Community-based Systems Dynamics for Reproductive Health: An Example from Urban Ohio.

BACKGROUND: Health outcomes, risk factors, and policies are complexly related to the reproductive health system. Systems-level frameworks for understanding and acting within communities through community-engaged research are needed to mitigate adverse reproductive health outcomes more effectively within the community. OBJECTIVES: To describe and share lessons learned from an ongoing application of a participatory modeling approach (community-based system dynamics) that aims to eliminate racial inequities in Black-White reproductive health outcomes. METHODS: The community-based system dynamics approach involves conducting complementary activities, workshops, modeling, and dissemination. We organized workshops, co-developed a causal loop diagram of the reproductive health system with participants from the community, and created materials to disseminate workshop findings and preliminary models. LESSONS LEARNED: Many opportunities exist for cross-fertilization of best practices between community-based system dynamics and community-based participatory research. Shared learning environments offer benefits for modelers and domain experts alike. Additionally, identifying local champions from the community helps manage group dynamics. CONCLUSIONS: Community-based system dynamics is well-suited for understanding complexity in the reproductive health system. It allows participants from diverse perspectives to identify strategies to eliminate racial inequities in reproductive health outcomes.

Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county.

OBJECTIVES: An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data. METHODS: We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types. RESULTS: A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26-35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race. CONCLUSIONS: Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.

Consistent screening of admitted infants with head injuries reveals high rate of nonaccidental trauma.

PURPOSE: Implementation of a nonaccidental trauma (NAT) screening guideline for the evaluation of infants admitted with an unwitnessed head injury has eliminated screening disparities. This study sought to determine the overall NAT rate and key predictive factors using this guideline. METHODS: All infants screened via the guideline from 2008 to 2015 were retrospectively reviewed. The overall rate of NAT as determined by our child abuse team was determined. In addition, a logistic regression model was developed to evaluate potential predictors of increased risk of NAT. RESULTS: A total of 563 infants were screened with an overall rate of NAT of 25.6% (n=144). NAT screening was consistent across race and insurance status. By univariate analysis, patients with government insurance or no insurance had a significantly higher rate of NAT, but race was not a factor. Also NAT victims had significantly higher ISS. Skeletal survey showed high positive predictive value of 94%. When regression modeling was performed, ISS, abnormal skeletal survey and having public or no insurance were significantly correlated with NAT, while race showed no correlation. CONCLUSION: One quarter of infants admitted with a head injury not witnessed in a public situation were identified as the victims of NAT. The high rate of abuse among this population supports routine screening in order to avoid missing intentional injuries and preventing future injuries. Race is not a predictor of NAT, but insurance status, as a proxy for socioeconomic status, is correlated, and further investigation is needed. LEVEL OF EVIDENCE: III.