AHRQ Multiple Chronic Conditions Research Network

This study is provided by the AHRQ Multiple Chronic Conditions Research Network.

Building Infrastructure for Comparative Effectiveness Protocols (BICEP), 2002-2012 [Connecticut] (ICPSR 34447) RSS

Principal Investigator(s):

Summary:

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?

Access Notes

  • One or more files in this study are not available for download due to special restrictions ; consult the restrictions note to learn more. You can apply online for access to the data. A login is required to apply for access.

    Access to the BICEP data is restricted. Users interested in obtaining these data must complete a Restricted Data Use Agreement and comply with specified data security requirements. Apply for access to these data through the ICPSR data access request system portal.

Dataset(s)

Dataset
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Study Description

Citation

Lynch, John. Building Infrastructure for Comparative Effectiveness Protocols (BICEP), 2002-2012 [Connecticut]. ICPSR34447-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2013-11-11. http://doi.org/10.3886/ICPSR34447.v1

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Funding

This study was funded by:

  • United States Department of Health and Human Services. Agency for Healthcare Research and Quality (R24HS019474)

Scope of Study

Subject Terms:   diabetes, health, health problems, health status, illness, medical care, medicine, primary care, treatment

Geographic Coverage:   Connecticut, United States

Time Period:  

  • 2002--2012

Date of Collection:  

  • 2002--2012

Universe:   Adult primary care patients recieving dual therapy diabetes medications.

Data Types:   administrative records data, aggregate data, census/enumeration data, clinical data, event/transaction data, machine-readable text

Methodology

Study Purpose:   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.

Study Design:   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.

Sample:  

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.

Data Source:

practice management system (demographics, scheduling, charges, diagnoses, procedures); EHR (allergies, immunizations, medications, office measures, orders, notes, plans, problems, results, tasks, encounters, structured forms, scanned documents); laboratory system; provider credentialing system; United States Census data; and disease registries.

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

Extent of Processing:  ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection:

  • Performed consistency checks.
  • Created variable labels and/or value labels.
  • Performed recodes and/or calculated derived variables.
  • Checked for undocumented or out-of-range codes.

Version(s)

Original ICPSR Release:  

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