Obesity Prevention in Early Life (OPEL) study: linking longitudinal data to capture obesity risk in the first 1000 days.

To develop robust prediction models for infant obesity risk, we need data spanning multiple levels of influence, including child clinical health outcomes (eg, height and weight), information about maternal pregnancy history, detailed sociodemographic information of parents and community-level factors. Few data sources contain all of this information. This manuscript describes the creation of the Obesity Prevention in Early Life (OPEL) database, a longitudinal, population-based database that links clinical data with birth certificates and geocoded area-level indicators for 19 437 children born in Marion County, Indiana between 2004 and 2019. This brief describes the methodology of linking administrative data, the establishment of the OPEL database, and the clinical and public health implications facilitated by these data. The OPEL database provides a strong basis for further longitudinal child health outcomes studies and supports the continued development of intergenerational linked clinical-public health databases.

HIV Care Continuum Among People Living With HIV and History of Arrest and Mental Health Diagnosis.

OBJECTIVE: Justice involvement and psychiatric comorbidities contribute to excess HIV morbidity, yet their interaction is poorly understood. We examined associations of this overlap with HIV outcomes among people living with HIV (PLWH). METHODS: We conducted a retrospective cohort study of PLWH aged 13 years and older residing in Marion County (Indianapolis), IN, during 2018 (n = 5730) using linked HIV surveillance, arrest, and clinical data. We used univariable and multivariable regression to evaluate main and interaction effects of 2010-2017 arrest and mental health diagnosis on 2018 linkage to care (LTC), retention in care (RIC), and undetectable viral load (UVL). RESULTS: LTC decreased among those with, versus without, an arrest (P = 0.02), although mental health diagnoses had no significant effect on LTC. When controlling for demographics and substance use disorder, analyses indicated a protective effect of arrest history on odds of RIC (adjusted odds ratio [aOR] = 1.54) and UVL (aOR = 1.26). Mental health diagnosis also increased odds of RIC (aOR = 2.02) and UVL (aOR = 1.95). Post hoc tests demonstrated that these results were mediated by outpatient care utilization, although an arrest or mental health diagnosis did increase odds of RIC among PLWH and a history of low outpatient utilization. CONCLUSIONS: Outpatient care utilization improves HIV outcomes, even among those with justice involvement and psychiatric comorbidities. Holistic approaches to care can increase utilization. Implementation of "no wrong door" approaches, such as integration of mental health care in the primary care setting, simplifies health care navigation and improves access. Among those arrested, access to a Behavioral Court program can improve, rather than disrupt, HIV care.

A Solutions-Based Approach to Building Data-Sharing Partnerships.

INTRODUCTION: Although researchers recognize that sharing disparate data can improve population health, barriers (technical, motivational, economic, political, legal, and ethical) limit progress. In this paper, we aim to enhance the van Panhuis et al. framework of barriers to data sharing; we present a complementary solutions-based data-sharing process in order to encourage both emerging and established researchers, whether or not in academia, to engage in data-sharing partnerships. BRIEF DESCRIPTION OF MAJOR COMPONENTS: We enhance the van Panhuis et al. framework in three ways. First, we identify the appropriate stakeholder(s) within an organization (e.g., criminal justice agency) with whom to engage in addressing each category of barriers. Second, we provide a representative sample of specific challenges that we have faced in our data-sharing partnerships with criminal justice agencies, local clinical systems, and public health. Third, and most importantly, we suggest solutions we have found successful for each category of barriers. We grouped our solutions into five core areas that cut across the barriers as well as stakeholder groups: Preparation, Clear Communication, Funding/Support, Non-Monetary Benefits, and Regulatory Assurances.Our solutions-based process model is complementary to the enhanced framework. An important feature of the process model is the cyclical, iterative process that undergirds it. Usually, interactions with new data-sharing partner organizations begin with the leadership team and progress to both the data management and legal teams; however, the process is not always linear. CONCLUSIONS AND NEXT STEPS: Data sharing is a powerful tool in population health research, but significant barriers hinder such partnerships. Nevertheless, by aspiring to community-based participatory research principles, including partnership engagement, development, and maintenance, we have overcome barriers identified in the van Panhuis et al. framework and have achieved success with various data-sharing partnerships.In the future, systematically studying data-sharing partnerships to clarify which elements of a solutions-based approach are essential for successful partnerships may be helpful to academic and non-academic researchers. The organizational climate is certainly a factor worth studying also because it relates both to barriers and to the potential workability of solutions.