Advancing digital health equity: Directions for behavioral and social science research.

The field of digital health is evolving rapidly and encompasses a wide range of complex and changing technologies used to support individual and population health. The COVID-19 pandemic has augmented digital health expansion and significantly changed how digital health technologies are used. To ensure that these technologies do not create or exacerbate existing health disparities, a multi-pronged and comprehensive research approach is needed. In this commentary, we outline five recommendations for behavioral and social science researchers that are critical to promoting digital health equity. These recommendations include: (i) centering equity in research teams and theoretical approaches, (ii) focusing on issues of digital health literacy and engagement, (iii) using methods that elevate perspectives and needs of underserved populations, (iv) ensuring ethical approaches for collecting and using digital health data, and (v) developing strategies for integrating digital health tools within and across systems and settings. Taken together, these recommendations can help advance the science of digital health equity and justice.

The field of digital health is quickly growing and changing. Digital health technologies have the potential to increase access to health-related information and healthcare and improve wellbeing, but it is important that those technologies don't widen existing health disparities or create new ones. Behavioral and social science researchers have a key role to play in centering equity in their research teams and theoretical approaches, focusing on key barriers to access, uptake, and usage, studying digital health in ways that elevate the voices and needs of historically underserved groups, being thoughtful about how digital health data are collected and used, and making sure that digital health tools are designed to be used in real-world settings.

Advancing Health Policy and Program Research in Diabetes: Findings from the Natural Experiments for Translation in Diabetes (NEXT-D) Network.

PURPOSE OF REVIEW: To advance our understanding of the impacts of policies and programs aimed at improving detection, engagement, prevention, and clinical diabetes management in the USA, we synthesized findings from a network of studies that used natural experiments to evaluate diabetes health policies and programs. FINDINGS: Studies from the Natural EXperiments for Translation in Diabetes (NEXT-D) network used rigorous longitudinal quasi-experimental study designs (e.g., interrupted time series) and analytical methods (e.g., difference-in-differences) to augment causal inference. Investigators partnered with health system stakeholders to evaluate whether glucose testing rates changed from before-to-after clinic interventions (e.g., integrating electronic screening decision prompts in New York City) or employer programs (e.g., targeted messaging and waiving copayments for at-risk employees). Other studies examined participation and behavior change in low- (e.g., wellness coaching) or high-intensity lifestyle modification programs (e.g., diabetes prevention program-like interventions) offered by payers or employers. Lastly, studies assessed how employer health insurance benefits impacted healthcare utilization, adherence, and outcomes among people with diabetes. NEXT-D demonstrated that low-intensity interventions to facilitate glucose testing and enhance engagement in lifestyle modification were associated with small improvements in weight but large improvements in screening and testing when supported by electronic health record-based decision-support. Regarding high-intensity diabetes prevention program-like lifestyle programs offered by payers or employers, enrollment was modest and led to weight loss and marginally lower short-term health expenditures. Health plans that incentivize patient behaviors were associated with increases in medication adherence. Meanwhile, shifting patients to high-deductible health plans was associated with no change in medication use and preventive screenings, but patients with diabetes delayed accessing healthcare for acute complications (e.g., cellulitis). Findings were more pronounced among lower-income patients, who experienced increased rates and acuity of emergency department visits for diabetes complications and other high-severity conditions. Findings from NEXT-D studies provide informative data that can guide programs and policies to facilitate detection, prevention, and treatment of diabetes in practice.

Spatial capture-mark-resight estimation of animal population density.

Sightings of previously marked animals can extend a capture-recapture dataset without the added cost of capturing new animals for marking. Combined marking and resighting methods are therefore an attractive option in animal population studies, and there exist various likelihood-based non-spatial models, and some spatial versions fitted by Markov chain Monte Carlo sampling. As implemented to date, the focus has been on modeling sightings only, which requires that the spatial distribution of pre-marked animals is known. We develop a suite of likelihood-based spatial mark-resight models that either include the marking phase ("capture-mark-resight" models) or require a known distribution of marked animals (narrow-sense "mark-resight"). The new models sacrifice some information in the covariance structure of the counts of unmarked animals; estimation is by maximizing a pseudolikelihood with a simulation-based adjustment for overdispersion in the sightings of unmarked animals. Simulations suggest that the resulting estimates of population density have low bias and adequate confidence interval coverage under typical sampling conditions. Further work is needed to specify the conditions under which ignoring covariance results in unacceptable loss of precision, or to modify the pseudolikelihood to include that information. The methods are applied to a study of ship rats Rattus rattus using live traps and video cameras in a New Zealand forest, and to previously published data.

Primary Care Behavioral Health (PCBH) Model Research: Current State of the Science and a Call to Action.

The Primary Care Behavioral Health (PCBH) model of service delivery is being used increasingly as an effective way to integrate behavioral health services into primary care. Despite its growing popularity, scientifically robust research on the model is lacking. In this article, we provide a qualitative review of published PCBH model research on patient and implementation outcomes. We review common barriers and potential solutions for improving the quantity and quality of PCBH model research, the vital data that need to be collected over the next 10 years, and how to collect those data.

Economic evaluation of an Internet-based weight management program.

OBJECTIVE: To determine whether a behavioral Internet treatment (BIT) program for weight management is a viable, cost-effective option compared with usual care (UC) in a diverse sample of overweight (average body mass index = 29 kg/m2), healthy adults (mean age = 34 years) serving in the US Air Force. STUDY DESIGN: Two-group parallel randomized controlled trial. METHODS: Participants were randomly assigned into 2 groups: UC (n = 215) and UC plus BIT (n = 227). Baseline and 6-month assessments were included in the analyses. Primary outcome measures (changes in body weight, percent body fat, and waist circumference) and secondary outcome measures (Weight Efficacy Lifestyle [WEL] questionnaire) were included in an incremental cost-effectiveness analysis (ICEA) model. Costs were computed using the perspective of an agency wanting to replicate the intervention. Sensitivity analyses were performed to measure the robustness of models. RESULTS: Overall cost for BIT intervention was $11,178.40, or $49.24 per BIT participant. Total staff-time cost was $14.03 per BIT participant. Intervention cost was $25.92 per kilogram of weight loss and $28.96 per centimeter of waist-circumference loss. The cost was $37.88 for each additional point gained on the WEL subscale, where increasing scores indicate increased confidence in managing social pressures to eat. CONCLUSIONS: The BIT program is a cost-effective choice for weight management. It may cost more initially, but it results in long-term cost savings. Such cost-effective, Internet-based behavioral interventions for weight management could provide a valuable tool for preventive care aimed at improving individual and societal health.