Combining alcohol interventions with tobacco addictions treatment in primary care—the COMBAT study: a pragmatic cluster randomized trial

Primary health care clinics in Ontario, Canada, implementing an existing smoking cessation program—the STOP program—at the time of the study are eligible to participate. The vast majority of three types of primary health care clinics across Ontario implement the STOP program: 150 out of 184 family health teams (FHTs), 59 out of 67 community health centres (CHCs), and 17 out of 23 nurse practitioner-led clinics (NPLCs). Clinics enroll their patients into the STOP program using a centralized, online portal; the STOP portal is an online data management and collection tool used by all clinics to complete participant enrollment, record smoking status at each visit, monitor treatment, and receive real-time progress reports. This tool allows for processes to be streamlined and for health care practitioners to track their patients’ progress throughout the program. All those FHTs, CHCs and NPLCs operational in the STOP program as of March 14, 2016 (222 clinics), are eligible for randomization, except one known to use paper-based patient enrollment options exclusively (i.e. any clinic not using the online portal). In total, 221 clinics will be randomized into the COMBAT trial.

To build capacity of all practitioners implementing the STOP program, an interactive webinar will be presented on SBIRT implementation using CDSS, as web-based SBIRT training has shown to be effective for and favourable among primary care practitioners [34]. The webinar audience will include the STOP Community of Practice (n = 300) of implementers, physicians, and executive directors, who interact through bi-weekly teleconferences to communicate updates, clarify procedures, address barriers or gaps in program delivery, and share experiences with other practitioners.

The COMBAT study will be a cluster randomized controlled trial with clusters (i.e. primary care clinics in Ontario, Canada) randomized to one of two treatment arms. Health care practitioners working in clinics randomized to the intervention arm, group B, will receive computer alerts when a patient is identified as consuming alcohol above the recommended guidelines and will be guided to provide the patient with a brief intervention and an educational workbook. Practices randomized to the treatment-as-usual arm, group A, will have access to the same resources available to practitioners in group B but will not receive computer alerts.

The web-based CDSS will take advantage of a platform that overcomes the common barriers to system-wide implementation, such as expensive time-consuming programming changes and validation for each electronic medical record (EMR) system, and will have features that are associated with improvement, such as clinical workflow integration, decision support at time of care, and electronic and direct recommendations [35].

Clinics will be the unit of randomization. Two stratification factors will be defined: clinic type with three levels (FHT, CHC, and NPLC) and clinic size with two levels, resulting in six strata. Actual clinic size, measured as number of trial participants enrolled, is not directly observable a priori; therefore, expected clinic size will be estimated based on past STOP enrollment. Independently for each clinic type, the two levels of clinic size will be set such that expected total enrollment in the two levels will be balanced. Within each stratum, clinics will be randomly allocated in a 1:1 allocation ratio to control (group A) or intervention (group B). Treatment allocation (randomization) for each clinic will be determined for all operational clinics en masse. Any clinics that begin implementing the STOP program after the randomization cut-off date (April 11, 2016) will not be eligible for participation in the COMBAT trial. The random assignment of treatment to clinic will be computer generated using the ralloc command of statistical computer software Stata V.13 [36].

This pragmatic, cluster trial is designed to evaluate an intervention to change practitioners’ behaviour. Blinding of the clinic via its practitioners will therefore not be possible as the practitioner will be aware of the presence or absence of the portal prompting system.

Participating clinics will not be told of their treatment allocation until the trial begins. Investigators are blinded to the randomization arm. Data analysis will be blinded to treatment allocation.

Participants in the COMBAT trial will be enrollees of the existing STOP smoking cessation program and will therefore be cigarette smokers wishing to quit, or those who have already begun their quit attempt.

Upon enrollment in the STOP program, participants will complete a baseline enrollment survey about their tobacco use and related measures. Alcohol consumption will be measured on the baseline survey using a seven-day timeline followback (TLFB) questionnaire [37] and the Alcohol Use Disorders Identification Test (AUDIT) [38]. Women consuming seven or more and men consuming fourteen or more alcoholic beverages (13.6 g per standard drink) in the past week—as measured by the TLFB—as well as any individual endorsing any episode of consuming five or more drinks on one occasion (AUDIT item 3) will be considered to be exceeding the Canadian Cancer Society (CCS)’s cancer prevention alcohol consumption guidelines [39]. In order for a participant to be eligible for the COMBAT trial, their baseline enrollment survey must be administered by a practitioner, in English, into the online portal in real time, so that the clinical interaction can be supported by the CDSS. Therefore, those patients who are administered the baseline survey on paper, or in French, cannot be included in the trial.

