A randomized controlled trial of a community health worker intervention in a population of patients with multiple chronic diseases: Study design and protocol

Abstract

Upstream interventions – e.g. housing programs and community health worker interventions-address socioeconomic and behavioral factors that influence health outcomes across diseases. Studying these types of interventions in clinical trials raises a methodological challenge: how should researchers measure the effect of an upstream intervention in a sample of patients with different diseases? This paper addresses this question using an illustrative protocol of a randomized controlled trial of collaborative-goal setting versus goal-setting plus community health worker support among patients multiple chronic diseases: diabetes, obesity, hypertension and tobacco dependence. At study enrollment, patients met with their primary care providers to select one of their chronic diseases to focus on during the study, and to collaboratively set a goal for that disease. Patients randomly assigned to a community health worker also received six months of support to address socioeconomic and behavioral barriers to chronic disease control. The primary hypothesis was that there would be differences in patients’ selected chronic disease control as measured by HbA1c, body mass index, systolic blood pressure and cigarettes per day, between the goal-setting alone and community health worker support arms. To test this hypothesis, we will conduct a stratum specific multivariate analysis of variance which allows all patients (regardless of their selected chronic disease) to be included in a single model for the primary outcome. Population health researchers can use this approach to measure clinical outcomes across diseases.

Rideshare-Based Medical Transportation for Medicaid Patients and Primary Care Show Rates: A Difference-in-Difference Analysis of a Pilot Program

“…Though the social determinants of health are widely recognized as a major contributor to premature deaths in the United States, 18 there are few evidence-based strategies to guide health systems toward addressing these determinants. [19][20][21] In this study, we describe the promising results of a pilot program to address one such factor-transportation barriers. Though our findings are significant, larger transportation trials are needed to further assess whether rideshare-based transportation service models to improve health care access are worth testing before moving policy and payment in this direction.…”

Effect of Community Health Worker Support on Clinical Outcomes of Low-Income Patients Across Primary Care Facilities

“…The multivariate analysis of variance is an extension of analysis of variance, which allowed all patients (regardless of their selected chronic disease focus) to be assessed in a single model. 19,33 Hypothesis tests were conducted using joint multivariate Wald test statistics. Permutation-based 2-sided P values were used for these tests to preserve the correct type I error without making any distributional assumptions; P = .05 indicated statistical significance.…”

Estimating scaled treatment effects with multiple outcomes

“…In Kangovi et al, 2 a follow-up study to Kangovi et al 1 patients were asked (prior to randomization) which of K outcomes they would like to focus on for improvement, so that V ∈ 2{1, …, K } indicates this choice. Investigators were interested in the overall average effect of treatment A on patients’ selected outcomes, which is a special case of the weighted summary in equation (15).…”

“…Each outcome was measured in terms of change between study enrollment and 6-month follow-up, so negative values indicate improvement. Kangovi et al 1,2,29 give full details of the study population and design; we give summary statistics and histograms of the outcome distributions in the online Supplementary Materials.…”

“…Such designs are particularly important in patient-centered research, for example, where different outcomes might be more or less important to different patients, 1,2 and more generally in prioritizing public health recommendations. 3 …”