Women in science (biodiversity)

Biodiversity Exhibition on women in science before 1922.

Smithsonian

Who did they miss?

Add your suggested entries in the comments.

Drug dependence neurobiology

Thoughtful review article.

Everitt & Robbins, 2016

George Comstock on Wade Hampton Frost

 

From Frost Biography:

 

Mary Gover: I have not found a biography.

Project on history of women in epidemiological research 

Hopkins School ScD graduating class of 1923:

 

 

LT and LC biases in drug dependence epidemiology

Perhaps more than is the case for any other neuropsychiatric or behavioral condition, except perhaps suicide ideation, attempts, and planning, we should have special concern about left-truncation and left-censoring biases in "clinical investigations" of quitting drug use or being in remission or recovery after having become drug dependent.

Leaving aside these biases, think about denominator and numerator issues.

When we estimate newly incident use or dependence, we generally have large denominators to work with. The S.E. help us deal with uncertainty such as the relatively small numbers of users who will die within the first 1-2 years after first use, or soon after dependence onset.

In contrast, the estimates for quitting among cases are constrained by the relatively small number of observed cases in each survey. Wide S.E. as a result, even if we ignore the LT and LC biases.

Make sure you can describe LT versus LC in the survey context.

A paper that I think over-values our epidemiological survey evidence on quitting: 

URL for Heyman, 2013

Perhaps "clinical investigation" samples are better than epidemiological samples in this domain of inquiry, if they can document zlT and LC bias constraints better than we can?

"Solutions Epidemiology"

About one decade ago, the concept of "consequential epidemiology" was introduced, but it seemed a bit gimmicky, and perhaps a bit of special pleading about a field possibly in need of a morale boost.

Thinking about our currently Balkanized epidemiology, I am re-visiting the idea with a different term.

I call it "Solutions Epidemiology," and with this term I invoke a return to the roots of epidemiology as a disciplined speeded-up way to achieve near-term solutions to pressing public health problems.

Other bullet points:

1. A deliberate choice of health problems that now qualify as low-hanging fruit, by which I mean defined problems that can be solved in the near-term, and that might qualify either as epidemics or as the equivalent in localized settings: e.g., hospital-acquired MRSA infections; unintended pregnancies; high death rates if near-term or full-term infants.

2. A deliberate reach from the more languorous flow of research and resulting evidence in chronic disease epidemiology in the direction of an accelerated flow of evidence toward implementation science issues faced in public health departments year by year, as opposed to decaf by decade.

3. A deliberate alliance of academic epidemiology researchers with public health department officials charged with solving problems in real time.

4. A deliberate abandonment of Balkanized epidemiology that organizes itself in terms of domains of the explanation or 'scale' of its variables of interest (e.g., genetic epidemiology, social epidemiology, molecular epidemiology) in the direction of a more holistic epidemiology that organizes itself with a focus on a population health problem that requires a near-term solution.

5. Leaving in the hands of others all epidemiological problems that require a languorous approach and cannot be accelerated more rapidly toward actin research with near-term yield.

6. Differentiation from allied endeavors such as "implementation science" by virtue of a clear point of departure in terms of definition of specified populations and keeping track of the sequence epidemiologists use (1) to ensure at least a limited but 'built-in' external generalizability of our work, and (2) to study the sick AND the well in each population with due attention to bias in estimates when sampling frames or achieved samples depart from pre-specified populations for our studies.

I could go on and on, but my intent is simply to plant the seed of an idea for "Solutions Epidemiolgy" and to elicit some comments to guide its future development.

We have an opportunity to show Solutions Epidemiology at work in relation to the current heroin epidemic in US counties. There are counties with zero or few heroin overdose events, fatal and non-fatal. Other counties are suffering. What differentiates them? Isn't that an important epidemiological question that can be answered quite speedily in the direction of action research to see what can be done (e.g., via randomized trials) to keep the low rate counties at steady state low rates? And wouldn't it be important to learn that evidence sooner rather than later?

Operations research traditions come to mind, along the lines that Deming devised to find flaws in manufacturing processes. Surely, the work of Deming and other operations researchers deserves attention in the education of 'solutions epidemiologists' of the future. At present, I see few epidemiologists who know of Deming and his operations research tradition. Sad state of affairs, and the next generation of epidemiologists can do something about it.

Obituary for Deming

Your thoughts?

I will return to this topic later, after I elicit some comments and give the idea more thought. Maybe it's not worth pursuing.

(HC: Please fix any typos that I have not caught. No need for comment on them. Just fix them. Thank you!)

Importing Econometric Models into Psychiatric Epidemiology, A Start

Leaving aside earlier history of probability and statistics that anticipated least squares regression, a potted history in this area can start with William Farr and his 'hero of concept,' the Marquis de Condorcet (Nicholas de Condorcet).

Sidebar 1: Let me ask one of your to make a comment about Condorcet for me to edit and add as a later sidebar, with attribution. Why was Condorcet important to Farr? How might Farr have learned about him? You can answer these questions by reading my copy of Humphreys' version of Farr's selected works, but it might be easier to search for Condorcet (and France) in the online version of Farr's work. In the process, you will learn that Farr's studies in France were in part financed by an elderly benefactor, and that Farr was accompanied by a wealthier physician-friend to study with Pierre Charles-Alexandre Louis. Many characterize Louis as the physician who formalized clinical trial elements such as randomized assignments.

Sidebar 2: While his friend was hiking the Alps, what was Farr doing, which is pertinent to his abiding interests in what we now call neuropsychiatric epidemiology of developmental disabilities?

Sidebar 3: Find and describe Farr's post-Snow description of factors x,y,z, etc., mentally held constant while the source of water supply was allowed to vary, an early conceptualization of what was to become the familiar multiple regression model for a univariate response. (Hint: You can find a rendition in one of my papers.)

Fast-forward, and skip over a lot of progress until you encounter:

(1) Sewell Wright, son of P. Wright, and discover their contributions to this topic, with particular focus on path analysis, and its contribution to a field called "Population Genetics," which now is seen no more than rarely as an acedimic department in universities. Can you figure out why and how it came and went?

(2) Contributions made to model-building in econometrics, which should be understood by epidemiologists. Why not read January Tinbergen's Nobel Prize lecture, which is about as good an explanation of models and model-building as I have seen lately:

January Tinbergen Nobel Prize lecture, 1969

P.s. Next up, error terms, Keynes, and Haavelmo, as we work our way toward comparison of Directed Acyclic Graphs and their sequences of univariate response models with simultaneous equations models for a multivariate response world.

Heart-warming story about friends and phages

Tom Patterson and Steff Strathdee

Courage in action story: 

link