Patient consent for participation in the STOP smoking cessation program will be obtained at the time of enrollment. Individuals younger than 18 years old will enroll with the consent of their legal guardian if required to do so.

In clinics assigned to group B (intervention), practitioners will receive an automated decision aid tool (CDSS) embedded in the STOP program’s online portal. The tool will provide decision support as part of the STOP program’s clinical workflow in three steps. In the first step, the tool will automatically score screening tool responses and identify to practitioners, in the form of a pop-up message, those patients who reported consuming alcohol in excess of CCS cancer prevention guidelines. In the second step, the tool will prompt practitioners to provide a brief alcohol reduction or abstinence intervention; the tool will provide guidance on how to conduct a brief intervention, or allow practitioners to conduct an intervention of their own, or to decline to conduct an intervention at that time. In the third step, practitioners will be asked to provide the patient with the appropriate educational, alcohol resource.

The trial will assess the effect of an online portal-based computer decision support system on health care practitioners’ provision of educational resources addressing alcohol consumption (i.e. “Referral to Treatment” portion of SBIRT) to patients who are exceeding cancer prevention alcohol drinking guidelines and engaging in an attempt to quit smoking. We designed two educational resources, one encouraging alcohol reduction, and one encouraging alcohol abstinence. Practitioners will be encouraged to deliver the alcohol reduction resource to patients exceeding CCS cancer prevention alcohol consumption guidelines. The alcohol reduction resource was adapted from four evidence-based resources including the National Institute on Alcohol Abuse and Alcoholism’s “Rethinking your Drinking” [40]; BC Partners for Mental Health and Addictions’ “Problem Substance Use Workbook” [41]; Capital Health Nova Scotia’s “My Choice: A Workbook For Making Changes” [42]; and College of Family Physicians of Canada and Canadian Centre on Substance Abuse’s “Drinking Smart: Your Health and Alcohol Consumption” [43]. In order to ensure the resources reflect the needs and interests of the population of interest, the adaptations were shaped by a community-informed engagement event with clients enrolled in the STOP program who reported drinking alcohol above the Canadian Cancer Society’s low-risk drinking guidelines.

However, participants who exceed cancer prevention alcohol consumption guidelines may be alcohol dependent, in which case, advice to abstain from alcohol consumption is warranted [44]. For participants scoring above a brief screen (AUDIT-C) cut-off (female >2 points; male >3 points), the online portal will launch a mandatory, full AUDIT (AUDIT-10) screen for alcohol dependence risk [38]. Practitioners will be trained to advise participants scoring above the AUDIT-10 cut-off (20 points) to abstain from alcohol consumption and to provide the alcohol abstinence resource including contact information for a provincial referral system (http://​www.​connexontario.​ca/​) for further treatment at local addiction programs.

In clinics assigned to group A (control), practitioners will administer the same alcohol screening tools and have access to the same educational resources as group B. However in order to identify patients who report consuming alcohol in excess of CCS cancer prevention guidelines, they will have to score patients’ screening tool responses (as they will not receive prompts from the portal) and decide which educational resource is the most appropriate for their patient.

Refer to Fig. 1 for a visual depiction of the study work flow.

All patients will be contacted to complete a six-month follow-up survey, where they will be asked about their current drinking and smoking behaviours.

The primary outcome of the study, measured at the cluster level, will be the offer of an appropriate educational alcohol resource, reduction or abstention, to patients exceeding the cancer prevention alcohol consumption guidelines upon completion of STOP smoking cessation program enrollment. This dichotomous outcome is recorded by the STOP program’s online portal for each enrolling participant.

Given that patients can refuse practitioners’ offer of resources, the secondary outcome of the study, measured at the cluster level, will be the acceptance of the educational alcohol resource by patients exceeding the cancer prevention alcohol consumption guidelines. This dichotomous outcome is also recorded by the STOP program’s online portal for each enrolling participant.

