Old Age Psychiatry: The Baby Boom Generation and What to Expect: Performance-Enhancing Drugs

A scholarly review through 2004-5 that identifies some important gaps in epidemiological evidence that remain unfilled:

Link to the review

Where I found this interesting report: 

Link to the page

Re-Thinking Epidemiology for the Mid-21st Century

Let me get started with a provocative idea. Before 1980, epidemiology stopped generating new ideas and theories. What passed for theory in epidemiology had become little more than borrowing from other scientific disciplines. The rest of this blog post is about that idea.

<snip>

 

Anyone who knows me appreciates that I was destined to be no more than a limited 'futurist' as compared to the best futurists. I lay the blame on the Catholic Church, becoming an altar boy, studying Latin for more years than I wish to count (including a year of self-study), and gaining too much respect for early origins and roots. Think about me as a conservative radical, conserving the roots. I might be too 'conservative' to be a good futurist.

 

At the same time, I also am known as an iconoclast, because I do not hesitate to challenge bogus concepts when they creep into our field of epidemiology, among which I can specify 'risk factor' and 'reverse causality' as special within-field jargon terms that have almost no communication value when we, as epidemiologists, try to describe our evidence to audiences beyond the local 'tribe'. (Worse yet, there are non-tribe members who actually think they know what we mean when we use these jargon-terms, as has occurred when my non-baseball friends have thought I'm talking about mental health when I use the term 'screwball' when describing a baseball pitcher.)

 

With that background in mind, you can imagine my response when I was giving an end-of-semester talk to my research trainees and students about some challenges that epidemiologists are going to face during the next few decades. I thought back to two issues raised when I was early in my studies of epidemiology back in the late 1970s: (1) an idea that David Mechanic and Bill Eaton mentioned about epidemiology having run out of ideas and theories, and (2) an idea that Bruce Dohrenwend offered about epidemiology being little more than a toolkit or collection of methodological approaches.

 

For my students, I framed my response to a hypothetical question about the challenges they might face in relation to these two ideas, and I offered this statement: "The most serious challenge you will face in your career is running out of substantive ideas and theories that deserve testing."

 

For the past day, I have been thinking about what I told them, and I realized that there might be an empirical indication of the degree to which epidemiology, as a discipline, has started to run out of ideas and theories, and has entered a terminal (?) stage of being a methods-oriented field of study as might be distinguished from a science, akin to the way we'd differentiate 'operations research' as a field of study without thinking about it as a 'science' in the generally accepted sense of the term.

 

My realization of an empirical approach required a reflection on (a) an early expression of Wade Hampton Frost's idea that epidemiology has to do with infective agents and infectious diseases, and he was uncertain about whether it made sense to think about epidemiology as applied to other conditions that did not originate with an exposure to an infective agent, (b) Mort Levin's assertion in a Milbank Memorial Fund session that Ernie Gruenberg had organized, which amounted to this statement: "There can be an epidemiology, and there can be a set of psychiatric disorders, but I'm not sure there needs to be an epidemiology of psychiatric disorders," and (c) a general principle I first learned from my PhD advisor Len Schuman at University of Minnesota, which also later was stressed by my local colleague and friend Nigel Paneth, along the lines of the advantages gained by restricting the prefix adjective for epidemiology to the domain of individual disease entities or disease classification names, as in 'cholera epidemiology' and 'infectious disease epidemiology,' as opposed to alternatives based on prefix adjectives that shifted attention from the epidemiological response under study in the direction of the causal explanations under study (e.g., 'genetic epidemiology').

 

When I first appreciated this point as it was being made by Len Schuman in my early years after starting graduate studies in epidemiology, I had not read Wade Hampton Frost nor had I met Mort Levin. I was out in Minnesota, ignorant of Mechanic's medical sociology program at University of Wisconsin, and completely ignorant about Bruce and Barbara Dohrenwend's social psychological research program at Columbia. Nonetheless, it was not long until I was reading the Lilienfeld-Gifford 'Chronic Diseases and Public Health' book assigned by Schuman, and I recognized a clear distinction being made between 'Infectious Disease Epidemiology' and 'Chronic Disease Epidemiology' (hereinafter abbreviated as ID and CD).

 

[At this point, I stopped for refreshment and I pick up the thread down below.]

 

Now that I have drawn attention to this separation of 'ID epidemiology' from 'chronic disease' epidemiology, I can offer a thought experiment that some enterprising young epidemiologist might try to pursue. (The most valuable thought experiments are those that can be investigated empirically once the experiment has been specified.)

