Longitudinal Analysis of Health Care Data in Large Populations: Data Configurations and Methods

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Longitudinal Analysis of Health Care Data in
Large Populations Data Configurations and
Methods Daniel Gilden JEN Associates,
Inc. Cambridge, Massachusetts USA
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Data Applications What is the process we are
supporting?

• The life cycle of applied research
• Research
• Policy development/strategic planning
• Active program management
• Evaluation of impact of policies/therapies
• Research...

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What Do We Measure?

• People
• personal and family demographics
• geographic and personal environment
• economic status
• diseases
• disabilities
• utilization of medical services/therapies
• healthcare provider affiliations

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Where is the Data?

• Birth and death records
• Public health disease registries
• Personal survey data
• Economic information by local area
• Administrative data
• cost reports
• aggregate purchasing information
• therapy level payment records
• enrollment data

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How do we Understand What is in the Data?

• Turning data into information
• the poorer the data source the more statistical
  manipulation will be required
• sample extrapolation
• insufficiently specified models
• the denser the source the easier the analytic
  problem but
• costly data preparation
• complex analytic file production

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Matching the Data to the Research or the Research
to the Data?

• Can the data support the unit of analysis?
• program
• population
• person
• Are the exposure and outcome measures available?

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Everybody Has Time But Can We Handle It?

• Interacting time with the unit of analysis
  multiplies both the data processing challenge and
  the analytic opportunities
• Cross-sectional
• standardized time bucket for simple comparative
  analyses
• Longitudinal
• following trajectories over time, why?
• to understand the past and predict the future

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Standardized Snapshot Measure Using Claims, Cost
Reports or Pharmacy Purchasing Records
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Expenditure Trend from Treatment Claims
50 Increase in Monthly Expenditures Over 3 Years
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Population Trajectories in Costs and Utilization
Rates
Non-Schizophrenia Dx Number of Users and Costs
Per User Driving Cost Increase
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Person-Level Report of Evolution in Therapy
Re-fills
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Data System Configurations

• Each example implies a different data
  infrastructure, from the least to most
  complexbut all the profiles are reasonable
  starting points for research
• Match the question to the datado not let your
  eyes become bigger than your resources
• The important point is to start and not wait for
  better systems or more detailed data... systems
  grow organically

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Steps to Developing a Research Data Infrastructure

• Data Inventory
• what is currently available
• Access model
• how many users and how deep the yield
• Analytic method selection
• analysis type determines resources
• Hardware and software follow the methods
• Design for economy - planned profligacy
• The human element - wheres the talent?

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Data Structures

• Vertical Data Archives
• fixed length
• a single record per observation
• research area related data fields
• Horizontal Analytic Records
• aggregated to the unit of analysis
• summary variables
• time oriented arrays

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System Design Goals

• Rational data structures minimize hardware and
  software requirements and reduce analysis time
• Reusable data structures and methods
• Select a data analysis strategy and stay with it,
  re-use data, re-use methods, never reinvent the
  wheel
• Design a data update and expansion strategy in
  advance to minimize disruptions and data damage

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Sample Configuration Typical US Source Data

• Large US State, 2.5 million Medicaid
  Beneficiaries
• Three Years, 800 million treatment records
• Monthly Enrollment Denominator
• Integrated and linked Pharmacy, Physician,
  Inpatient, Post-acute Care, Long Term and Chronic
  Care
• Payments, diagnoses, therapies
• Linked to regional economic profiles
• 2.5 million person level summary records

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Successful System Configuration

• 1 Tera-Byte Disk capacity
• 1 Giga-Byte RAM
• 1 Off the shelf Dell PC, Windows XP
• SAS license for large database steps and
  statistical analysis
• Oracle license for storage of output tables
• Supports three researchers - not necessarily
  skilled programmers
• Access model is deep but narrow

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Results Performance

• Interaction between vertical and horizontal data
  structures, four examples revisited
• Snapshot of drugs by Psycho-active (PA) category
  2 minutes
• 3 years of PA Rx payments 3 minutes
• PA prescribing in schizophrenia population
  identified from physician records 12 minutes
• Time relative Rx refills for populations with new
  schizophrenia 15 minutes