The tertiary outcome, measured at the patient level, will be patient abstinence from smoking, and alcohol consumption within cancer prevention guidelines, at the 6-month follow-up survey as recorded in the STOP program’s online portal. Smoking abstinence will be defined by a negative response to the seven-day point prevalence question, “Have you had a cigarette, even a puff, in the last 7 days?” Alcohol consumption at follow-up will be measured using the same survey items as those used in the baseline enrollment survey, described previously. Alcohol consumption within CCS guidelines will be defined as the inverse of alcohol consumption in excess of CCS guidelines.

Based on measures of alcohol consumption used in the STOP Program prior to the start of the COMBAT trial, and accounting for intra-cluster correlation within each clinic as well as unequal enrollment by clinic, we assumed an enrollment of 23 participants per clinic over the 12-month course of this trial, in 189 clinics, for a total of 4347 subjects exceeding CCS guidelines, with enrollment size varying by a coefficient of 1.04. We set alpha = 0.05 and power = 80% and estimated an intra-cluster correlation coefficient of 0.04. Since there were no available estimates of the trial’s primary outcome measure prior to the initiation of the trial, the minimum detectable effect size was estimated for a range of values of P ₁, the proportion of control group enrollments in which practitioners delivered the brief intervention. The estimated detectable effect size ranged from a relative risk of 1.45 (if P ₁ = 0.1) to 1.04 if (P ₁ = 0.9).

An interim analysis in the form of a power calculation was conducted at the 8-month mark once estimates of P1 (defined above) were available. As described above, we used a method that accounts for intra-cluster correlation within each clinic as well as unequal enrollment by clinic, now incorporating the updated measures of alcohol consumption introduced with the start of the trial. The observed average cluster size was 12.71 and cluster sizes varied by a coefficient of 1.38. Setting alpha = 0.05, the analyst, unblinded to treatment allocation, obtained an estimate of P ₁ from the data collected to that date. To detect a relative risk of 1.20, the required sample size was estimated to be 6158. Therefore, the recruitment period will be extended until such a time as that sample size is achieved.

Descriptive statistics will compare cluster-level and patient-level baseline characteristics between the two treatment groups.

Results will be analyzed using a generalized estimation approach for fitting logistic regression using a population-averaged method with treatment arm and stratification variables (i.e. organization type and cluster size) regarded as fixed effects and clinic (i.e. cluster) as random effects in the model. To account for clustering, an exchangeable correlation matrix and robust standard errors will be specified [45]. The intra-cluster correlation coefficient will be estimated, and if it is found to be negative, an analysis not adjusting for clustering will be used.

As the primary and secondary outcomes will be captured by the online portal, complete case analyses will be used (i.e. imputation will not be used for these outcomes). The tertiary outcome will be available only for those patients who complete a 6-month follow-up survey; therefore, missing data is expected. A single imputation of best-case scenario (i.e. not smoking and drinking within guidelines) and a single imputation of worst-case scenario (i.e. smoking and drinking in excess of guidelines) will be conducted. If the best-case and worst-case scenarios imply different conclusions, a multiple imputation approach that accounts for the clustered structure of the data will be employed.

All analyses will follow an intention-to-treat approach in which clusters and participants are analyzed in the intervention arm to which they were originally assigned.

Qualitative interviews are taking place with health care practitioners to examine the facilitators and barriers of implementing SBIRT within their practice, as well as the diffusion of knowledge to others within their practice. We will analyze the interviews using a hybrid approach of inductive and deductive coding [46].

The study was reviewed by the Research Ethics Board at the Centre for Addiction and Mental Health (approval number: 035-2015). At the time of manuscript submission, the trial is ongoing with baseline data and primary and secondary outcome data collection in progress. The process evaluation data collection has not yet been undertaken.

Initially, this study was believed not to qualify as a clinical trial as per the World Health Organization definition because the primary outcome measures health care practitioner behaviour change, rather than patient health outcomes. However, at the time of manuscript submission, study investigators learned it was appropriate to register any trial involving human participants as a clinical trial. Since this study’s tertiary outcome is to measure patient-level behaviour change, this study was retrospectively registered with ClinicalTrials.gov (NCT03108144). To eliminate any risk of reporting or publication bias, all study investigators have remained blinded to preliminary data analyses, which are conducted by an analyst external to the investigator team to monitor study progress.