Geographic health information systems: a platform to support the 'triple aim'

A Study Examining the Impact of County-Level Demographic, Socioeconomic, and Political Affiliation Characteristics on COVID-19 Vaccination Patterns in Indiana

The COVID-19 vaccination campaign resulted in uneven vaccine uptake throughout the United States, particularly in rural areas, areas with socially and economically disadvantaged groups, and populations that exhibited vaccine hesitancy behaviors. This study examines how county-level sociodemographic and political affiliation characteristics differentially affected patterns of COVID-19 vaccinations in the state of Indiana every month in 2021. We linked county-level demographics from the 2016-2020 American Community Survey Five-Year Estimates and the Indiana Elections Results Database with county-level COVID-19 vaccination counts from the Indiana State Department of Health. We then created twelve monthly linear regression models to assess which variables were consistently being selected, based on the Akaike Information Criterion (AIC) and adjusted R-squared values. The vaccination models showed a positive association with proportions of Bachelor's degree-holding residents, of 40-59 year-old residents, proportions of Democratic-voting residents, and a negative association with uninsured and unemployed residents, persons living below the poverty line, residents without access to the Internet, and persons of Other Race. Overall, after April, the variables selected were consistent, with the model's high adjusted R2 values for COVID-19 cumulative vaccinations demonstrating that the county sociodemographic and political affiliation characteristics can explain most of the variation in vaccinations. Linking county-level sociodemographic and political affiliation characteristics with Indiana's COVID-19 vaccinations revealed inherent inequalities in vaccine coverage among different sociodemographic groups. Increased vaccine uptake could be improved in the future through targeted messaging, which provides culturally relevant advertising campaigns for groups less likely to receive a vaccine, and increasing access to vaccines for rural, under-resourced, and underserved populations.

Characterizing opioid overdose hotspots for place-based overdose prevention and treatment interventions: A geo-spatial analysis of Rhode Island, USA

OBJECTIVE

Examine differences in neighborhood characteristics and services between overdose hotspot and non-hotspot neighborhoods and identify neighborhood-level population factors associated with increased overdose incidence.

METHODS

We conducted a population-based retrospective analysis of Rhode Island, USA residents who had a fatal or non-fatal overdose from 2016 to 2020 using an environmental scan and data from Rhode Island emergency medical services, State Unintentional Drug Overdose Reporting System, and the American Community Survey. We conducted a spatial scan via SaTScan to identify non-fatal and fatal overdose hotspots and compared the characteristics of hotspot and non-hotspot neighborhoods. We identified associations between census block group-level characteristics using a Besag-York-Mollié model specification with a conditional autoregressive spatial random effect.

RESULTS

We identified 7 non-fatal and 3 fatal overdose hotspots in Rhode Island during the study period. Hotspot neighborhoods had higher proportions of Black and Latino/a residents, renter-occupied housing, vacant housing, unemployment, and cost-burdened households. A higher proportion of hotspot neighborhoods had a religious organization, a health center, or a police station. Non-fatal overdose risk increased in a dose responsive manner with increasing proportions of residents living in poverty. There was increased relative risk of non-fatal and fatal overdoses in neighborhoods with crowded housing above the mean (RR 1.19 [95 % CI 1.05, 1.34]; RR 1.21 [95 % CI 1.18, 1.38], respectively).

CONCLUSION

Neighborhoods with increased prevalence of housing instability and poverty are at highest risk of overdose. The high availability of social services in overdose hotspots presents an opportunity to work with established organizations to prevent overdose deaths.

An informatics infrastructure to catalyze cancer control research and practice

PURPOSE

A disconnect often exists between those with the expertise to manage and analyze complex, multi-source data sets, and the clinical, social services, advocacy, and public health professionals who can pose the most relevant questions and best apply the answers. We describe development and implementation of a cancer informatics infrastructure aimed at broadening the usability of community cancer data to inform cancer control research and practice; and we share lessons learned.

METHODS

We built a multi-level database known as The Ohio Cancer Assessment and Surveillance Engine (OH-CASE) to link data from Ohio's cancer registry with community data from the U.S. Census and other sources. Space-and place-based characteristics were assigned to individuals according to residential address. Stakeholder input informed development of an interface for generating queries based on geographic, demographic, and disease inputs and for outputting results aggregated at the state, county, municipality, or zip code levels.

RESULTS

OH-CASE contains data on 791,786 cancer cases diagnosed from 1/1/2006 to 12/31/2018 across 88 Ohio counties containing 1215 municipalities and 1197 zip codes. Stakeholder feedback from cancer center community outreach teams, advocacy organizations, public health, and researchers suggests a broad range of uses of such multi-level data resources accessible via a user interface.

CONCLUSION

OH-CASE represents a prototype of a transportable model for curating and synthesizing data to understand cancer burden across communities. Beyond supporting collaborative research, this infrastructure can serve the clinical, social services, public health, and advocacy communities by enabling targeting of outreach, funding, and interventions to narrow cancer disparities.

