Holtrop 2017.

Study characteristics
Methods	Diabetic and obese patient clinical outcomes improve during a care management implementation in primary care Clustered RCT (10 clusters and NR providers), conducted in 1) 5 practices implemented care management and were compared with 5 comparison practices within the same practice organisation. The participating practices were part of a physician‐owned medical group in southeast Michigan. Ten total primary care practices were categorised according to size (large being 5 or more providers or small being 4 or less providers), discipline (family or internal medicine) and rural or suburban location and placed in pairs. One practice from each pair was randomly selected using a random number generator for intervention. 2) Addition of new care managers and using new care management software to help patients co‐ordinate their care and self‐manage their conditions. In United States of America. 2 arms: 1. Control (comparison practices) (control arm) and 2. Intervention (practices implemented care management) (intervention arm)
Participants	Control arm N: 443 Intervention arm N: 444, NA, NA Diabetes type: 2 Mean age: 57.9 ± 17.35 % Male: 53.8 Longest follow‐up: 12 months
Interventions	Control arm: (comparison practices) Intervention arm: (practices implemented care management) 1) Case management 2) Team change 3) Electronic patient registry 4) Clinician reminder 5) Promotion of self‐management
Outcomes	Glycated haemoglobin Systolic blood pressure Low‐density lipoprotein
Funding source	The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The project described was supported by Award Number 1 R18 DK082377‐01A2 from the National Institute of Diabetes and Digestive and Kidney Diseases. Additional support was provided by Grant No. P30DK092926 from the National Institute of Diabetes and Digestive, and Kidney Disease. John Piette is a US Department of Veterans Affairs Research Career Scientist.
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Risk of bias
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Unclear risk	Low risk for clinic randomisation and high risk for patient selection. Ten total primary care practices were categorised according to size (large being 5 or more providers or small being 4 or less providers), discipline (family or internal medicine) and rural or suburban location and placed in pairs. One practice from each pair was randomly selected using a random number generator for intervention. Intervention patients were randomly selected but each control patients were matched to an intervention patients based on these criteria: same baseline risk score (defined on an 8‐point scale from 0 = no risk factors to 7 = diabetes and LDL > 100 mg/dL and SBP > 140 mm Hg), disease status (diabetes vs obesity without diabetes), and whose first available clinic datum was within ± 3 months of the enrollment time of the intervention patient.
Allocation concealment (selection bias)	Low risk	Pair‐matched cluster‐randomised controlled trial.
Provider's baseline characteristics (selection bias)	Unclear risk	No data reported. The practice participants included 10 primary care practices within one physician‐owned practice organisation in southeast Michigan. Intervention practices were pair‐wise matched with similar comparison practices on practice discipline, size and geographic location. There were 2 internal medicine (1 large urban and 1 small urban) and 3 family medicine (1 large urban, 1 large rural, and 1 small rural) in each pair.
Patient's baseline characteristics (selection bias)	Low risk	Table 1, left panel (patients with diabetes). Gender and age have P value higher than 0.05.
Patient's baseline outcomes (selection bias)	High risk	Table 1, left panel (patients with diabetes). BMI, oral diabetic medication (%), insulin (%) and statin (%), weight (pounds) have significant P values. Quote: "Patients in the intervention group at baseline were overall significantly different on several characteristics as compared with matched comparison patients."
Incomplete outcome data (attrition bias)	High risk	The number lost is not reported. Quote: "We noted that variability in patient health care participation was a source of missing data. Clearly, all patients did not visit the clinics every quarter and missing data may be more common among patients with poorer health status."
Blinding of participants and personnel (performance bias) and of outcome assessors (detection bias)	Low risk	All of our outcomes of interest were objectively measured (HbA1c, SBP and LDL).
Selective reporting (reporting bias)	High risk	No registered protocol. They only reported these data at baseline: we also extracted data on prescriptions for metformin, long‐acting insulin, short‐acting insulin, glitazones, DPP‐4 agents, sulfonylureas, beta‐blockers, angiotensin‐converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARB‐II), calcium channel blockers, centrally acting antihypertensives (e.g. clonidine), statins, orlistat and appetite suppressants. Only report SBP, not DBP.
Risk of contamination (other bias)	Low risk	Pair‐matched cluster‐randomised controlled trial. Clustered by practice and no mention of contamination events.
Other bias	Low risk	None.