Defining the key variables of interest

To identify patients with and without polyps, we summarized polyps by patientid in polyp dataset. Below are list of our study variables:

• Patient age • Patient race • Patient gender • Sedation type • Protocol type • Polyp • Adenoma 26,523 procedures as of February 4, 20,912 initial

20,570 patients with initial colonoscopy 30-89 years

30 excludes dues to prior history of colon/rectum resection 20,540 study eligible 15,203 patients with 2-person 4,773 patients with 2- person technique 604 patients with 1- person technique

342 <30 years & ≥90 years 5,611 2nd and later procedures

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• Number of polyp found per subject

• Number of adenoma found per subject

• Procedure time

• Bowel preparation

Quality indicators

The procedure time, named Timeproc is calculated from the original downloaded fields from SCMEC – starttime and endtime. Time during the day was extracted as

starttime and endtime usng the raw variables ScopeIn and Scopeout. The interval in

seconds between ScopeIn and ScopeOut was taken and divided by 60 to calculate

TimeProc in minutes.

For polypsize, variable polypsizemm is used to categorize polyps into three groups, “≤5mm”, “6-9mm”, and “10+mm”. For polyp location, PolypLocation was the original variable used to create the intermediate variables. Polyp location was defined as proximal if located in the cecum, ascending colon, hepatic flexure, and transverse colon, and as distal if located in the splenic flexure, descending colon, sigmoid colon, or rectum. For those located in the proximal, we coded as “right colon”, for the remaining, “left colon” .

Adenoma and polyp detection rate

Adenoma detection is our key dependent variable to define the quality. To detect adenomas (which are identified by pathologic examination of polyps), endoscopists must first find the polyps during the colonoscopy, classify the polyp by gross appearance, and take part of the lesion to lab for biopsy to confirm the histology of the polyp. Therefore,

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we first study the polyp detection and then proceed to adenoma detection as our key variables of interest.

Polyp detection rate is defined as the percentage of patients with at least one polyp found. Each polyp has a polypid and a procedureid to link to the patient it belongs to. To identify the likelihood of a patient having a polyp, we summarized the polyps in each patient into a polyp dataset. If a patient ID exists in the polyp summary dataset, the patient was coded as “Yes” for the polyps variable in the procedure dataset, and if not, it was coded“No”. To calculate the polyp detection rate, patients with polyps equal to “Yes” are divided by total patients. Adenoma detection rate is calculated by the same process.

For the number of polyps found per subject, polyps were summarized within each patient using patient ID in the polyp dataset and the variable with count of polyps was merged into procedure dataset as SumPolyps using the patientid. A similar process was used for the number of adenomas found per subject, named SumAdenomas.

Protocol type

The protocol type was classified at the level of provider using the providerID from SCMEC. Per SCMEC, providerID equal to 56 and 64 were classified as 1-person technique specialists, whereas providerID equal to 1, 22 and 59 were classified as 2- person technique specialists, and the remaining physicians were PCPs all using 2-person technique. Procedures by these respective physicians were thus assigned to the protocol category stated above.

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The sedation type is categorized by procedure date. Every procedure conducted before April 1, 2006 was categorized as Midazolam-meperidine sedated procedure, while April 1, 2006 and after were propofol-sedated procedure.

Control variables classified

Patient age was calculated from patient date of birth downloaded from the

SCMEC’s administrative billing system. The continuous variable of age was recoded into four age groups: <50 years, 50-59 years, 60-69 years, and 70-89 years. Patient gender was downloaded from the SCMEC’s administrative system coded as male and female. Patient race is coded as Whites, Blacks, Other and unknown (missing information).

The bowel preparation status, bowelprep, is based on a field directly downloaded from SCMEC data system called ColPrep. If ColPrep was equal to missing, it remains missing in bowelprep. If ColPrep equal to “excellent”, it remains the same in bowelprep. If ColPrep indicated “fair” or “good”, it was re-coded “fair” in bowelprep. If ColPrep equal to “poor”, it remains the same in bowelprep.

The variable to classify training procedure or not, named training is based on a field directly downloaded from SCMEC administrative system called ColPCPSeq. All primary care physicians had their cumulative procedures assigned for each procedure because their very first training procedures started at the study center. Specialists do not qualify for procedure volume variable and have a missing value in this field because all of the specialists completed their first 140 training procedures before getting credentialed in colonoscopy during their training. Therefore, if ColPCPSeq equals to “missing” or more than 140, training of this physician will be coded as “No (0)”. If ColPCPSeq less than or equal to 140, training value is “Yes (1)”.

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Interaction terms to be tested

We further studied the interaction between bowel preparation status (excellent, fair and poor) and sedation type. To test the effect of interaction six categories were created based on bowelprep and sedation variables, which are Midazolam-

meperidine/Poor, Midazolam-meperidine/Fair, Midazolam-meperidine /Excellent, Propofol/Poor, Propofol/Fair, Propofol/Excellent.

The cecal intubation rate, named cecalintub, is coded based on original fields downloaded from SCMEC – termileumintubated and advnacedupto. If

termileumintubated equal to “Yes” or advancedupto equal to “the cecum”, then

cecalintub equal to “Yes.” To calculate the cecal intubation rate, patients with cecalintub

equal to “Yes” are divided by total patients.

3.7 Data analysis