Patient- and Community-Oriented Primary Care Approaches for Health in Rural, Remote and Resource-Dependent Places: Insights for Eco-Social Praxis

Accelerating ecological and societal changes require re-imagining the role of primary care and public health to address eco-social concerns in rural and remote places. In this narrative review, we searched literatures on: community-oriented primary care, patient-oriented research engagement, public health and primary care synergies, and primary care addressing social determinants of health. Our analysis was guided by questions oriented to utility for addressing concerns of social-ecological systems in rural, remote contexts characterized by a high degree of reliance on resource extraction and development (e.g., forestry, mining, oil and gas, fisheries, agriculture, ranching and/or renewables). We describe a range of useful frameworks, processes and tools that are oriented toward bolstering the resilience and engagement of both primary care and public health, though few explicitly incorporated considerations of eco-social approaches to health or broader eco-social context(s). In synthesizing the existing evidence base for integration between primary care and public health, the results signal that for community-oriented primary care and related frameworks to be useful in rural and remote community settings, practitioners are required to grapple with complexity, durable relationships, sustainable resources, holistic approaches to clinician training, Indigenous perspectives, and governance.

Using Electronic Health Records to understand the population of local children captured in a large health system in Durham County, NC, USA, and implications for population health research

Although local policies aimed at reducing childhood health inequities can benefit from local data, sample size constraints in population representative health surveys often prevent rigorous evaluations of child health disparities and health care patterns at local levels. Electronic Health Records (EHRs) offer a possible solution as they contain large amounts of information on pediatric patients within a health system. In this paper, we consider the suitability of using EHRs from a large health system to study local children's health by evaluating the extent to which the EHRs capture the county's child population. First, we compare the demographic characteristics of Duke University Health System pediatric patients who live in Durham County, NC (USA) to the child population estimates in the 2015-2019 American Community Survey. We then examine geographic variation in census tract rates of children captured in the EHR data and estimate negative binomial models to assess how tract characteristics are associated with these rates. We also perform these analyses for the subset of pediatric patients who have a well-child encounter. We find that the demographic characteristics of pediatric patients captured by the EHRs are similar to those of the county's child population. Although the county rate of children captured in the EHRs is high, there is variation across census tracts. On average, census tracts with higher concentrations of non-Hispanic Black residents have lower capture rates and tracts with higher concentrations of poverty have higher capture rates, with the poorest tracts showing the largest racial gap in rates of children captured by EHRs. Our findings suggest that EHRs from a large health system can be used to assess children's population health, but that EHR-based evaluations of children's health disparities and health care patterns should account for differences in who is captured by the EHRs based on census tract characteristics.

The importance of weight stabilization amongst those with overweight or obesity: Results from a large health care system

Data on patterns of weight change among adults with overweight or obesity are minimal. We aimed to examine patterns of weight change and associated hospitalizations in a large health system, and to develop a model to predict 2-year significant weight gain. Data from the Duke University Health System was abstracted from 1/1/13 to 12/31/16 on patients with BMI ≥ 25 kg/m2 in 2014. A regression model was developed to predict patients that would increase their weight by 10% within 2 years. We estimated the association between weight change category and all-cause hospitalization using Cox proportional hazards models. Of the 37,253 patients in our cohort, 59% had stable weight over 2 years, while 24% gained ≥ 5% weight and 17% lost ≥ 5% weight. Our predictive model had reasonable discriminatory capacity to predict which individuals would gain ≥ 10% weight over 2 years (AUC 0.73). Compared with stable weight, the risk of hospitalization was increased by 37% for individuals with > 10% weight loss [adj. HR (95% CI): 1.37 (1.25,1.5)], by 30% for those with > 10% weight gain [adj. HR (95% CI): 1.3 (1.19,1.42)], by 18% for those with 5-10% weight loss [adj. HR (95% CI): 1.18 (1.09,1.28)], and by 10% for those with 5-10% weight gain [adj. HR (95% CI): 1.1 (1.02,1.19)]. In this examination of a large health system, significant weight gain or loss of > 10% was associated with increased all-cause hospitalization over 2 years compared with stable weight. This analysis adds to the increasing observational evidence that weight stability may be a key health driver.

Design and analytic considerations for using patient-reported health data in pragmatic clinical trials: report from an NIH Collaboratory roundtable

Pragmatic clinical trials often entail the use of electronic health record (EHR) and claims data, but bias and quality issues associated with these data can limit their fitness for research purposes particularly for study end points. Patient-reported health (PRH) data can be used to confirm or supplement EHR and claims data in pragmatic trials, but these data can bring their own biases. Moreover, PRH data can complicate analyses if they are discordant with other sources. Using experience in the design and conduct of multi-site pragmatic trials, we itemize the strengths and limitations of PRH data and identify situational criteria for determining when PRH data are appropriate or ideal to fill gaps in the evidence collected from EHRs. To provide guidance for the scientific rationale and appropriate use of patient-reported data in pragmatic clinical trials, we describe approaches for ascertaining and classifying study end points and addressing issues of incomplete data, data alignment, and concordance. We conclude by identifying areas that require more research.

