patient library.  His particular paranoia is very much concerned with thought reading and thought broadcasting. He is not a person one might expect to have good general knowledge of literature and political culture, and he did not have much preconceived notion what it might be about.  He had heard somewhere it was an important book.  We were a little concerned what he might take away from the book, but we don’t get much involved in people’s selections.

He found it sad.  This guy had a girlfriend, but he lost her.

He didn’t really notice the paranoia-inducing parts of the book.  Those were just normal background to him

I think about that a lot, most often when I see a poll question asking people how they feel about current events or to compare previous years to this one. Getting people’s impressions without knowing their baseline can be highly misleading.

5 Disorders With Suprising Sex Differences in Diagnoses

There was a great article in the Atlantic this past week called “What Joe Biden Can’t Bring Himself to Say“.  The article focused on his (and the authors) struggle with stuttering, and contained a lot of fascinating information about stuttering that I never knew. Regardless of your political orientation and/or feelings about Joe Biden, it’s a very worthwhile read.

One of the interesting stats it contained was that stuttering was twice in common in boys than in girls, and that girls have a higher recovery rate. I was interested in this, because aside from a vague “girls have better verbal skills earlier, so I guess that makes sense” train of thought, there doesn’t seem to be a clear reason for this. I Googled a bit and found that no one is really clear on the reason for the discrepancy, though there is a thought that girls may tend to get earlier help because people expect them to be more verbal. This discussion got me interested in other similar disorders. We’re not surprised to hear that issues like prostate cancer or breast cancer are more common in one sex than the other, but some things feel like they should be more gender neutral.

I decided to look up a few other examples, though I excluded mental health type disorders since some of the sex differences there can be a bit controversial, and excluded diseases or disorders that seem to be linked to differences in behavior (such as lung cancer):

Student Debt: A Few Facts and Figures

I wasn’t intending to write about student debt this week, but oddly enough I had two different people ask me about it on the same day. The first was a younger coworker, who had heard Elizabeth Warren say that student loan debt disproportionately affected African Americans and was curious if that was true. He also wanted to where “average student loan debt” numbers came from. The second was the AVIs wife, who sent me a new report looking at the return on investment from different types of colleges, and wanted to know if family income was taken in to account.

Okay, so let’s take this one thing at a time. First, Warren’s comments came from a Tweet where she also shared this article. For clarity, I want to note that this article ISN’T by Warren, but her Tweet would seem to indicate some agreement. The article started with the stat that the average student loan debt was $37,102. My colleague (a fairly recent grad) thought that sounded low.

The average student loan debt number comes from this Chamber of Commerce report. Now this report was interesting because it is looking only at those people who graduated in 2017. When my colleague had first mentioned this to me, I had wondered if the “average” number was including those further from graduation, but it doesn’t. It did however, point out that student loan debt varies wildly based on the region of the country you live in:

New England is one of the highest average levels, so those of us living here will tend to see higher loan totals among our peers. Additionally, borrowers owing 6 figures are the fastest growing group of borrowers,  with about 2 million people owing over $100,000. While much of that is due to graduate school debt, one would suspect those folks would be concentrated in the same areas as the higher levels of debt.

So what about the disproportionate impact claim? Well, that also came from the Chamber of Commerce report. More black students take out loans to pay for their education (77% vs a national average of 60%), they take out higher amounts ($29,000 vs $25,000) and are more likely to default on their loans within 12 years of graduation (50% vs 36% of Hispanic students and 21% of white students). However, it’s important to note that this is comparing graduating students to other graduating students….it excludes those who didn’t go to college or didn’t graduate. Those groups are also disproportionately comprised of minorities. Inside Higher Ed has a good graph of the outcomes by race 6 years after people matriculate:

I think this is striking because it’s something I always wonder about when we talk about student loan forgiveness. Some people choose not to go to college or start in community colleges because of the expense of college. Forgiveness of debt may really help some people, but in many cases big choices have already been made. If there is inequality in those initial choices, then loan forgiveness will not solve those inequalities. We know that white 18 to 24 year olds are more likely to enroll in college than black or Hispanic students, so while the loans taken out by black students may be higher, the proportion of people taking them out is lower. It may still be true, but I think it should be clearer that we’re only talking about students here.

I think this is an important point because if we’re talking fairness, then we have to consider the poorest among us may be among the least likely to take out student loans to begin with. Indeed, an analysis of Warren’s plan showed only 10% of the benefits of this plan would go to the bottom 20% of households. By contrast, the top 20% of qualifying households would get 18% of the benefits. This may even out as the other parts of her plan were implemented (reduction in college cost going forward), but it’s something to consider.  (Note: I will fully admit I haven’t spent much time studying Warren’s proposal, so I may be missing something. Let me know in the comments if I’ve misstated something and I’ll update. I’m using her plan as an example to discuss the broader point about who currently carries student loan debt, not to knock her proposal over others. I really appreciate that she was willing to publicly release her plan for discussion like this.)

Alright so now to the last point….what’s the return on investment for college students? Well according to this calculation in the short run (10 years) it’s better to have gone to a public school than a private one, but by the 40 year mark it’s better to have gone to a private school. For example, my alma mater is Boston University. At the 10 year mark, it’s the 3,318th best ROI in the country. By year 20 post grad, it jumps to 464. By year 30, it’s at 142, and by 40 years it’s almost one of the top 100 best values at 116. The calculator is fun to play around with because you note some interesting patterns. Small schools in the Boston area do better than small schools in New Hampshire, which I will guarantee is a function of the graduates staying near cities. There’s no cost of living adjustment in alumni salary calculations. Some of the Protestant colleges my friends and family went to don’t fare well, but I’d suspect an inordinate number of graduates go in to things like social work, teaching or other ministry positions. In fact a good number of the “worst” ROI schools are actually Rabbincal colleges.

So are these institutions cherry picking rich students and then taking credit for their earnings? Possibly. Nothing in the calculations takes your family’s wealth in to consideration, so a kid who inherits the family business gets counted the same way as a kid who comes from nothing. Additionally, it’s interesting to note that some specialty schools do really well (pharmacy) and some really poorly (art). Schools who don’t have a lot of different types of graduates are very tied to how the professions associated with them are doing.

Additionally, families with money probably tend to send their kids to private schools to begin with. For example, Stephanie and Shane McMahon (children of Vince and Linda McMahon, owners of the WWE) both went to Boston University and then promptly went to work for the family business. I don’t know for sure, but I suspect they never looked at UCONN when they were applying. Now this wasn’t every kid at BU, but having even a handful of the already wealthy can be enough to boost your lifetime earnings scores. In other words, we don’t know if BU is a better deal for a kid from a middle class household who wants to be a high school teacher, or if UMASS would be equal in those circumstances. We only know that overall, BU grads do better 40 years out.

So overall we don’t really know what we don’t know here, but we do know that many college stats leave out some confounders (who didn’t go to college, who was going to have money handed to them regardless of college status). Overall I think they are good for getting a general sense of things, but up close they have some issues. Like a Monet painting or something.