Forecasting health gaps
You’re an average person.
You don’t know what diseases you’ll get in the future.
You know people get diseases and certain populations get diseases more than others, enough to say certain things cause diseases.
You’re not quite the average person.
You have a strong preference against sickness and a strong belief in your ability to mitigate deleterious circumstances.
You have access to preventative research. You know if you don’t work in a coal mine, overtrain when running, and eat healthy, you can stay healthier than those who take those risks.
You know that some disease outcomes are less than predictable, so you want to work towards the available of treatments that fill gaps in the availability of therapeutics. For instance, you might want a treatment for HIV to be developed, in case you become HIV infected, since there is a risk of HIV exposure for almost anyone exposed to unprotected sex, since they won’t necessarily know their sexual partners entire serohistory (noologism?)
However, you don’t know which diseases you will get. So how do you prioritise?
Perhaps, medical device and pharmaceutical company strategies could be ported to your situation.
Most people, including non-epidemiologist researchers, don’t have access to epidemiology data sets.
Most people, don’t have the patience to read a book on medical market research
You don’t have the funds or connections to employ the world’s only specialist in the area of medical market forecasting.
At least he’s broken down the field into best practice questions:
Where can we find epidemiological information/data?
How do we judge/evaluate it?
What is the correct methodology for using it?
What’s useful and what’s not useful for pharma market researchers?
How do we combine/apply it with MR data?
The only firm, other than Bill’s, that appears to specialist in the area fortunately breaks down the techniques in the field for us:
Integrated forecasts based on choice modeling or univariate demand research to ensure that the primary marketing research is aligned with the needs of forecast
Volumetric new product forecasting to provide the accuracy required for pre-launch planning
Combination epidemiology-/sales-volume-based forecast models that provide robust market sizing and trend information
Custom patient flow models that represent the dynamics of complex markets not possible with cross-sectional methods
Oncology-specific forecast models to accept the data and assumptions unique to cancer therapeutics and accurately forecast patients on therapy
Subscription forecasting software for clients who would like to build their own forecasts using user-friendly functionality to save time and prevent calculation and logic errors
The generalisations in the industry, things that are applicable across particular populations, therapeutics or firms appears to be summarised here:
It’s 36 pages long, but well worth it if area is interesting to you.
So now you know how this market operates, what are the outputs:
Mega trends are available here
A detailed review is available here
Do they answer the questions, use the techniques proposed, and answer the ultimate question of what gaps exist in the provision of medical therapeutics?
I don’t know how to apply the techniques to tell. What do you think?
I know there are other ways to think about these problems.
For instance, if I put myself in a pharmaceutical company’s position, I could use strategic tools like Porter’s 4 forces and see whether a particular decision looks compelling.
The 2018 paper suggests that pain killers in developed countries are going to get lots of government investment.
So, does it makes sense to supply that demand?
There are a number of highly risky threats that might suggest say a potential poppy producer shouldn’t proceed:
**technological**
Disruptive biotechnology, such as genetically modified yeast which can convert glucose to morphine. There have been suggestions that this invention is overhyped
**political**
Licensing poppy producers who currently supply illicit drug producers
This said, the whole thing is very underdetermined so I suspect actual organisations are far more procedural in their approaches. What do you think?
Yesterday, I was surprised when I treated a patient (for a heart attack) to find that he later ended up in the ER. I’ve only treated a handful (exactly five!) with MI-like symptoms, and none of them had his low heart rate or controlled breathing. Surprise meant my model needed to be updated, and a quick look at this showed me that I really only needed two symptoms to be wary.
Physicians and other healthcare providers in the ‘algorithmic’ camp (vs ‘clinical’) essentially forecast diagnosis through probability-based predictions. Algorithms are also referred to simply as ‘flowcharts’ and as part of standard operating procedure. Medical algorithms incorporate a large number of established heuristics in a standardized manner, and have been shown to dramatically increase diagnostic accuracy. The medical branch of the military has access to a wealth of patient data, and they create algorithms based on that data.
In the private sector, there’s Medal, Apervita, Syapse and others. There’s a few private companies ‘democratizing healthcare data’ for a price. There’s even more information from health insurance providers, which tend to have their own healthcare data companies, which organizations can also access for a price.
DXplain uses Bayesian logic for diagnostics, and is open to physicians. TXdent does the same for dental care. Adjuvant! is publicly available to healthcare providers and exists for cancer patients. eMedicine is pretty great, but nigh useless to the layperson, other than a better version of WebMD, even though it’s a service offered by the same company. If you wanted to improve self-care, you might be able to get some mileage out of CATmaker, but it assumes you’re a provider, and I doubt a layperson would get use out of it. Tripdatabase is a curated database, mostly with links to studies from NIH, so you’ll encounter the paywall either way.
There’s also CDSS, which, while acknowledged as effective, is having problems with implementation due to the state of IT in healthcare.
Disease models exist primarily to forecast infection rates and risks. In addition to census data, there are publicly available datasets for infectious diseases.
All this to say that I suspect the specialists in your post are obfuscating the problem.
Medical datasets are not freely available to every medical institution.
Not every provider employs information gleaned from this data.
Algorithmic, probability based care is essentially controlled by a few companies.
Solve the problem, and you close part of the gap in English speaking countries. If you can read Dutch or Russian, you might be able to get access to all of the above (albeit with more geographically limited datasets) for free, but I don’t really know.
If you want to identify health risks for yourself, cross reference the probability of infection in your meatspace community with your own demographic (parents, habits, location, age, general health, medical history) information and take appropriate preventive measures.
The quote sounds like unalloyed marketing bullshit to me.
That question is easily answered by looking at the tables of mortality and morbidity.
I still don’t understand your point. The question of “how do you prioritise?” is entirely different from the question of forecasting the trends in the healthcare industry.
What about marketing is “bullshit”? Calling market research bullshit isn’t very constructive.
Tables of mortality and morbidity of what? Just mortality and morbidity? How does that tell you anything except when you’re going to die and with with what causes? Not all diseases of concern kill you.
Really? So do you prioritise concerns for other things without thinking about the future? Do you prioritise the threat of titanic bunny rabbit asteroids hitting the earth over pandemic influenza without accounting for the extreme inprobabiltiy of the former, and relatively higher probability of the latter.
Marketing’s reason for existence is to persuade you of something. Truth is not only optional, but in many cases is just contraindicated.
Of humans. Do you know what these tables are? And what is “morbidity”?
I? I do not prioritise these threats at all since there is nothing I can do about them.
Why do you believe that there only one specialist in that area? Each of the big pharma companies likely employs someone for the task.