Neighborhood-Level Lead Paint Hazard for Children under 6: A Tool for Proactive and Equitable Intervention

Lead is well known for its adverse health effects on children, particularly when exposure occurs at earlier ages. The primary source of lead hazards among young children is paint used in buildings built before 1978. Despite being 100% preventable, some children remain exposed and state and local policies often remain reactive. This study presents a methodology for planners and public health practitioners to proactively address lead risks among young children. Using geospatial analyses, this study examines neighborhood level measurement of lead paint hazard in homes and childcare facilities and the concentration of children aged 0-5. Results highlight areas of potential lead paint hazard hotspots within a county in the Midwestern state studied, which coincides with higher concentration of non-white children. This places lead paint hazard in the context of social determinants of health, where existing disparity in distribution of social and economic resources reinforces health inequity. In addition to being proactive, lead poisoning intervention efforts need to be multi-dimensional and coordinated among multiple parties involved. Identifying children in higher lead paint hazard areas, screening and treating them, and repairing their homes and childcare facilities will require close collaboration of healthcare professionals, local housing and planning authorities, and community members.

Neighborhood socioeconomic deprivation, racial segregation, and organ donation across 5 states

One in 10 people die awaiting transplantation from donor shortage. Only half of Americans register as organ donors. In this cross-sectional study, we evaluated population-level associations of neighborhood socioeconomic deprivation and racial segregation on organ donor registration rates. We analyzed state identification card demographic and organ donor registration data from 5 states to estimate the association between a neighborhood socioeconomic deprivation index (range [0, 1]; higher values indicate more deprivation) and a racial index of concentration at the extreme (ICE) (range [-1, 1]; lower values indicate predominantly black neighborhoods, higher values indicate predominantly white neighborhoods) on organ donor registration rates within a specified geography (census tract or ZIP code tabulation area [ZCTA]). Among 26 720 738 registrants, 32% of the sample were registered organ donors. At the census tract level, with each 0.1 decrease in the deprivation index, the organ donor registration rate increased by 6.8% (95% confidence interval [CI]: 6.6%, 7.0%). With each 0.1 increase in the racial ICE, the rate increased by 1.5% (95% CI: 1.5%, 1.6%). These associations held true at the ZCTA level. Areas with less socioeconomic deprivation and a higher concentration of white residents have higher organ donor registration rates. Public health initiatives should consider neighborhood context and novel data sources in designing optimal intervention strategies.

Opening a "Can of Worms" to Explore the Public's Hopes and Fears About Health Care Data Sharing: Qualitative Study

BACKGROUND

Evidence suggests that health care data sharing may strengthen care coordination, improve quality and safety, and reduce costs. However, to achieve efficient and meaningful adoption of health care data-sharing initiatives, it is necessary to engage all stakeholders, from health care professionals to patients. Although previous work has assessed health care professionals' perceptions of data sharing, perspectives of the general public and particularly of seldom heard groups have yet to be fully assessed.

OBJECTIVE

This study aims to explore the views of the public, particularly their hopes and concerns, around health care data sharing.

METHODS

An original, immersive public engagement interactive experience was developed-The Can of Worms installation-in which participants were prompted to reflect about data sharing through listening to individual stories around health care data sharing. A multidisciplinary team with expertise in research, public involvement, and human-centered design developed this concept. The installation took place in three separate events between November 2018 and November 2019. A combination of convenience and snowball sampling was used in this study. Participants were asked to fill self-administered feedback cards and to describe their hopes and fears about the meaningful use of data in health care. The transcripts were compiled verbatim and systematically reviewed by four independent reviewers using the thematic analysis method to identify emerging themes.

RESULTS

Our approach exemplifies the potential of using interdisciplinary expertise in research, public involvement, and human-centered design to tell stories, collect perspectives, and spark conversations around complex topics in participatory digital medicine. A total of 352 qualitative feedback cards were collected, each reflecting participants' hopes and fears for health care data sharing. Thematic analyses identified six themes under hopes: enablement of personal access and ownership, increased interoperability and collaboration, generation of evidence for better and safer care, improved timeliness and efficiency, delivery of more personalized care, and equality. The five main fears identified included inadequate security and exploitation, data inaccuracy, distrust, discrimination and inequality, and less patient-centered care.

CONCLUSIONS

This study sheds new light on the main hopes and fears of the public regarding health care data sharing. Importantly, our results highlight novel concerns from the public, particularly in terms of the impact on health disparities, both at international and local levels, and on delivering patient-centered care. Incorporating the knowledge generated and focusing on co-designing solutions to tackle these concerns is critical to engage the public as active contributors and to fully leverage the potential of health care data use.

How and when informative visit processes can bias inference when using electronic health records data for clinical research

OBJECTIVE

Electronic health records (EHR) data have become a central data source for clinical research. One concern for using EHR data is that the process through which individuals engage with the health system, and find themselves within EHR data, can be informative. We have termed this process informed presence. In this study we use simulation and real data to assess how the informed presence can impact inference.

MATERIALS AND METHODS

We first simulated a visit process where a series of biomarkers were observed informatively and uninformatively over time. We further compared inference derived from a randomized control trial (ie, uninformative visits) and EHR data (ie, potentially informative visits).

RESULTS

We find that only when there is both a strong association between the biomarker and the outcome as well as the biomarker and the visit process is there bias. Moreover, once there are some uninformative visits this bias is mitigated. In the data example we find, that when the "true" associations are null, there is no observed bias.

DISCUSSION

These results suggest that an informative visit process can exaggerate an association but cannot induce one. Furthermore, careful study design can, mitigate the potential bias when some noninformative visits are included.

