A History Of The Impacts Of Discriminatory Policies On Housing And Maternal And Infant Health In An Ohio Neighborhood.

Community-level disinvestment and de facto segregation rooted in decades of discriminatory race-based policies and racism have resulted in unacceptably large infant mortality rates in racial minority neighborhoods across the US. Most community development and housing work, implemented with the goal of addressing health and social inequities, is designed to tackle current challenges in the condition of neighborhoods without a race-conscious lens assessing structural racism and discrimination. Using one historically segregated neighborhood-Linden, in Columbus, Ohio-we detail how state and local policies have affected the neighborhood and shaped neighborhood-level demographics and resources during the past 100 years. We explore how structural racism- and discrimination-informed strategic community reinvestment could provide a solution and yield lasting change.

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis.

BACKGROUND: Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level. METHODS: This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis. RESULTS: By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level. CONCLUSIONS: The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

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.

Addressing Health Equity Goals for COVID-19 Vaccination Using Integrated Data and Mapping Tools: A Collaboration Between Academia, Public Health, and Health Care Systems in Columbus and Franklin County, Ohio.

CONTEXT: Data sharing between local health departments and health care systems is challenging during public health crises. In early 2021, the supply of COVID-19 vaccine was limited, vaccine appointments were difficult to schedule, and state health departments were using a phased approach to determine who was eligible to get the vaccine. PROGRAM: Multiple local health departments and health care systems with the capacity for mobile and pop-up vaccine clinics came together in Columbus and Franklin County, Ohio, with a common objective to coordinate where, when, and how to set up mobile/pop-up COVID-19 vaccine clinics. To support this objective, the Equity Mapping Tool, which is a set of integrated tools, workflows, and processes, was developed, implemented, and deployed in partnership with an academic institution. IMPLEMENTATION: The Equity Mapping Tool was designed after a rapid community engagement phase. Our analytical approaches were informed by community engagement activities, and we translated the Equity Mapping Tool for stakeholders, who typically do not share timely and granular data, to build capacity for data-enabled decision making. DISCUSSION: We discuss our observations related to the sustainability of the Equity Mapping Tool, lessons learned for public health scientists/practitioners, and future directions for extending the Equity Mapping Tool to other jurisdictions and public health crises.

Economic Evaluation in Opioid Modeling: Systematic Review.

OBJECTIVES: The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature. METHODS: A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study. RESULTS: The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies' accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs. CONCLUSION: The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.

311 service requests as indicators of neighborhood distress and opioid use disorder.

Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as "311" requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008-2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.

Health economic design for cost, cost-effectiveness and simulation analyses in the HEALing Communities Study.

BACKGROUND: The HEALing Communities Study (HCS) is designed to implement and evaluate the Communities That HEAL (CTH) intervention, a conceptually driven framework to assist communities in selecting and adopting evidence-based practices to reduce opioid overdose deaths. The goal of the HCS is to produce generalizable information for policy makers and community stakeholders seeking to implement CTH or a similar community intervention. To support this objective, one aim of the HCS is a health economics study (HES), the results of which will inform decisions around fiscal feasibility and sustainability relevant to other community settings. METHODS: The HES is integrated into the HCS design: an unblinded, multisite, parallel arm, cluster randomized, wait list-controlled trial of the CTH intervention implemented in 67 communities in four U.S. states: Kentucky, Massachusetts, New York, and Ohio. The objectives of the HES are to estimate the economic costs to communities of implementing and sustaining CTH; estimate broader societal costs associated with CTH; estimate the cost-effectiveness of CTH for overdose deaths avoided; and use simulation modeling to evaluate the short- and long-term health and economic impact of CTH, including future overdose deaths avoided and quality-adjusted life years saved, and to develop a simulation policy tool for communities that seek to implement CTH or a similar community intervention. DISCUSSION: The HCS offers an unprecedented opportunity to conduct health economics research on solutions to the opioid crisis and to increase understanding of the impact and value of complex, community-level interventions.

