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Reddit mentions of Epidemiology: An Introduction

Sentiment score: 3
Reddit mentions: 3

We found 3 Reddit mentions of Epidemiology: An Introduction. Here are the top ones.

Epidemiology: An Introduction
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    Features:
  • Oxford University Press USA
Specs:
Height9.1 Inches
Length0.7 Inches
Number of items1
Release dateJune 2012
Weight0.88405367062 Pounds
Width6 Inches

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Found 3 comments on Epidemiology: An Introduction:

u/tavoundji · 3 pointsr/publichealth

Both posts above are great advice. You have a short amount of time to accomplish as much as possible, and practical experience is invaluable if you want to be competitive in the job market. I'm in a 2-year MPH program (in Epi), and finished all the required coursework in a year and a half taking 5 classes. The workload hasn't been too bad, and having a part-time job on the side shouldn't be too much of a problem (except maybe around midterms/finals, obviously).

A friend of mine who was already in the MPH program recommended reading this book before I started, and it helped get comfortable with Epi, so I didn't feel like I was plunged into a whole new world when classes began: http://www.amazon.com/Epidemiology-Introduction-Kenneth-J-Rothman/dp/0199754551/ref=dp_ob_title_bk

u/arbiter_of_tastes · 3 pointsr/datascience

Whoa, there. Healthcare data scientist here, mainly working in areas like clinical epidemiology and with a background in health services research and pharmacoepidemiology.

First, kudos for having questions and reaching out for help. This is my opinion, but health care is different from other sectors. The work you do has the potential to affect people in visceral, fundamentally life-changing ways...such as recommending a patient should or should not get treatment. Or a patient should or should not be placed on end-of-life-care...that a life-threatening complication is or is not related to a pharmaceutical on the market. Point just being - I think this sector carries responsibility that many other sectors don't.

Second, are you at a pharmaceutical/related organization? If so, there should be qualified biostatisticians/epidemiologists/psychometricians/health economist/something similar to sit down with you and help you figure you this out.

Third, you said you study 'data science and knowledge engineering', but I'm not sure what your curriculum consists of - do you study causal inference? If you don't, it's the most important topic you need to be familiar with (not competent, mind you). Here are several references that could get you familiar with identifying and dealing with bias and confounding, and designing experiments to assess causal relationships instead of just association. In healthcare you have to know when a question warrants a causal analysis vs a predictive or associative one. If a causal analysis is needed, an epidemiologist or biostatistician might likely do that work, but it certainly helps to know what a DAG is and how to read one.

https://www.amazon.com/Epidemiology-Introduction-Kenneth-J-Rothman/dp/0199754551

https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Fourth, I'm hesitant to suggest anything about your dataset, because I still only have a rough idea of the details. Also, it sounds like you've got a psychometric dataset, and I've never studied psychometrics. I will say, though, that the question (hypothesis) being asked should really drive the analytic approach. Is the goal to look at a homogenous population and find that there's something about that causing them to require or be adherent to treatment? Do those results then need to get applied to a diverse, heterogenous population? That's a very high bar to achieve for experimental purposes. Is it enough to look at some data and say that certain characteristics are associated or predictive of certain outcomes? That's a much lower bar from an experimental standpoint and probably an analytic standpoint, too. If there is a selection bias, I think that's only relevant if there's a desire to extrapolate the study results to a different population. As you point out, if the desire is to generalize results to a larger population it's likely a significant problem that would require a intentional experimental design to address. If the company you're working with doesn't recognize this or can't have a qualified person explain why it's not a study design problem, you're working with bad people that likely don't know what they're doing. I've colloborated with several software/'health analytic' companies and startups that are like this, and it's why I'm dis-trustful of all health analytic software until proven.

Hope this helps!

​

u/ar_604 · 1 pointr/medicine

There's a good book by Kenneth Rothman (one of the top epidemiologists in the world) that would be a good intro into study design and how to interpret findings. Epi folks often called it the 'Baby Rothman' because he's also written (arguably) the top text in epidemiology as well. The two books work well in tandem as well.

Edit: Just to add, the FDA and AHRQ put out pretty good guidance documents as well that explain the nuances of trials, observational research, etc. I actually used them a fair bit when I was studying for my comprehensive exams. If you're interested, I can fish out the links.