 

The empirical work involves the study of time trends in appearance of adjectives used to modify the term 'epidemiology' as can be seen in traces such as Web of Science or Google Search or JSTOR or other bibliographic search tools that have emerged as a consequence of internet advances.

 

An initial interesting time trend will depict the relative frequency of 'infectious disease epidemiology' and 'chronic disease epidemiology' as these terms appeared in the published literature, year by year, over time. Here, the expectation would be an increasing frequency of both terms, but in addition, even with de-trending, the ID/CD ratio might be expected to decline, at least until the mid-1980s, given (1) increasing prominence of work in CD epidemiology until (2) emergence of HIV/AIDS research as priorities for NIH before 1990. (I have not studied these trends so these expectations are based solely on my 'seat of the pants' reckoning, and I might well be wrong.)

 

A savvy approach will put into play de-trending research approaches that compensate for generally increasing frequencies of 'epidemiology' as a search term, based on knowledge that NIH and other funding sources increased their investments in epidemiology across these years. A more savvy approach will take other interesting issues into account, such as might be illuminated by studying citation counts as opposed to what can be learned via 'topic' or 'title' restricted searches over these years.

 

But I leave these issues up to others in order to focus on the possibility that epidemiology's de-celerating rate of innovative ideas might be reflected by studying (1) the relative frequency of 'disease entity' adjectives positioned as prefix terms before 'epidemiology' appears as a noun, and (2) the corresponding relative frequency of 'non-disease-entity' adjectives positioned as prefix terms. Here, by 'disease entity' adjectives, I'll use as examples 'cancer epidemiology' and 'dementia epidemiology.' For 'non-disease-entity' examples, I'll use examples 'social epidemiology' and 'genetic epidemiology' or 'nutrtional epidemiology' or 'life-course epidemiology.'

 

It is fair to ask why the relative frequency of these prefix adjectives might convey something important about the content of epidemiological research and the inherent generativity of epidemiology as a source of new ideas and theories. I have little more than a simple answer of the following form, expressed in relation to 'Types' of epidemiology:

 

(Type 1) When the prefix adjective is focused on the response variable, such as a cancer or a dementia, then the domain of potential explanations is expanded almost infinitely to cover whatever might be accounting for variation in the incidence rates and prevalence proportions for that response. The epidemiologist is faced with the challenge of drawing from multiple disciplines in an effort to understand what might account for the population-level variations in the epidemiological parameters. Let's think of the specification of the response variable as one dimension of epidemiology.

 

(Type 2) When the prefix adjective is focused on a domain of explanation (e.g., 'social' or 'nutritional' or 'genetic'), there is a greater constraint on the range of sources of variation. The epidemiologist is faced with a more circumscribed challenge, which is to ask whether and under what circumstances that domain of explanation might be more or less important in attempts to account for population-level variations in the epidemiological parameters. Let's think of the specification of the explanatory domain as a second dimension of epidemiology, when 'second' denotes a time sequence rather than my own pre-judgement (prejudice) about the ordinal ranking of importance or public health significance.

 

(Type 3) When the prefix adjective is focused on a domain of process (e.g., life-course'), there is a third dimension that can be considered quite separately from the first two dimensions, and the third dimension has a correlation with time. This dimension might reflect age (considered subgroup by subgroup, as in a cohort-sequential study), or aging (considered as a progression from one birthday to the next), or as developmental (as in the relatively fuzzy sets that we use to distinguish developmental stages along the lines of (a) pre-term, full-term, etc., with respect to measured gestational age of a newborn, or (b) 'pre-pubertal,' 'pubertal,' etc., along the lines of the Tanner stages, or (c) pre-senile versus senile, as in the differentiation of age of onset of dementia syndromes based on when the syndrome was detected, earlier for those with trisomy-21, at mid-life for those with certain familial histories of early-onset, or later for those formerly known as 'sporadic' due to the absence of the family history of mid-life onset of the dementia syndrome. This is a third dimension, separable from the first two dimensions.

 

It is my speculation that an increasing frequency of prefix adjectives of type (2) and (3), relative to type (1), over time, can serve as an indication of the degree to which epidemiology, per se, has stopped generating its new ideas and theories, and instead is turning in the direction of other disciplines to fill a gap of imagination or generativity that otherwise would not require turning toward these other disciplines.

 

I have no idea whether this speculation has merit. I offer it as a suggestion for epidemiologists and historians of science who might wish to seek quantitive evidence about the degree to which a discipline has 'run out of its own ideas and theories' versus the alternative of borrowing from other disciplines.

