Friday, 17 Nov 2017

You are here

Digital Epidemiology

Today I came across two interesting sessions given by Gossec Laure from France and William Dixon from the United Kingdom. They talked about digital epidemiology in this changing world, as there are approximately 15 billion connected devices and more than 80,000 medical apps.

In the past, players of epidemiology studies were only rheumatologists and patients who voluntarily shared data. However, these players are changing - patients are going from active to passive givers of information. Popular devices which are used to collect passive information are- smart phones, cars, watches and activity trackers. Other not commonly used sources include clothes, houses, UV patches, connected pill boxes etc. An important source of passive data is when people look up their symptoms in search engines like google. They believe they are looking for answers, but they also end up giving data. For example, influenza epidemic onset is correlated with google searches for symptoms.

Registries currently provide actively collected data. Linking data sources biobanks, claims or electronic medical data provide a rich resource. Genetic datasets containing large scale sequences are now available. NIH collects data on 700 patients on a daily basis and has archives of 560,000 individual samples. All of these are considered “Big data”.  Three components to big data are

1)      Volume of data in terms of number of patients or data points

2)      Velocity which is mainly the frequency of input 

3)      Variety is mainly heterogeneity of data

Some peculiar examples of “Big data” studies using Sociological data are Neighborhood environment wide association study (NE-WAS), or using social media disease reporting for qualitative analysis.

With all the available data, it is important to develop techniques to accurately analyze data, and make better use of new predication models and mitigate bias. Machine learning statistics is the newest technology available for this and may be better as they don’t have causality hypothesis. Machine has the ability to learn without being explicitly programmed. It comes up with prediction models which works best after several reiterations.

It is important to recognize the challenges with the digitalization and “big data” :-

-          Ownership of data and privacy policy for data protection,

-          Commercial use of the data by pharmaceuticals/ corporate giants and insurance companies may be worrisome and

-          Losing sight of the priorities given so much available data

In an ideal digital epidemiology world, we could have “cradle to grave” records in order to predict and improve patient outcomes.

Disclosures: 
The author has no conflicts of interest to disclose related to this subject

Add new comment

More Like This

The RheumNow Week in Review - 17 November 2017

Dr. Jack Cush reviews the news and highlights from the past week on RheumNow.com. This week he covers FDA warnings on gout drugs, steroid use in Australia, biosimilars lost savings and methotrexate hepatotoxicity in psoriatic arthritis (PsA).

Podcast of ACR17 - Day 4

Care to learn what you missed at last weeks ACR 2017 meeting in San Diego?  Here are 4 one hour audio podcasts - each with a compliation of 2-4 minute reports from Drs. Cush, Kavanaugh, the RheumNow Faculty and other rheumatology thought leaders and researchers.  Another good way to learn from RheumNow.

The ACR17 RheumNow Week in Review - 10 November 2017

This special edition of the RheumNow Week in Review covers highlights of selected sessions from the 2017 ACR annual meeting in San Diego. Dr. Jack Cush reviews lupus and the microbiome, daily podcasts, pregnancy and lactation, osteoporosis drug holidays and screening for pulmonary hypertension.

Many thanks to the RheumNow Faculty for their work and expertise!

Podcast of ACR 17 - Day 3

Check out this compilation of our ACR17 Day 3 broadcasts, merged into a single one hour podcast !

ACR 2017 - Day 3 and 4 Highlights

Day 3 at the annual meeting was rich with information. Yet the most anticipated and best attended session was the Late Breaking Abstracts and the session revealing the new ACR/NPF Guidelines for Psoriatic Arthritis (more on the latter in another report).  Day 4 was full of review sessions and a modicum of original content and for me, the 7:30AM Rheumatology Roundup.