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Building Infrastructure for Comparative Effectiveness Protocols (BICEP), 2002-2012 [Connecticut]

Version
v1
Resource Type
Dataset : administrative records data, clinical data, event/transaction data, text, aggregate data, census/enumeration data
Creator
  • Lynch, John (Connecticut Center for Primary Care)
Publication Date
2013-11-07
Funding Reference
  • United States Department of Health and Human Services. Agency for Healthcare Research and Quality
    • Award Number: R24HS019474
Free Keywords
Schema: ICPSR
diabetes; health; health problems; health status; illness; medical care; medicine; primary care; treatment
Description
  • Abstract

    CCPC's long term vision is to use pragmatic comparative effectiveness methods, linked to an extensive primary care practice data repository, to establish evidence about best practices for complex real world patients and deliver appropriate, real-time decision support at point of service for primary care practitioners (PCPs) in a way that will account for individualized management of conditions and choice of treatments in order to provide optimal care.

    The primary aim of BICEP was to advance analytical methods of observational Comparative Effectiveness Research (CER) to support evidentiary needs of primary care practitioners in answering important questions related to care of patient populations with Multiple Complex Conditions (MCCs).

    The secondary aim of BICEP was to conduct a pilot study to demonstrate the feasibility and value of using the analytic methods for conducting CER among complex patients.

    BICEP sought to answer the following clinical research questions: In adults with Type 2 Diabetes Mellitus (T2DM) coupled with additional chronic diseases,

    1. What is the comparative effectiveness of T2DM medications in achieving glycemic control?
    2. What is the comparative effectiveness of T2DM medications on intermediate outcomes, adverse events, side effects, tolerability?
    3. Does the effectiveness and safety of the diabetic treatment options differ across subgroups of patients based on patient demographic characteristics, complex co-morbidities, or the use of other concurrent therapies?
  • Abstract

    The primary aim of BICEP was to advance analytical methods of observational Comparative Effectiveness Research (CER) to support evidentiary needs of primary care practitioners in answering important questions related to care of patient populations with Multiple Complex Conditions (MCCs). The secondary aim of BICEP was to conduct a pilot study to demonstrate the feasibility and value of using the analytic methods for conducting CER among complex patients.
  • Methods

    This project used a pragmatic trial approach, assembled data from multiple electronic health record sources, derived essential variables, calculated co-morbidity, determined medical intervention clusters, adjusted for heterogeneity using propensity techniques, analyzed outcomes of dual therapy regimens, and documented statistical associations with an extensive list of co-variables.
  • Methods

    Presence of Common Scales: icd9, cpt4, GPI, HCC
  • Abstract

    Datasets: DS1: Building Infrastructure for Comparative Effectiveness Protocols (BICEP), 2002-2012 [Connecticut]
Temporal Coverage
  • 2002-01-01 / 2012-12-31
    Time Period: Tue Jan 01 00:00:00 EST 2002--Mon Dec 31 00:00:00 EST 2012
  • 2002-01-01 / 2012-12-31
    Collection Date(s): Tue Jan 01 00:00:00 EST 2002--Mon Dec 31 00:00:00 EST 2012
Geographic Coverage
  • United States
  • Connecticut
Sampled Universe
Adult primary care patients recieving dual therapy diabetes medications.
Sampling

The CER database was created in two passes. On the first pass, the eligible T2DM patient cohort was identified by triangulation of multiple variables from different sources. Patients not seen between 2009 and 2011 were excluded. Children under age 18 were excluded. The EHR problem list ICD-9 code of 250.xx was considered the most accurate single positive indication of diagnosis of T2DM. However, other "problem" labels such as metabolic syndrome, hyperglycemia, abnormal glucose, etc. could also be considered T2DM. Therefore, patients with a HbA1c result greater than 6.5 on two or more occasions were considered for inclusion. Patients with two or more T2DM indications from other information sources were also considered: diabetes medication in the medication list; any of 30 specific medications in text notes; abnormal glucose test greater than 200mg/dl; billing claim diagnosis of T2DM; diabetes in text notes or EHR form values (excluding pre-diabetes). However, if the patient had type-1 diabetes, gestational diabetes, or polycystic disease, they were excluded from the study, as were any other patients not exhibiting the T2DM indications described above. From the 21,761 remaining potential study candidates, 2,190 patients exhibiting less than 2 encounters post baseline and were also excluded.

In the second pass, the study identifiers of the 19,571 identified T2DM patients were used to identify, pull and upload all desired research variables from pertinent data tables into the de-identified CER database: Practice management system demographics, encounter claims, EHR Office measures, EHR Problems, EHR medications, and laboratory results.

In order to determine which patients were on a dual oral therapy regimen, as well as the start and end dates of such a regimen, examination of dual regimens was limited to four:

  1. MET and SU
  2. MET and TZD
  3. MET and DPP4
  4. a combined group of less frequent combinations (SU and DPP4 or SU and TZD)

These regimens could be prescribed as either a single formulation in one pill or two separate pills. Researchers then selected the first active prescription and the first subsequent discontinued record for any one of these GPI codes, and determined the start and end delta dates for each regimen. If a particular medication was never recorded as "discontinued," it was presumed active through study endpoint. Once a time-frame for each medication had been established, the study looked at the overlap among regimens and determined a "count" for each patient at any point in time. Any regimen including insulin, only mono therapy, and more than two non-insulin diabetic medications was excluded. The process resulted in a cohort of 4,040 T2DM patients on dual oral therapy regimen as well as the start and end dates for the regimen.

Availability
Download
One or more files in this study are not available for download due to special restrictions; consult the study documentation to learn more on how to obtain the data.
Relations
  • Is version of
    DOI: 10.3886/ICPSR34447

Update Metadata: 2020-11-18 | Issue Number: 7 | Registration Date: 2015-06-16

Lynch, John (2013): Building Infrastructure for Comparative Effectiveness Protocols (BICEP), 2002-2012 [Connecticut]. Version: v1. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/ICPSR34447.v1