Potential and Pitfalls of Health-Related Big Data. Ana Aizcorbe. March 6, 2014

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1 Potential and Pitfalls of Health-Related Big Data Ana Aizcorbe March 6, 2014

2 What is Big Data? Big Data is revolutionizing 21st-century business without anybody knowing what it actually means. MIT Technology Review, October 3, 2013

3 Outline of talk Methodological issues in traditional data sources vs Big Data Examples of health-related studies that have used various types of Big Data

4 Methodological Issues

5 There is a population of interest that is too expensive to measure directly on a regular basis A sampling frame is pulled from the population with basic demographic information (For 90,000 respondents, NHIS collects age, gender, race, ethnicity, type of health insurance, where they live, etc. every year) More detailed information is collected from a sample: nutrition, exercise, risk factors, medical care. Medical Expenditure Panel Survey (eventlevel data, with cost, diagnoses, how paid, etc.) 10,000 households NHANES includes the results of a physical exam conducted on respondents. (4,000 individuals) Traditional Government Surveys

6 Survey Design Starts with a question: What is the prevalence of diabetes and what can we learn about its determinants? A survey is designed with questions that speak to the question of interest Complex, cognitive studies The survey is administered to a representative sample Includes everyone you want covered Contains the right mix of different types of people (not all from Miami, not all bankers, e.g.) Additional science to deal with non-response

7 What people think of when they think Big Data Data obtained from Twitter, Facebook, Google, smartphones, GPS locations, and tiny sensors built into everything. Gartner. In 2001, a Meta (now Gartner) report noted the increasing size of data, the increasing rate at which it is produced and the increasing range of formats and representations employed. This report predated the term big data but proposed a three-fold definition encompassing the three Vs : Volume, Velocity and Variety. This idea has since become popular and sometimes includes a fourth V: veracity, to cover questions of trust and uncertainty. MIT Technology Review, October 3, 2013

8 The three V s

9 Volume: just dealing with the sheer size of it (big) Volume For example, according to Sullivan [2012], Google has seen 30 trillion URLs, crawls over 20 billion of those a day, and answers 100 billion search queries a month. Analyzing even one day's worth of data of this size is virtually impossible with conventional databases. (Google, Hal Varian) Health claims data: 7.2 billion records for over 50 million covered lives (MarketScan)

10 Volume: Large claims database

11 Variety Variety: handling the multiplicity of types and sources and formats, (unstructured) Example of structured data CCS Name of Disease 123 Influenza 124 Tonsillitis 127 Bronchitis 242 Allergic Reaction

12 Variety Unstructured data basically has no identifiable structure and is divided in two main categories: Textual objects: based on written language, which includes word documents, reports, s, blogs, web-pages Bitmap objects: non-language based documents including images, video, audio files Unstructured data requires tools with a capacity to capture, curate, manage and process data. Electronic Medical Records

13 Velocity Velocity: speed of processing Traditional statistical approach takes time Big data have the potential to be more timely Price stats provides daily updates on inflation (government statistics produce statistics at monthly frequencies, with lags). Credit card data, data on automobile purchases, scanner data available at weekly frequencies with little lag. Pressure to develop methods for real time monitoring

14 The fourth V: Veracity Veracity: how can we cope with uncertainty, imprecision, missing values, and yes, occasionally, misstatements or untruths? Studies of the statistical properties of Big Data are in their infancy Coverage Representativeness

15 Statistical Properties of Big Data Typically obtained for purposes other than the question at hand: Scraped from the web. Convenience samples data vendors purchase data from whomever they can then license it. No sampling strategy so no guarantee of proper coverage or representative properties Examples of coverage problems: Scanner data sold by Nielsen does not include Walmart (who refuses to participate) or purchases made over the internet. On line surveys exclude households without computers Examples of representative issues: One well-known health claims database obtains data from large self-insured firms (says nothing about people that work for medium or small firms)

16 Traditional methods vs Big Data Traditional statistical sampling methods were developed to deal with cost constraints How to use samples to obtain estimates of population measures (prevalence, cost of medical care, etc.) Increasingly, data are becoming available at lower cost Need to shift from best estimates given constraints to what would you do in a perfect world?

17 Examples of health-related studies that use these novel data sources

18 VBI explorations of novel data sources VBI is interested in the diffusion of pandemics Requires time use surveys, which are conducted regularly in the US They are conducting studies outside the US: India, Sweden, Israel, UK, e.g. They are conducting internet-based time use surveys for their study (AMT) Fitbit monitoring of physical activity

19 Medicare Claims Can purchase 5% or 20% representative sample No statistical representativeness issue Coverage problem: don t observe medicare patients in Medicare HMOs Dartmouth Group: Regional differences in the cost of medical care Govn t surveys don t provide very granular geographic detail MEPS supports 4 regions in the US/Medicare claims allows analysis at the county level

20 MarketScan data Issues No behavioral information (diet, exercise) Zip only at 3-digit level; no race No outcomes (lab results, e.g.) Exiting the data Possibilities Study diffusion of new technologies Study drivers of spending growth

21 Rare conditions Treatment of Heroin Addiction Cervical cancer Organ transplants Examples of MarketScan Studies Geographic detail 3-digit zip codes can be used to document differences in utilization and costs across MSAs, counties, like Medicare claims Contains information on type of health insurance Effect of drug formularies on choice VBI is partnering with the data vendor to study the statistical properties of their sample

22 The promise of Big Data Big Data is revolutionizing 21st-century business.. But, we need to learn more about what it can tell us.

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