Data Access, Analysis, and Communication

Data Access, Analysis, and Communication

Data access is dependent on picking the right database to meet the objectives of our clients and their projects.   By accessing private, federal and state data sources we can tailor our data source to meet a client's needs.

Data Sources:
  • We work with clients to identify the appropriate data sources to meet study needs
  • Not all data sources work for all research
  • We use a combination of different types of data (claims, EMR, lab, and government data)
  • We can work with YOUR data, help obtain data from a variety of sources, or provide turn-key analytical solutions.
  • We have a variety of clinical experts in many disciplines
  • We utilize unique methods to disseminate data to key customers and opinion leaders

What we do:

Taking Data to the Next Level

Data access is dependent on picking the right database to meet the objectives of our clients and their projects.   By accessing private, federal and state data sources we can tailor our data source to meet a client's needs.

Our team of affiliated researchers can access and analyze:

  • Commercially available and Proprietary Databases
  • Information on benefit design, co-pays, deductibles, out-of-pocket costs, demographic, job-related information, and absence time and costs.
  • Health Benefit Costs—Direct and indirect components
  • Direct Medical Costs and Services by Point/Place of Service.
  • Absenteeism
  • Presenteeism
  • The Severity of Illness
  • Persistence and Compliance (P&C), and
  • Comorbidities
  • US Based Commercial and Medicare Databases
  • VA Databases
  • Over the Counter (OTC)/Consumer Data--(with Medical and Prescription Components)
  • Electronic Medical/Health Records (EMRs/EHRs)—Plan and condition specific systems
  • Hospital Data
  • Government based data—Medicaid, Medicare, Veterans Administration (VA), Military, and dual-eligible populations, and
  • Other specialty sources
Statistical Analysis:
  • T tests and chi-square tests (generally) used for the demographic data.
  • Two-part regression modeling
  • Logistic regression to model the likelihood of having non-zero data for each data element during the year after the index date.
  • Generalized-linear regression to model each data element controlling for differences in demographics, job-related variables, and geography
  • Models for the different metrics only included employees with data for that metric (ie, SL, STD, LTD, WC, and PoS)
Recent Retrospective Database projects include
  • Comparing the costs and absences of products used to treat chronic conditions such as multiple sclerosis and hepatitis-c.
  • Identifying patients tolerant to therapies
  • The incremental impact of conditions such as GERD (gastroesophageal reflux disease), insomnia, and pain on employees
  • Examining the costs, prevalence, and services of standardized comorbidity categories associated with various conditions and the relationship of comorbidity categories to specific conditions.
  • Comparisons of Fixed Dose Combination products with Lose Dose Combination products on costs, absence, and patient persistence with therapies.
  • Comparative research on the adherence and compliance in Multiple Sclerosis and the impact of add-back therapy on patient compliance in persons with Endometriosis.
  • The impact of plan design and co-pays on patient persistence and adherence with therapies.
  • Using Medicaid data, annual timeframes, and pre-post designs.