In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed.

Much of the existing risk factor literature focuses on identifying predictors of low levels of substance use versus higher-levels of substance use. In this article, we explore more nuanced patterns of alcohol, tobacco and other drug (ATOD) use during late adolescence. Our aims were to: (1) identify subgroups of youth with qualitatively different patterns of ATOD use and (2) explore whether membership among qualitatively distinct, high-risk classes could be predicted based on early adolescent risk factors. Data came from a selected subsample of the National Longitudinal Survey of Youth (n = 1689). Predictors were measured when youth were about 12 years old; ATOD use was assessed when youth were aged 17 years. Results showed that adolescent ATOD use is not a homogenous behavior. Four distinct classes of adolescent ATOD users were derived. Each class had a qualitatively distinct and discriminable pattern of ATOD use. Ecological predictors were shown to differentiate between latent classes, with peer factors playing a particularly important role in differentiating between high-risk and higher-risk users. Implications for prevention and limitations are discussed.

Accruing evidence reveals best practices for how to help individuals living with Sickle Cell Disease (SCD); yet, the implementation of these evidence-based practices in healthcare settings is lacking. The Sickle Cell Disease Implementation Consortium (SCDIC) is a national consortium that uses implementation science to identify and address barriers to care in SCD. The SCDIC seeks to understand how and why patients become unaffiliated from care and determine strategies to identify and connect patients to care. A challenge, however, is the lack of agreed-upon definition for what it means to be unaffiliated and what it means to be a “SCD expert provider”. In this study, we conducted a Delphi process to obtain expert consensus on what it means to be an “unaffiliated patient” with SCD and to define an “SCD specialist,” as no standard definition is available. Twenty-eight SCD experts participated in three rounds of questions. Consensus was defined as 80% or more of respondents agreeing. Experts reached consensus that an individual with SCD who is unaffiliated from care is “someone who has not been seen by a sickle cell specialist in at least a year.” A sickle cell specialist was defined as someone with knowledge and experience in SCD. Having “knowledge” means: being knowledgeable of the 2014 NIH Guidelines, “Evidence-Based Management of SCD”, trained in hydroxyurea management and transfusions, trained on screening for organ damage in SCD, trained in pain management and on SCD emergencies, and is aware of psychosocial and cognitive issues in SCD. Experiences that are expected of a SCD specialist include experience working with SCD patients, mentored by a SCD specialist, regular attendance at SCD conferences, and obtains continuing medical education on SCD every 2 years.” The results have strong implications for future research, practice, and policy related to SCD by helping to lay a foundation for an new area of research (e.g., to identify subpopulations of unaffiliation and targeted interventions) and policies that support reaffiliation and increase accessibility to quality care.

Background: Sickle Cell Disease (SCD) has been designated by WHO as a public health problem in sub-Saharan Africa, and the development of newborn screening (NBS) is crucial to the reduction of high SCD morbidity and mortality. Strategies from the field of implementation science can be useful for supporting the translation of NBS evidence from high income countries to the unique cultural context of sub-Saharan Africa. One such strategy is community engagement at all levels of the healthcare system, and a widely-used implementation science framework, “Getting to Outcomes^®” (GTO), which incorporates continuous multilevel evaluation by stakeholders about the quality of the implementation. Objectives: (1) to obtain critical information on potential barriers to NBS in the disparate ethnic groups and settings (rural and urban) in the healthcare system of Kaduna State in Nigeria; and, (2) to assist in the readiness assessment of Kaduna in the implementation of a sustainable NBS programme for SCD. Methods: Needs assessment was conducted with stakeholder focus groups for two days in Kaduna state, Nigeria, in November 2017. Results: The two-day focus group workshop had a total of 52 participants. Asking and answering the 10 GTO accountability questions provided a structured format to understand strengths and weaknesses in implementation. For example, we found a major communication gap between policy-makers and user groups. Conclusion: In a two-day community engagement workshop, stakeholders worked successfully together to address SCD issues, to engage with each other, to share knowledge, and to prepare to build NBS for SCD in the existing healthcare system.