CONCLUSIONS

While there are legitimate concerns regarding biases that "messy" EHR data may induce, the conditions for such biases are extreme and can be accounted for.

Estimating Uncertainty Intervals from Collaborating Networks

Effective decision making requires understanding the uncertainty inherent in a prediction. In regression, this uncertainty can be estimated by a variety of methods; however, many of these methods are laborious to tune, generate overconfident uncertainty intervals, or lack sharpness (give imprecise intervals). We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions. Specifically, one network approximates the cumulative distribution function, and the second network approximates its inverse. We refer to this method as Collaborating Networks (CN). Theoretical analysis demonstrates that a fixed point of the optimization is at the idealized solution, and that the method is asymptotically consistent to the ground truth distribution. Empirically, learning is straightforward and robust. We benchmark CN against several common approaches on two synthetic and six real-world datasets, including forecasting A1c values in diabetic patients from electronic health records, where uncertainty is critical. In the synthetic data, the proposed approach essentially matches ground truth. In the real-world datasets, CN improves results on many performance metrics, including log-likelihood estimates, mean absolute errors, coverage estimates, and prediction interval widths.

Democratizing health system data to impact social and environmental health contexts: a novel collaborative community data-sharing model

BACKGROUND

Community health data are infrequently viewed in the context of social and environmental health determinants. We developed a novel data-sharing model to democratize health system data and to facilitate community and population health improvement.

METHODS

Durham County, the City of Durham in North Carolina, Durham health systems and other stakeholders have developed a data-sharing model to inform local community health efforts. Aggregated health system data obtained through clinical encounters are shared publicly, providing data on the prevalence of health conditions of interest to the community.

RESULTS

A community-owned web platform called the Durham Neighborhood Compass provides aggregate health data (e.g. on diabetes, heart disease, stroke and other conditions of interest) in the context of neighborhood social (e.g. income distribution, education level, demographics) and environmental (e.g. housing prices, crime rates, travel routes, school quality, grocery store proximity) contexts. Health data are aggregated annually to help community stakeholders track changes in health and health contexts over time.

CONCLUSIONS

The Durham Neighborhood Compass is among the first collaborative public efforts to democratize health system data in the context of social and environmental health determinants. This model could be adapted elsewhere to support local community and population health improvement initiatives.

Evaluation of associations between asthma exacerbations and distance to roadways using geocoded electronic health records data

Background

Asthma exacerbations in children often require medications, urgent care, and hospitalization. Multiple environmental triggers have been associated with asthma exacerbations, including particulate matter 2.5 (PM2.5) and ozone, which are primarily generated by motor vehicle exhaust. There is mixed evidence as to whether proximity to highways increases risk of asthma exacerbations.

Methods

To evaluate the impact of highway proximity, we assessed the association between asthma exacerbations and the distance of child’s primary residence to two types of roadways in Durham County, North Carolina, accounting for other patient-level factors. We abstracted data from the Duke University Health System electronic health record (EHR), identifying 6208 children with asthma between 2014 and 2019. We geocoded each child’s distance to roadways (both 35 MPH+ and 55 MPH+). We classified asthma exacerbation severity into four tiers and fitted a recurrent event survival model to account for multiple exacerbations.

Results

There was a no observed effect of residential distance from 55+ MPH highway (Hazard Ratio: 0.98 (95% confidence interval: 0.94, 1.01)) and distance to 35+ MPH roadway (Hazard Ratio: 0.98 (95% confidence interval: 0.83, 1.15)) and any asthma exacerbation. Even those children living closest to highways (less 0.25 miles) had no increased risk of exacerbation. These results were consistent across different demographic strata.

Conclusions

While the results were non-significant, the characteristics of the study sample – namely farther distance to roadways and generally good ambient environmental pollution may contribute to the lack of effect. Compared to previous studies, which often relied on self-reported measures, we were able to obtain a more objective assessment of outcomes. Overall, this work highlights the opportunity to use EHR data to study environmental impacts on disease.

Supplementary Information

Supplementary information accompanies this paper at 10.1186/s12889-020-09731-0.

Assessment of Value of Neighborhood Socioeconomic Status in Models That Use Electronic Health Record Data to Predict Health Care Use Rates and Mortality

Importance

Prediction models are widely used in health care as a way of risk stratifying populations for targeted intervention. Most risk stratification has been done using a small number of predictors from insurance claims. However, the utility of diverse nonclinical predictors, such as neighborhood socioeconomic contexts, remains unknown.

Objective

To assess the value of using neighborhood socioeconomic predictors in the context of 1-year risk prediction for mortality and 6 different health care use outcomes in a large integrated care system.

Design, Setting, and Participants

Diagnostic study using data from all adults age 18 years or older who had Kaiser Foundation Health Plan membership and/or use in the Kaiser Permantente Northern California: a multisite, integrated health care delivery system between January 1, 2013, and June 30, 2014. Data were recorded before the index date for each patient to predict their use and mortality in a 1-year post period using a test-train split for model training and evaluation. Analyses were conducted in fall of 2019.

Main Outcomes and Measures

One-year encounter counts (doctor office, virtual, emergency department, elective hospitalizations, and nonelective), total costs, and mortality.