 

I look forward to comments and to any empirical evidence on these matters.

 

 

Some dance to remember; some dance to forget... or drink in this case?

Interesting article 

https://www.ncbi.nlm.nih.gov/pubmed/28391598

I am fascinated by the finding that low parenting predicts LESS alcohol use in the second group of adolescent girls (p=0.057, almost). Are they self-disciplined precocious occasional drinkers? Is low parenting an indicator for something else for this group? Also noticed the correlation between alcohol use and low parenting is negative at baseline for the second group. They are definitely a special group.

I think the authors raised an important point. We often look at the average assuming cases and effect estimates are homogeneous, but more often they are not. 

My thoughts about Jim's question on whether it is worth the while to publish cross-sectional evidence on parenting-alcohol relationship: it would come down to the assumptions. If it is safe to assume the relationship is a positive feedback loop, then yes. If it is a one-way positive or negative relationship, then yes. Would provide initial estimates for future longitudinal studies. If it is a two-way negative relationship or a mixture of positive and negative relationship, then probably no, because it becomes complicated very quickly depending on when kids are assessed in a cross-sectional survey. 

But again, that is population average. Does it apply to everyone? Maybe not. In addition, there may be unobserved heterogeneity in the background. Parental drinking, peer affiliation, number of kids in the household, neighborhood environment... 

Comments from Jim:

Chicken and egg problem: What if drug use causes parents to relax their supervision and monitoring?There is a quite rational decision in epidemiology to start with relatively inexpensive and logistically feasible study designs, such as case-control research, before moving on to the more expensive prospective two-wave or longitudinal multi-wave designs that can throw more light on issues such as uncertain temporal sequencing. (Here, some epidemiologists would turn to the within-field jargon term of 'reverse causality,' but let me again warn that communication across disciplinary boundaries is far more important than any display of group membership as represented by use of within-field jargon terms. 'Uncertainty about temporal sequencing' is apt to serve well in a public forum or research article, whereas epidemiology 'bull sessions' might be appropriate venues for the within-field jargon of 'reverse causation' and the like.)

This new contribution is a very interesting longitudinal multi-wave study that deserves attention because it has a capacity, within the limits of its assumptions, to estimate the degree to which drinking might cause parents to back off and relax their supervision and monitoring behaviors. The assumptions, both conceptual and methodological, should be studied with care, and after you consider the evidence, ask yourself whether it was a mistake for journals to publish the early articles with no more than cross-sectional or two-wave study data on the monitoring hypothesis.

If interesting comments are provoked, I will leave a later comment to tell you what I  think.

Opioids epidemic: points to ponder

Article can be read quickly  here.

But watch for a bit of sophistry in there.

On the legalize side: Which legalization model? EOH model? Nicotine model? State monopoly model?

On the prohibition side: State pays minimum of $36K/year for non-productive and life chances-damaging incarceration? 

State actively discourages pharmaceutical R&D via market constraint (won't allow marketing except when the product meets FDA criteria for each medicine to be used in practice of medicine)?

What if the only intended use is 'to get high' under controlled conditions of 'recreational use' as might be set by the state regulatory agency for laser tag or paintball game establishments.

At present, the pharma R&D cannot innovate in this domain due to the federal government's creation of a monopoly for 'unethical innovators' such that 'ethical Pharma' could never make a profit on a drug developed with non-medicinal 'getting high' in mind.

Analogy in auto industry:

You cannot build a car 'for speed' unless its only use is on a non-public track.

Plus, we don't allow anyone to create a non-public track.

Consider a flight around the moon.

Govt says you cannot build a space travel device unless its only use is for military-industrial-scientific purposes.

Plus, you cannot launch it without govt permission.

So, at present, it's easier to raise capital (and find willing consumers) and to develop commercial space travel than it is to develop safe alternatives to our ancient plant-origin drugs with inherent toxicities: EOH, nicotine, cannabis, cocaine, psilocybin.....

Please, each of you, sign up, and monitor situation.

NSDUH restricted data portal and some issues of inferential statistics in large sample context.

Sign up here to be notified: 

sign up

See B.7 of this report for issues faced when comparing state estimates, plus useful information about covariances (sometimes ignorable).

http://samhda.s3-us-gov-west-1.amazonaws.com/s3fs-public/field-uploads/2k15StateFiles/NSDUHsaeMethodology2015.htm#b.6

On risk factor, and its essential meaninglessness

Perhaps the title should be: Its essential over-abundant meaningfulness, because it refers to so many different things, and thereby has little communication value outside the tribe of epidemiologists (JCA : 28 April 2017).

