Health Dialogue Intervention Versus Opportunistic Screening for Type 2 Diabetes and Cardiovascular Disease Prevention

DETECT (health Dialogue intErvention versus opporTunistic scrEening in primary Care for Type 2 diabetes and cardiovascular disease prevention) targets the challenges of primary prevention for individuals with low socioeconomic status by implementing and evaluating two preventive interventions, a Health Dialogue Intervention (HDI) and an Opportunistic Screening (OS), conducted in primary care, specifically targeting settings with low socioeconomic status. The interventions will focus on detecting risk factors for CVD and supporting changes in unhealthy lifestyle behaviors.

The study is designed as a parallel cluster-randomized trial with two conditions, with primary care centers (PCCs) serving as the unit of randomization and individual patients as units of observation for primary and secondary outcomes. Participants randomized to the HDI intervention will be invited to partake in a systematic screening of cardiovascular and metabolic risk factors using questionnaires, blood sampling, and clinical examinations, all of which together form the risk profile. Next, they will be invited to an individually oriented health dialogue which is prescribed by a care provider. The dialogue focuses on promoting healthy lifestyle behaviors and is based on the screening results and the given risk profile. The effects of HDI will be compared to that from OS, wherein participants will be recruited upon scheduling of an appointment at their PCC for any reason, except for individuals with pre-scheduled appointments related to hypertension, T2D, and CVD. Participants receiving OS will be screened for hypertension, overweight/obesity, tobacco usage, blood-lipid profile, and blood glucose.

Short-term outcomes will be assessed at baseline, and 6 and 12-months after receiving the intervention, and long-term outcomes (i.e. 5 and 10 years post intervention) using nationwide registers.

The goal is to recruit a total of 30 PCCs (n=15 in each arm) in the county of Stockholm. Based on real observed variance in levels of systolic blood pressure in the county of Stockholm and accounting for clustering effects, the investigators calculate that a minimum of 840 participants (n=420 in each arm and n=28 per cluster) would yield 80% power to detect a reduction of 5 mmHg systolic blood pressure in the HDI group. To allow for the expected difficulties with recruitment and subsequent attrition, the investigators therefore aim to recruit n=100 patients per cluster, yielding a total study population of 3000.

The core tool of analysis will be a hierarchical model consisting of two levels, individuals and PCCs. The primary model for analyzing changes in systolic blood pressure (primary outcome), other risk factors, lifestyle behaviors, and quality-of-life over 6 and 12 months, will be a mixed-effects Generalized Linear Model to capture variability between and within PCCs. In these models, the treatment effect (HDI versus OS) will be assessed using a fixed effect represented by a dummy variable at PCC level, specifying the type of treatment (HDI/OS). Together, this enables the estimation of the average treatment effect, while accounting for the hierarchical structure of the data. We will estimate unadjusted effects and effects adjusted both/either at the individual level and PCC level, e.g., adjusted for demographic characteristics, socioeconomic status, CNI level, and baseline values of the referred outcome. The analysis will be conducted on an intention-to-treat basis. Potential effect modification will be explored through subgroup analyses and interaction analyses considering sex, birth country, and socioeconomic characteristics. A sensitivity analysis will be conducted to assess whether the individual randomization of a single PCC introduced bias. Thus, we will perform analyzes both including and excluding this PCC.

For the assessment of T2D and CVD incidence and mortality during the extended 5 and 10 years of follow-up, we will calculate hazard ratios using a mixed-effect Cox regression. We will also calculate the number needed to treat (NNT) as 1/absolute risk reduction.

Finally, we also aim to estimate the time needed to treat (TNT) for the interventions. The TNT is a new method designed to consider clinician's time as a finite resource, with the aim of facilitating for guideline committees who develop clinical practice guidelines. The TNT can be expressed in three different ways: 1) the clinician time needed to improve the outcome for one person (TNTNNT), 2) the clinician time needed to provide the intervention for all eligible in a population (absolute TNT), 3) the proportion of the total clinician time available for patient care needed to implement the intervention for everyone eligible (relative TNT). More detailed information on the TNT method and its assumptions is available elsewhere.