Results

A total of 2 951 588 patients met inclusion criteria (mean [SD] age, 47.2 [17.4] years; 47.8% were female). The mean (SD) Neighborhood Deprivation Index was -0.32 (0.84). The areas under the receiver operator curve ranged from 0.71 for emergency department use (using the LASSO method and electronic health record predictors) to 0.94 for mortality (using the random forest method and electronic health record predictors). Neighborhood socioeconomic status predictors did not meaningfully increase the predictive performance of the models for any outcome.

Conclusions and Relevance

In this study, neighborhood socioeconomic predictors did not improve risk estimates compared with what is obtainable using standard claims data regardless of model used.

The Geography of Farmworker Health in Colorado: An Examination of Disease Clusters and Healthcare Accessibility

Background: Known by some as the "invisible" people because of their precarious work and low social status, migratory and seasonal farmworkers (MSFW) are a critical and underappreciated component to the agriculture industry in the United States. Despite advances in knowledge about the health needs of this population, identifying geographies of high-risk remains a challenging task for community health workers and farmworker advocacy organizations.Methods: Using patient encounter data (2011-2015) from regional Community and Migrant Health Centers (C/MHC), this study investigates the geography of farmworker chronic disease (diabetes, obesity, hypertension) and associated risk factors (anxiety, stress, depression, tobacco use) in Northeastern Colorado through the lens of Geographic Information Science (GIS).Results: Spatial scan statistics (SaTScan) identified disease cluster hot spots in 151 zip codes and chronic disease risk factor clusters in 44 zip codes. Additionally, 13487 farmworkers or 82% of the total population is found in zip codes designated as chronic disease hot spots, while 10,115 or 62% of the population reside in zip codes identified as risk factor hot spots. GIS-based Network Analysis determined that 1,269 farmworkers lived greater than 30 minutes from a C/MHC, or 7.7% of the total population in the study area (n = 16,419).Conclusions: The findings of this study confirm the need for geospatial analytics in farmworker population healthcare management. These methods, combined with multiple contextual and methodological perspectives, will inform appropriate outreach, research, and policy strategies, and further, serve to address the unique geographic challenges facing MSFW's in Northeastern Colorado.

The European medical information framework: A novel ecosystem for sharing healthcare data across Europe

INTRODUCTION

The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET).

METHODS

The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR.

RESULTS

Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention.

CONCLUSIONS

EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence Network (EHDEN). Furthermore, EMIF has facilitated collaborations between partners and continues to promote a wider adoption of principles, technology and architecture through some of its continued work.

The color of health: how racism, segregation, and inequality affect the health and well-being of preterm infants and their families

Racism, segregation, and inequality contribute to health outcomes and drive health disparities across the life course, including for newborn infants and their families. In this review, we address their effects on the health and well-being of newborn infants and their families with a focus on preterm birth. We discuss three causal pathways: increased risk; lower-quality care; and socioeconomic disadvantages that persist into infancy, childhood, and beyond. For each pathway, we propose specific interventions and research priorities that may remedy the adverse effects of racism, segregation, and inequality. Infants and their families will not realize the full benefit of advances in perinatal and neonatal care until we, collectively, accept our responsibility for addressing the range of determinants that shape long-term outcomes.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Identifying geospatial "hot spots" of glomerular diseases: opportunities and challenges

Canney et al. discovered significant geographic clustering of incident glomerular diseases by applying geospatial modeling techniques to routinely collected information. The results provide further evidence to support the role of environmental triggers in the pathogenesis of glomerular diseases. The study highlights the largely untapped capacity to exploit routinely collected administrative data with appropriate analyses to explore disease pathogenesis and inform health service planning.

A Ledger of Me: Personalizing Healthcare Using Blockchain Technology

Personal Health Records (PHRs) have the potential to give patients fine-grained, personalized and secure access to their own medical data and to enable self-management of care. Emergent trends around the use of Blockchain, or Distributed Ledger Technology, seem to offer solutions to some of the problems faced in enabling these technologies, especially to support issues consent, data exchange, and data access. We present an analysis of existing blockchain-based health record solutions and a reference architecture for a "Ledger of Me" system that extends PHR to create a new platform combining the collection and access of medical data and digital interventions with smart contracts. Our intention is to enable patient use of the data in order to support their care and to provide a strong consent mechanisms for sharing of data between different organizations and apps. Ledger of Me is based on around the principle that this combination of event-driven smart contracts, medical record data, and patient control is important for the adoption of blockchain-based solutions for the PHR. The reference architecture we present can serve as the basis of a range of future blockchain-based medical application architectures.

An outcome model approach to transporting a randomized controlled trial results to a target population

OBJECTIVE

Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best to transport RCT results to target populations has focused on weighting RCT data to look like the target data. Simulation work, however, has suggested that an outcome model approach may be preferable. Here, we describe such an approach using source data from the 2 × 2 factorial NAVIGATOR (Nateglinide And Valsartan in Impaired Glucose Tolerance Outcomes Research) trial, which evaluated the impact of valsartan and nateglinide on cardiovascular outcomes and new-onset diabetes in a prediabetic population.

MATERIALS AND METHODS

Our target data consisted of people with prediabetes serviced at the Duke University Health System. We used random survival forests to develop separate outcome models for each of the 4 treatments, estimating the 5-year risk difference for progression to diabetes, and estimated the treatment effect in our local patient populations, as well as subpopulations, and compared the results with the traditional weighting approach.

