Big Data and health - Center for Economic Studies (CES)

Big Data and health

                   Melanie Lührmann   Royal Holloway, University of London and IFS

                   September 16, 2020

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Big data in economics

    Data innovations relevant for economic analysis in recent years
    • increasing availability of administrative data, and linkages
      between administrative and survey data
    • emergence of (social) network, mobility and other “online” data
    • huge cost reduction and increase in smaller-scale online surveys

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Administrative data in health

   1. Germany:
            • claims data from health insurers (Farbmacher et al 2020)
            • administrative hospital claims data (Reif et al. 2018)

   2. UK:
            • hospital episode statistics: contain information on diagnosis,
              hospital spells, treatments,...
            • UK Biobank: an online survey of ca. 500,000 individuals between
              2006 and 2010, linked with administrative hospitalisation data,
              birth and death records, and soon GP data
   3. US:
            • Medicare claims data: 20% subsample (also contains data on plan
              choice in Medicare Part D)

   4. Mortality data from death records, sometimes linked to census
      data (e.g. UK Celsius panel)
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Administrative data: pros and cons

   + typically large samples → this is particularly attractive to study
     relatively rare events such as severe health shocks (heart attack,
     stroke, cancer, mortality etc.)
   + typically representative (large) subsamples of the population or
     population data
     - typically small set of individual socio-economic characteristics
     - no (direct) information on perceptions, preferences, attitudes,
       expectations, subjective well-being
   + sometimes long time series available → particularly important for
     the study of life cycle health or long-run impacts of interventions

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(Social) network and “online” data

    • particularly important now to study Covid-19 spread and impacts
    • mobility data from mobile phone companies, google,....
    • social network data from twitter, fb, ...
    • knowledge and information spread data from google trends

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Example: Google trends data, Covid vs. Brexit, UK, 2018
to today

           Figure: Google trends data, Covid vs. Brexit, UK, 2018 to today

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Example: Kuchler et al. (2020)

    • use aggregated data from Facebook
    • Social Connectedness Index: probability that Facebook users in a
      pair of regions are Facebook friends with each other (Bailey et al.,
      2018)
    • show that COVID-19 was more likely to spread between regions
      with stronger social network connections
    • Areas with more social ties to two early COVID-19 hotspots
      (Westchester County, NY, in the U.S. and Lodi province in Italy)
      generally had more confirmed COVID-19 cases at the end of
      March
    • in the U.S., a county’s social proximity to recent COVID-19 cases
      predicts future outbreaks over and above physical proximity

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Example: Kuchler et al. (2020)

     Figure: Social connectedness and cases per 10k popoulation, Westchester

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(Social) network and “online” data: pros and cons

   + typically large samples
     - typically selective in terms of platform use and general online
       usage
     - typically linkable to geographic areas, but small set of individual
       socio-economic characteristics or none, so requires linkage to
       other data
     - direct information on choices (i.e. location, information
       consumption and communication)
   + available in high frequency
   + also available retrospectively after large shocks (unanticipated by
     researchers)

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Researcher-administered online surveys: pros and cons

   + cheap
   + tailored to the researchers’ interest
   + allows fast response surveys (depends only on resources and speed
     of researchers)
   + sample size on demand
     - intransparency about representativeness of company-provided
       online samples
    - difficult to check reliability of answers compared to
      interviewer-conducted surveys (measurement error)
    • typically selective (depend on sample recruitment process,
      incentives to participate, tastes?)
     - typically small set of individual socio-economic characteristics as
       billable per question → creates datasets with limited reusability
       for other researchers, so at macro level not so cost-efficient?
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Researcher-administered online surveys: sample
representativeness

    The representativeness of online samples depends on the selectivity
    of
    • internet participation
    • platform participation
    • survey participation
    • survey-firm provided incentives
    • attrition and item non-response
    • ...

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Researcher-administered online surveys: sample
representativeness

    Can we fix this through weighting?
    • Couper et al. (2007) argue that simple “calibration weighting”
      (by age, gender education) is unlikely to appropriately account for
      the selection in online survey responses
    • large panel studies such as GSOEP, PSID or Understanding
      Society can account for non-response selection by developing
      probabilistic weights (through learning about initial non-response
      and subsequent survey attrition over time)
    • they also allow direct observation of non-response and analysis of
      selectivity
    • for more on inverse probabilistic (IP) weighting, see Wooldridge
      (2002)

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Example: Crossley et al. (2020) - Impacts of COVID-19
or: the power of weights

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Crossley et al. (2020)

    • null is rejected for calibration weights for all variables
    • under full IP weighting, the null is rejected only for the
      percentage owning a home with a mortgage.
    • using calibration weights, one would
            • overestimate the fraction of individuals reporting that they were
              living comfortably by 4 percentage points
            • overestimate the fraction managing to save some of their income
              by 8 percentage points (not shown)
            • underestimate the fraction of individuals living in social housing by
              9 percentage points (not shown)
    • Hence: sampling matters and weighting is non-trivial in online
      surveys
    • in experiments that focus more on internal validity, this is less of
      a concern (as they randomise within the potentially
      non-representative sample)
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Particulars in health economics

    • power is often a major concern as prevalence rates for many
      conditions are low until later stages of the life cycle (where
      selective mortality concerns kick in)
    • exceptions: studies of risky lifestyles, nutrition, bodyweight and
      self-rated health
    • hence: large reliance on administrative data
    • renewed interest in contagious diseases due to Covid-19 and
      widespread anti-vaccination movements are likely to result in
      rising interest in network data
    • additional type of new(ish) data:
      genetic data and biomarkers
      → typically elicited in the context of large surveys (ELSA,
      SHARE, UK Biobank, HRS...)

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