Information Systems and the Opioid Crisis

Jefferson County, Alabama has alarming opioid statistics. The Jefferson County Alabama Coroner saw a 140% spike in heroin deaths in 2014, and an even more distressing 340% increase in fentanyl deaths from 2013 to 2016 (Jefferson County Coroner/Medical Examiner’s Office 2017). Studies indicate that individuals with prior incidents of non-fatal opioid overdose are among those at greatest risk for subsequent overdose (Wolfenden and Wiggers 2014). Evidence suggests that lack of awareness of, and lack of utilizing available behavioral health resources contributes to exacerbation of mental illness and substance abuse (Johnson et al. 2015). Community health leaders are searching for effective and sustainable models to address these challenges. This research takes an action-design research approach to investigate socio-technical interventions designed and applied to enable improvements to support a Jefferson County Department of Health person-centered peer navigator (PN) initiative through mobile and web-based technologies to coordinate and support client and PN needs. Trained PNs will support, educate, and facilitate clients accessing available community resources, while mobile health technologies will be used to connect clients with resources and collect essential data to measure program outcomes critical for sustaining and maximizing the program’s success and impact. \ \ This study uses an Information Systems Design Theory (ISDT) approach to design a mobile web-based application to aid in providing continuity of PN services and community resources for opioid at-risk clients. Consistent with ISDT, artifact development will consist of four components: 1) meta-requirements, 2) meta-design, 3) kernel theories, and 4) testable design propositions (Walls, Widmeyer, and El Sawy 1992). User Centered Design (UCD) methods will guide the design process as we work closely with our community partner to ensure a useable and useful socio-technical service model for addressing the opioid epidemic in Jefferson County Alabama, with potential application elsewhere.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

Background Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study’s population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants’ protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. Methods This protocol demonstrates how to: (1) securely geocode patients’ residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. Results Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients’ coded census tract locations. Conclusions This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information.

BackgroundMaintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.MethodsThis protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.ResultsCompletion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.ConclusionsThis protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives

Macro-enabled excel file.

Macro-enabled Excel file that can be used to (1) Link census tracts containing patient geocoded addresses to indicators of neighborhood crime and socioeconomic disadvantage using the census tract geoidentifier, and (2) Assign randomly generated identification numbers to census tracts and strip them of geoidentifiers to maintain patient confidentiality. (XLSM)</p

County-level Differences in Liver-related Mortality, Waitlisting, and Liver Transplantation in the United States

Background. Much of our understanding regarding geographic issues in transplantation is based on statistical techniques that do not formally account for geography and is based on obsolete boundaries such as donation service area. Methods. We applied spatial epidemiological techniques to analyze liver-related mortality and access to liver transplant services at the county level using data from the Centers for Disease Control and Prevention and Scientific Registry of Transplant Recipients from 2010 to 2018. Results. There was a significant negative spatial correlation between transplant rates and liver-related mortality at the county level (Moran's I, -0.319; P = 0.001). Significant clusters were identified with high transplant rates and low liver-related mortality. Counties in geographic clusters with high ratios of liver transplants to liver-related deaths had more liver transplant centers within 150 nautical miles (6.7 versus 3.6 centers; P < 0.001) compared with all other counties, as did counties in geographic clusters with high ratios of waitlist additions to liver-related deaths (8.5 versus 2.5 centers; P < 0.001). The spatial correlation between waitlist mortality and overall liver-related mortality was positive (Moran's I, 0.060; P = 0.001) but weaker. Several areas with high waitlist mortality had some of the lowest overall liver-related mortality in the country. Conclusions. These data suggest that high waitlist mortality and allocation model for end-stage liver disease do not necessarily correlate with decreased access to transplant, whereas local transplant center density is associated with better access to waitlisting and transplant

Lost potential and missed opportunities for DCD liver transplantation in the United States

Donation after cardiac death(DCD) has been proposed as an avenue to expand the liver donor pool. We examined factors associated with nonrecovery of DCD livers using UNOS data from 2015 to 2019. There 265 non-recovered potential(NRP) DCD livers. Blood type AB (7.8% vs. 1.1%) and B (16.9% vs. 9.8%) were more frequent in the NRP versus actual donors (p < 0.001). The median driving time between donor hospital and transplant center was similar for NRP and actual donors (30.1 min vs. 30.0 min; p = 0.689), as was the percentage located within a transplant hospital (20.8% vs. 20.9%; p = 0.984).The donation service area(DSA) of a donor hospital explained 27.9% (p = 0.001) of the variability in whether a DCD liver was recovered. A number of potentially high quality DCD donor livers go unrecovered each year, which may be partially explained by donor blood type and variation in regional and DSA level practice patterns. •A significant number of potentially high quality DCD livers go unrecovered.•Non-recovery of DCD livers is not associated with travel distance for recovery.•Local practices may explain a significant percentage of why DCD livers go unrecovered