RESULTS

Our models suggested that the treatment effect for valsartan in our patient population was the same as in the trial, whereas for nateglinide treatment effect was stronger than observed in the original trial. Our effect estimates were more efficient than the weighting approach and we effectively estimated subgroup differences.

CONCLUSIONS

The described method represents a straightforward approach to efficiently transporting an RCT result to any target population.

Development of a Novel Emergency Department Mapping Tool

OBJECTIVES

Develop a built environment mapping workflow. Implement the workflow in the emergency department (ED). Demonstrate the actionable representations of the data that can be collected using this workflow.

BACKGROUND

The design of the healthcare built environment impacts the delivery of patient care and operational efficiency. Studying this environment presents a series of challenges due to the limitations associated with existing technology such as radio-frequency identification. The authors designed a customized mapping workflow to collect high-resolution spatial, temporal, and activity data to improve healthcare environments, with emphasis on patient safety and operational efficiency.

METHOD

A large, urban, academic medical center ED collaborated with an architecture firm to create a data collection, and mapping workflow using ArcGIS tools and data collectors. The authors developed tools to collect data on the entire ED, as well as individual patients, physicians, and nurses. Advanced visual representations were created from the master data set.

RESULTS

In 48 consecutive hourly snapshots, 5,113 data points were collected on patients, physicians, nurses, and other staff reflecting the operations of the ED. Separately, 84 patients, 10 attending physicians, 10 resident physicians, and 17 nurses were tracked.

CONCLUSIONS

The data obtained from this pilot study were used to create advanced visual representations of the ED environment. This cost-effective ED mapping workflow may be applied to other healthcare settings. Further investigation to evaluate the benefits of this high-resolution data is required.

Geographic surveillance of community associated MRSA infections in children using electronic health record data

BACKGROUND

Community- associated methicillin resistant Staphylococcus aureus (CA-MRSA) cause serious infections and rates continue to rise worldwide. Use of geocoded electronic health record (EHR) data to prevent spread of disease is limited in health service research. We demonstrate how geocoded EHR and spatial analyses can be used to identify risks for CA-MRSA in children, which are tied to place-based determinants and would not be uncovered using traditional EHR data analyses.

METHODS

An epidemiology study was conducted on children from January 1, 2002 through December 31, 2010 who were treated for Staphylococcus aureus infections. A generalized estimated equations (GEE) model was developed and crude and adjusted odds ratios were based on S. aureus risks. We measured the risk of S. aureus as standardized incidence ratios (SIR) calculated within aggregated US 2010 Census tracts called spatially adaptive filters, and then created maps that differentiate the geographic patterns of antibiotic resistant and non-resistant forms of S. aureus.

RESULTS

CA-MRSA rates increased at higher rates compared to non-resistant forms, p = 0.01. Children with no or public health insurance had higher odds of CA-MRSA infection. Black children were almost 1.5 times as likely as white children to have CA-MRSA infections (aOR 95% CI 1.44,1.75, p < 0.0001); this finding persisted at the block group level (p < 0.001) along with household crowding (p < 0.001). The youngest category of age (< 4 years) also had increased risk for CA-MRSA (aOR 1.65, 95%CI 1.48, 1.83, p < 0.0001). CA-MRSA encompasses larger areas with higher SIRs compared to non-resistant forms and were found in block groups with higher proportion of blacks (r = 0.517, p < 0.001), younger age (r = 0.137, p < 0.001), and crowding (r = 0.320, p < 0.001).

CONCLUSIONS

In the Atlanta MSA, the risk for CA-MRSA is associated with neighborhood-level measures of racial composition, household crowding, and age of children. Neighborhoods which have higher proportion of blacks, household crowding, and children < 4 years of age are at greatest risk. Understanding spatial relationship at a community level and how it relates to risks for antibiotic resistant infections is important to combat the growing numbers and spread of such infections like CA-MRSA.

Training in Cardiovascular Epidemiology and Prevention: A 50-Year Journey From Makarska to Goa

The first International Ten-Day Teaching Seminar on Cardiovascular Epidemiology and Prevention was held in Makarska, in the former Yugoslavia in August 1968. The goals of the Seminar were to help bridge the gap between cardiology and cardiovascular epidemiology, promote international collaboration, and provide training in cardiovascular epidemiology and prevention. The 50th Seminar took place in Goa, India in June 2018. This perspective article provides an overview of the major accomplishments of the Seminar as well as its persisting challenges. It also addresses unique opportunities as the Seminar embarks on the next phase of international training seminars in cardiovascular epidemiology and prevention. In particular, this article highlights strategies that offer the opportunity to significantly increase the number of Seminar participants annually, especially from low- and middle-income countries and Small Island Developing States where the burden and trend in cardiovascular diseases and cardiometabolic risk factors pose the greatest concerns. It also discusses the importance of using big data for descriptive, predictive, and prescriptive analytics at the local level, and the need to leverage information technology and digital platforms to create greater access to and sharing of lessons learned. The article also highlights the opportunity to embrace active dissemination and implementation research and the science of health care delivery as important components of training in cardiovascular disease epidemiology and prevention.