Explaining Racial Inequality in Food Security in Columbus, Ohio: A Blinder-Oaxaca Decomposition Analysis.

Food insecurity is a leading public health challenge in the United States. In Columbus, Ohio, as in many American cities, there exists a great disparity between Black and White households in relation to food insecurity. This study investigates the degree to which this gap can be attributed to differences in food shopping behavior, neighborhood perception, and socioeconomic characteristics. A Blinder-Oaxaca decomposition method is used to analyze a household survey dataset collected in 2014. We find a 34.2 percent point difference in food security between White and Black households. Variables related to food shopping behavior, neighborhood perception, and socioeconomic characteristics explain 13.8 percent, 11.6 percent, and 63.1 percent of the difference, respectively. These independent variables combined can explain 68.2 percent of the food security gap between White and Black households. Most of this is attributable to socioeconomic variables. Sense of friendship in neighborhood, use of private vehicles, and satisfaction of neighborhood food environment also partially contribute to the food security gap.

The Evidence Base for Social Determinants of Health as Risk Factors for Infant Mortality: A Systematic Scoping Review.

The objectives of this study were to identify social determinants of health as risk factors for infant mortality, particularly among African Americans, and to determine the extent to which research has demonstrated an association between each social determinant of health and infant mortality. A systematic scoping review was conducted following PRISMA guidelines. Databases searched include: EBSCOhost, CINAHL Plus, PsycINFO, SocINDEX, Social Work Abstracts, and Sociological Collection. Following a three-step selection process conducted by two reviewers, 89 studies were included. The social ecological model was used to categorize both the search terms and the findings. Findings revealed that the majority of studies focused on the individual and public policy levels, and most failed to account for the complexity of the issue. Additional research is needed to explore the social determinants of health that are hypothesized to affect infant mortality across all levels, applying more complex, system-level approaches.

Public Funding for Genomics and the Return on Investment: A Public Health Perspective.

This article traces the emergence of lean principles in genomics research and connects this new way of doing science with many of the current pitfalls of precision medicine in its attempts at improving population health outcomes. Precision medicine has a history of public funding, yet the benefits in clinical settings are very slowly being realized due to a variety of factors, such as uncertainty regarding relevant treatments after identifying disease risk, lack of cost-effectiveness studies for general population-level interventions, and letting a culture of "over promise and under deliver" permeate some areas of genomics research. The article concludes with insights into the challenges and opportunities that will need careful consideration and consultation with the wider society in order to decide whether to turn off the "tap" for investment of public funds in research on genomics and other "omics." Ultimately, this article argues for a moderate course correction in how public funds are invested to truly improve the health of all of us, and not just some of us.

Social deprivation and burden of influenza: Testing hypotheses and gaining insights from a simulation model for the spread of influenza.

Factors associated with the burden of influenza among vulnerable populations have mainly been identified using statistical methodologies. Complex simulation models provide mechanistic explanations, in terms of spatial heterogeneity and contact rates, while controlling other factors and may be used to better understand statistical patterns and, ultimately, design optimal population-level interventions. We extended a sophisticated simulation model, which was applied to forecast epidemics and validated for predictive ability, to identify mechanisms for the empirical relationship between social deprivation and the burden of influenza. Our modeled scenarios and associated epidemic metrics systematically assessed whether neighborhood composition and/or spatial arrangement could qualitatively replicate this empirical relationship. We further used the model to determine consequences of local-scale heterogeneities on larger scale disease spread. Our findings indicated that both neighborhood composition and spatial arrangement were critical to qualitatively match the empirical relationship of interest. Also, when social deprivation was fully included in the model, we observed lower age-based attack rates and greater delay in epidemic peak week in the most socially deprived neighborhoods. Insights from simulation models complement current understandings from statistical-based association studies. Additional insights from our study are: (1) heterogeneous spatial arrangement of neighborhoods is a necessary condition for simulating observed disparities in the burden of influenza and (2) unmeasured factors may lead to a better quantitative match between simulated and observed rate ratio in the burden of influenza between the most and least socially deprived populations.