Interventional researchers face many design challenges when assessing intervention implementation in real-world settings. We focus here on commonly used QEDs (prepost designs with nonequivalent control groups, interrupted time series, and stepped-wedge designs) and discuss several variants that maximize internal and external validity at the design, execution and implementation, and analysis stages.

The World Health Organization recommends routine household tuberculosis contact investigation in high-burden countries but adoption has been limited. We sought to identify barriers to and facilitators of TB contact investigation during its introduction in Kampala, Uganda. We collected cross-sectional qualitative data through focus group discussions and interviews with stakeholders, addressing three core activities of contact investigation: arranging household screening visits through index TB patients, visiting households to screen contacts and refer them to clinics, and evaluating at-risk contacts coming to clinics. We analyzed the data using a validated theory of behavior change, the Capability, Opportunity, and Motivation determine Behavior (COM-B) model, and sought to identify targeted interventions using the related Behavior Change Wheel implementation framework. We led seven focus-group discussions with 61 health-care workers, two with 21 lay health workers (LHWs), and one with four household contacts of newly diagnosed TB patients. We, in addition, performed 32 interviews with household contacts from 14 households of newly diagnosed TB patients. Commonly noted barriers included stigma, limited knowledge about TB among contacts, insufficient time and space in clinics for counselling, mistrust of health-center staff among index patients and contacts, and high travel costs for LHWs and contacts. The most important facilitators identified were the personalized and enabling services provided by LHWs. We identified education, persuasion, enablement, modeling of health-positive behaviors, incentivization, and restructuring of the service environment as relevant intervention functions with potential to alleviate barriers to and enhance facilitators of TB contact investigation. The use of a behavioral theory and a validated implementation framework provided a comprehensive approach for systematically identifying barriers to and facilitators of TB contact investigation. The behavioral determinants identified here may be useful in tailoring interventions to improve implementation of contact investigation in Kampala and other similar urban settings.

One of the fastest growing risk groups for early onset of diabetes is women with a recent pregnancy complicated by gestational diabetes, and for this group, Latinas are the largest at-risk group in the USA. Although evidence-based interventions, such as the Diabetes Prevention Program (DPP), which focuses on low-cost changes in eating, physical activity and weight management can lower diabetes risk and delay onset, these programs have yet to be tailored to postpartum Latina women. This study aims to tailor a IT-enabled health communication program to promote DPP-concordant behavior change among postpartum Latina women with recent gestational diabetes. The COM-B model (incorporating Capability, Opportunity, and Motivational behavioral barriers and enablers) and the Behavior Change Wheel (BCW) framework, convey a theoretically based approach for intervention development. We combined a health literacy-tailored health IT tool for reaching ethnic minority patients with diabetes with a BCW-based approach to develop a health coaching intervention targeted to postpartum Latina women with recent gestational diabetes. Current evidence, four focus groups (n = 22 participants), and input from a Regional Consortium of health care providers, diabetes experts, and health literacy practitioners informed the intervention development. Thematic analysis of focus group data used the COM-B model to determine content. Relevant cultural, theoretical, and technological components that underpin the design and development of the intervention were selected using the BCW framework. STAR MAMA delivers DPP content in Spanish and English using health communication strategies to: (1) validate the emotions and experiences postpartum women struggle with; (2) encourage integration of prevention strategies into family life through mothers becoming intergenerational custodians of health; and (3) increase social and material supports through referral to social networks, health coaches, and community resources. Feasibility, acceptability, and health-related outcomes (weight loss, physical activity, consumption of healthy foods, breastfeeding, and glucose screening) will be evaluated at 9 months postpartum using a randomized controlled trial design. STAR MAMA provides a DPP-based intervention that integrates theory-based design steps. Through systematic use of behavioral theory to inform intervention development, STAR MAMA may represent a strategy to develop health IT intervention tools to meet the needs of diverse populations. ClinicalTrials.gov NCT02240420

Studies of the quality of tuberculosis (TB) diagnostic evaluation of patients in high burden countries have generally shown poor adherence to international or national guidelines. Health worker perspectives on barriers to improving TB diagnostic evaluation are critical for developing clinic-level interventions to improve guideline implementation. We conducted structured, in-depth interviews with staff at six district-level health centers in Uganda to elicit their perceptions regarding barriers to TB evaluation. Interviews were transcribed, coded with a standardized framework, and analyzed to identify emergent themes. We used thematic analysis to develop a logic model depicting health system and contextual barriers to recommended TB evaluation practices. To identify possible clinic-level interventions to improve TB evaluation, we categorized findings into predisposing, enabling, and reinforcing factors as described by the PRECEDE model, focusing on potentially modifiable behaviors at the clinic-level. We interviewed 22 health center staff between February 2010 and November 2011. Participants identified key health system barriers hindering TB evaluation, including: stock-outs of drugs/supplies, inadequate space and infrastructure, lack of training, high workload, low staff motivation, and poor coordination of health center services. Contextual barrier challenges to TB evaluation were also reported, including the time and costs borne by patients to seek and complete TB evaluation, poor health literacy, and stigma against patients with TB. These contextual barriers interacted with health system barriers to contribute to sub-standard TB evaluation. Examples of intervention strategies that could address these barriers and are related to PRECEDE model components include: assigned mentors/peer coaching for new staff (targets predisposing factor of low motivation and need for support to conduct job duties); facilitated workshops to implement same day microscopy (targets enabling factor of patient barriers to completing TB evaluation), and recognition/incentives for good TB screening practices (targets low motivation and self-efficacy). Our findings suggest that health system and contextual barriers work together to impede TB diagnosis at health centers and, if not addressed, could hinder TB case detection efforts. Qualitative research that improves understanding of the barriers facing TB providers is critical to developing targeted interventions to improve TB care.

Background: Although randomized controlled trials are often a gold standard for determining intervention effects, in the area of practice-based research (PBR), there are many situations in which individual randomization is not possible. Alternative approaches to evaluating interventions have received increased attention, particularly those that can retain elements of randomization such that they can be considered “controlled” trials. Methods: Methodological design elements and practical implementation considerations for two quasi-experimental design approaches that have considerable promise in PBR settings – the stepped-wedge design, and a variant of this design, a wait-list cross-over design, are presented along with a case study from a recent PBR intervention for patients with diabetes. Results: PBR-relevant design features include: creation of a cohort over time that collects control data but allows all participants (clusters or patients) to receive the intervention; staggered introduction of clusters; multiple data collection points; and one-way cross-over into the intervention arm. Practical considerations include: randomization versus stratification, training run in phases; and extended time period for overall study completion. Conclusion: Several design features of practice based research studies can be adapted to local circumstances yet retain elements to improve methodological rigor. Studies that utilize these methods, such as the stepped-wedge design and the wait-list cross-over design, can increase the evidence base for controlled studies conducted within the complex environment of PBR.

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.