Spatial dynamics of access to primary care for the medicaid population

Primary care (PC) has always been underestimated and underinvested by the United States health system. Our goal was to investigate the effect of Medicaid expansion and the Affordable Care Act (ACA) provisions on PC access in Broome County, NY, a county that includes both rural and urban areas, and can serve as a benchmark for other regions. We developed a spatial system dynamics model to capture different stages of PC access for the Medicaid population by using the health belief model constructs and simulate the effect of several hypothetical interventions on PC utilisation. The government data portals used as data sources for calibrating our model include the New York State Department of Health, the Medicaid Delivery System Reform Incentive Payment (DSRIP) dashboards, and the US census. In our unique approach, we integrated the simulation results within Geographical Information System (GIS) maps, to assess the influence of geospatial factors on PC access. Our results identify hot spot demographic areas that have poor access to PC service facilities due to transportation constraints and a shortage in PC providers. Our decision support tool informs policymakers about programmes with the strongest impact on improving access to care, considering spatial and temporal characteristics of a region.

Progress towards using community context with clinical data in primary care

Community-level factors have significant impacts on health. There is renewed enthusiasm for integrating these data with electronic health record (EHR) data for use in primary care to improve health equity in the USA. Thus, it is valuable to reflect on what has been published to date. Specifically, we comment on: (1) recommendations about combining community-level factors in EHRs for use in primary care; (2) examples of how these data have been combined and used; and (3) the impact of using combined data on healthcare, patient health and health equity. We found publications discussing the potential of combined data to inform clinical care, target interventions, track population health and spark community partnerships with the goal of reducing health disparities and improving health equity. Although there is great enthusiasm and potential for using these data to inform primary care, there is little evidence of improved healthcare, patient health or health equity.

Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data

IMPORTANCE

Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contribution of neighborhood socioeconomic status (nSES) in predicting health events is unknown and may help inform population-level risk reduction strategies.

OBJECTIVE

To quantify the association of nSES with adverse outcomes and the value of nSES in predicting the risk of adverse outcomes in EHR-based risk models.

DESIGN, SETTING, AND PARTICIPANTS

Cohort study in which data from 90 097 patients 18 years or older in the Duke University Health System and Lincoln Community Health Center EHR from January 1, 2009, to December 31, 2015, with at least 1 health care encounter and residence in Durham County, North Carolina, in the year prior to the index date were linked with census tract data to quantify the association between nSES and the risk of adverse outcomes. Machine learning methods were used to develop risk models and determine how adding nSES to EHR data affects risk prediction. Neighborhood socioeconomic status was defined using the Agency for Healthcare Research and Quality SES index, a weighted measure of multiple indicators of neighborhood deprivation.

MAIN OUTCOMES AND MEASURES

Outcomes included use of health care services (emergency department and inpatient and outpatient encounters) and hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke.

RESULTS

Among the 90 097 patients in the training set of the study (57 507 women and 32 590 men; mean [SD] age, 47.2 [17.7] years) and the 122 812 patients in the testing set of the study (75 517 women and 47 295 men; mean [SD] age, 46.2 [17.9] years), those living in neighborhoods with lower nSES had a shorter time to use of emergency department services and inpatient encounters, as well as a shorter time to hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. The predictive value of nSES varied by outcome of interest (C statistic ranged from 0.50 to 0.63). When added to EHR variables, nSES did not improve predictive performance for any health outcome.

CONCLUSIONS AND RELEVANCE

Social determinants of health, including nSES, are associated with the health of a patient. However, the results of this study suggest that information on nSES may not contribute much more to risk prediction above and beyond what is already provided by EHR data. Although this result does not mean that integrating social determinants of health into the EHR has no benefit, researchers may be able to use EHR data alone for population risk assessment.

Mapping Neighborhood Health Geomarkers To Clinical Care Decisions To Promote Equity In Child Health

Health disparities, which can be understood as disadvantages in health associated with one's social, racial, economic, or physical environment, originate in childhood and persist across an individual's life course. One's neighborhood may drive or influence these disparities. Information on neighborhoods that can characterize their risks-what we call place-based risks-is rarely used in patient care. Community-level data, however, could inform and personalize interventions such as arranging for mold removal from the home of a person with asthma from the moment that person's address is recorded at the site of care. Efficient risk identification could lead to the tailoring of recommendations and targeting of resources, to improve care experiences and clinical outcomes while reducing disparities and costs. In this article we highlight how data on place-based social determinants of health from national and local sources could be incorporated more directly into patient-centered care, adding precision to risk assessment and mitigation. We also discuss how this information could stimulate cross-sector interventions that promote health equity: the attainment of the highest level of health for neighborhoods, patient panels, and individuals. Finally, we draw attention to research questions that focus on the role of geographical place at the bedside.

Behavioral Risk Factors and Regional Variation in Cardiovascular Health Care and Death

INTRODUCTION

Reducing the burden of death from cardiovascular disease includes risk factor reduction and medical interventions.

METHODS

This was an observational analysis at the hospital service area (HSA) level, to examine regional variation and relationships between behavioral risks, health services utilization, and cardiovascular disease mortality (the outcome of interest). HSA-level prevalence of cardiovascular disease behavioral risks (smoking, poor diet, physical inactivity) were calculated from the Behavioral Risk Factor Surveillance System; HSA-level rates of stress tests, diagnostic cardiac catheterization, and revascularization from a statewide multi-payer claims data set from Maine in 2013 (with 606,260 patients aged ≥35 years), and deaths from state death certificate data. Analyses were done in 2016.

