Prestes 2017.

Study characteristics
Methods	Improving diabetes care at primary care level with a multistrategic approach: results of the DIAPREM programme Clustered RCT (30 clusters and 60 (30 physicians and 30 nurses) providers), conducted in 1) Primary care units (PCU) of La Matanza Health Secretariat, province of Buenos Aires, Argentina. 2) Physicians and nurses were randomly selected to be trained in Argentina 2 arms: 1. Control (traditional care) (control arm) and 2. Intervention (DIAPREM: DIAbetes Primary care, Registry, Education and Management) (intervention arm)
Participants	Control arm N: 157 Intervention arm N: 154, NA, NA Diabetes type: 2 Mean age: 55.2 ± 6.93 % Male: 33.73 Longest follow‐up: 12 months
Interventions	Control arm: (traditional care) 1) Team change 2) Electronic patient registry Intervention arm: (DIAPREM: DIAbetes Primary care, Registry, Education and Management) 1) Team change 2) Electronic patient registry 3) Clinician education 4) Clinician reminder 5) Patient reminders
Outcomes	Lipid‐lowering drugs Antihypertensive drug Retinopathy screening Glycated haemoglobin Systolic blood pressure Diastolic blood pressure Low‐density lipoprotein Hypertension control
Funding source	DIAPREM implementation was partially supported by a grant provided by the World Diabetes Foundation (WDF12‐761)
Notes	—
Risk of bias
Bias	Authors' judgement	Support for judgement
Random sequence generation (selection bias)	Unclear risk	Method not reported. Of the 40 primary care units (PCU) of La Matanza Health Secretariat, they randomly selected 30 physicians and 30 nurses. Of those, 15 were randomly selected to be trained (IG group), and another group of 15 physicians and nurses from another 15 PCUs were also randomly selected and used as controls (CG group).
Allocation concealment (selection bias)	Unclear risk	Clustered RCT but the authors never use the term cluster.
Provider's baseline characteristics (selection bias)	Unclear risk	No provider and clinic characteristics reported. No evidence of block‐randomisation, pair‐matching or stratification to try to homogenise groups.
Patient's baseline characteristics (selection bias)	Unclear risk	Table 1. No P values between groups at baseline. Data only for the completers.
Patient's baseline outcomes (selection bias)	Unclear risk	Table 1. No P values between groups at baseline. Data only for the completers.
Incomplete outcome data (attrition bias)	High risk	During the 1‐year follow‐up, patients who dropped out were significantly fewer in the intervention than in the control group (28% and 48%, respectively; P < 0.0003). No significant differences were found between clinical and metabolic characteristics of adherent compared to dropout patients in any of the groups (data not shown).
Blinding of participants and personnel (performance bias) and of outcome assessors (detection bias)	Low risk	Primary outcome was objectively measured (HbA1c) as well as BP and LDL. It is assumed that drug prescription were extracted from QUALIDIAB registry (objective).
Selective reporting (reporting bias)	Unclear risk	No registered or published protocol. They reported many more outcomes than the main outcomes stated in the methods section.
Risk of contamination (other bias)	Low risk	Clustered RCT. Each physician‐nurse team working in different clinics; unlikely that they have communicated. Quote: "Of the 40 primary care units (PCU) of La Matanza Health Secretariat, we randomly selected 30 physicians and 30 nurses. Of those, 15 were randomly selected to be trained (IG group), and another group of 15 physicians and nurses from another 15 PCUs were also randomly selected and used as controls (CG group)."
Other bias	Low risk	No evidence of other risk of bias.