Words lose their value when you must define them clearly each time you use them. As with 'addict' or 'addiction' or 'going crazy,' here we have a term that, by itself, carries little or no information value.

December 2016, BMJ: A 'taboo' against using 'risk factor' in research reports, 

 

See Section 1.12.3.4 in this book chapter from 1998:

Anthony & Van Etten, 1998

Thanks to friend, David Vlahov, for bringing the BMJ article to my attention!

The Prevescalation Concept and its origin

Raymond Niaura, in Addictive Behaviors, 2017:

 

Race? Or ethnic self-identification?

Thoughts?

The History of Racism I Didn’t Want to Share By JANE MADEMBO; APRIL 7, 2017

 

Xanax human interest story

Human interest story: Xanax (alprazolam, anti-anxiety benzodiazepine)

Story March 2017

Incremental Validity - some thoughts

This problem might not occur to those of you who don't devise new measurements, but it is an interesting topic anyway, and the article introduced me to a new validity concept: "incremental validity"

It's what you get when you can make an established screening test into a better predictor by adding new information. Or, as they cast the problem, when you want to evaluate prediction from one already accepted test and you think your test will perform better.

I'm not entirely convinced that p-values and significance testing are the right tools to solve this problem in the first place. I think more in terms of AUC parameter estimation.

Nevertheless, read it and see if you can convince me that the significance testing approach is the correct one.



 

Race, skin color, ethnic self-identification, or...

Interesting arguments about how to objectively measure 'race'
skin color, not race

Now the question is "is race just skin deep?"

Would a non-white person be treated exactly the same way if he/she is in rural Virginia compared to NYC? Would their outfits or body arts change people's perception? Does skin color simplify or complicate the problem, or just shift one complexity to another? 

What about a person who spent some time on the beach comes back with more melanin? Temporarily discriminated until his/her true color comes back?

Is a lighter makeup the grand 'fix' for problems that have lasted for decades?

I wonder if one day aliens from a galaxy far far away come and invade the Earth. They happen to be black, what happens? Are we 'Earthlings' no matter what color our skin or hair is, or are we judged by our degree of resemblance to aliens or our degree of aggression? Maybe the shape of our ears will be the measure then.

D. Brooks gives advice about response to opioids problems in the U.S.

Opinion piece

Thoughts?

Opium, labor, capitalism, and Peru

Over the past ~25 years of work with colleagues of and in Peru, I sadly have not become any sort of area studies expert, but I have learned a little bit about Peru's many cultures, including an Afro-Peruvian tradition that dates back to inhumane slave labor on plantations.

(There are some manifestations of a Black Power movement in Peru, but there also are manifestations of other themes.)

Chinca is a "Chincha Alta" region city not far South from Lima. I encountered it en route to Pisco and the Ballestas Island Reserve, and a view of some Nazca lines, but wish to return for closer study. Chinca Baja is an agricultural region just to the North of Pisco. 

Pinch map to see Chinca relative to the Ballestas.

Peru map local to Islas Ballestas

I offer a glimpse of "local culture" in a non-judgmental way, but I'd wish to know more about the local views of Chinca citizens. (I know my Lima colleagues were not entirely happy to see these lifesize 'statuettes' close by the central Plaza.)

 

The image started my search to learn more.

Here, I offer an example of scholarship about "coolie" labor and transportation to Peru for cultivation of opium. There is much to be learned about this facet of culture and history in Peru. Let me know if you would like to pursue this lead. Two links:

Link to Project Muse article:

http://muse.jhu.edu.proxy1.cl.msu.edu/article/214706/pdf

Here is a link to more general bibliographies on opium and China:

Opium and China

Here is a recent article of interest on the Opium wars:

Possibly can be found via MSU media educational videos? Netflix?

Goodcheer!

J.

Aaron Joshua Rosanoff (26JUN1878 - 07JAN43)

I decided to read Aaron J. Rosanoff's body of published ideas in 1977.

(In  later note, I will mention Gruenberg's PELP collection, its scanned e-rendition, and what he taught me about using it. I found Rosanoff in that collection.)

Much of what I have done can be traced to his work. Alas, too late, I learned he was possessed by Eugenics ideas that I cannot support.

I just found an online version of a text of interest:

https://archive.org/details/manualofpsychiate4rogu

Note:

Rosanoff worked at Kings Park, possibly this one:

https://en.m.wikipedia.org/wiki/Kings_Park_Psychiatric_Center