RESULTS

There were marked differences across 32 Maine HSAs in behavioral risks: smoking (12.4%-28.6%); poor diet (43.6%-73.0%); and physical inactivity (16.4%-37.9%). After adjustment for behavioral risks, rates of utilization varied by HSA: stress tests (28.2-62.4 per 1,000 person-years, coefficient of variation=17.5); diagnostic cardiac catheterization (10.0-19.8 per 1,000 person-years, coefficient of variation=17.3); and revascularization (4.6-6.2 per 1,000 person-years; coefficient of variation=9.1). Strong HSA-level associations between behavioral risk factors and cardiovascular disease mortality were observed: smoking (R²=0.52); poor diet (R²=0.38); and physical inactivity (R²=0.35), and no association between revascularization and cardiovascular disease mortality after adjustment for behavioral risk factors (R²=0.02). HSA-level behavioral risk factors were also strongly associated with all-cause mortality: smoking (R²=0.57); poor diet (R²=0.49); and physical inactivity (R²=0.46).

CONCLUSIONS

There is substantial regional variation in behavioral risks and cardiac utilization. Behavioral risk factors are associated with cardiovascular disease mortality regionally, whereas revascularization is not. Efforts to reduce cardiovascular disease mortality in populations should focus on prevention efforts targeting modifiable risk factors.

Neighborhood Inequalities in Hepatitis C Mortality: Spatial and Temporal Patterns and Associated Factors

Deaths attributable to hepatitis C (HCV) infection are increasing in the USA even as highly effective treatments become available. Neighborhood-level inequalities create barriers to care and treatment for many vulnerable populations. We seek to characterize citywide trends in HCV mortality rates over time and identify and describe neighborhoods in New York City (NYC) with disproportionately high rates and associated factors. We used a multiple cause of death (MCOD) definition for HCV mortality. Cases identified between January 1, 2006, and December 31, 2014, were geocoded to NYC census tracts (CT). We calculated age-adjusted HCV mortality rates and identified spatial clustering using a local Moran's I test. Temporal trends were analyzed using joinpoint regression. A multistep global and local Poisson modeling approach was used to test for neighborhood associations with sociodemographic indicators. During the study period, 3697 HCV-related deaths occurred in NYC, with an average annual percent increase of 2.6% (p = 0.02). The HCV mortality rates ranged from 0 to 373.6 per 100,000 by CT, and cluster analysis identified significant clustering of HCV mortality (I = 0.23). Regression identified positive associations between HCV mortality and the proportion of non-Hispanic black or Hispanic residents, neighborhood poverty, education, and non-English-speaking households. Local regression estimates identified spatially varying patterns in these associations. The rates of HCV mortality in NYC are increasing and vary by neighborhood. HCV mortality is associated with many indicators of geographic inequality. Results identified neighborhoods in greatest need for place-based interventions to address social determinants that may perpetuate inequalities in HCV mortality.

Using Address Information to Identify Hardships Reported by Families of Children Hospitalized With Asthma

OBJECTIVE

Socioeconomic hardship is common among children hospitalized for asthma but often not practically measurable. Information on where a child resides is universally available. We sought to determine the correlation between neighborhood-level socioeconomic data and family-reported hardships.

METHODS

Caregivers of 774 children hospitalized with asthma answered questions regarding income, financial strain, and primary care access. Addresses were geocoded and linked to zip code-, census tract-, and block group-level (neighborhood) data from the US Census. We then compared neighborhood median household income with family-reported household income; percentage of neighborhood residents living in poverty with family-reported financial strain; and percentage of neighborhood households without an available vehicle with family-reported access to primary care. We constructed heat maps and quantified correlations using Kendall rank correlation coefficient. Receiver operator characteristic curves were used to assess predictive abilities of neighborhood measures.

RESULTS

The cohort was 57% African American and 73% publicly-insured; 63% reported income <$30,000, 32% endorsed ≥4 financial strain measures, and 38% reported less than adequate primary care access. Neighborhood median household income was significantly and moderately correlated with and predictive of reported household income; neighborhood poverty was similarly related to financial strain; neighborhood vehicle availability was weakly correlated with and predictive of primary care access. Correlations and predictions provided by zip code measures were similar to those of census tract and block group.

CONCLUSIONS

Universally available neighborhood information might help efficiently identify children and families with socioeconomic hardships. Systematic screening with area-level socioeconomic measures has the potential to inform resource allocation more efficiently.

Space-Time Cluster Analysis to Detect Innovative Clinical Practices: A Case Study of Aripiprazole in the Department of Veterans Affairs

OBJECTIVE

To identify space-time clusters of changes in prescribing aripiprazole for bipolar disorder among providers in the VA.

DATA SOURCES

VA administrative data from 2002 to 2010 were used to identify prescriptions of aripiprazole for bipolar disorder. Prescriber characteristics were obtained using the Personnel and Accounting Integrated Database.

STUDY DESIGN

We conducted a retrospective space-time cluster analysis using the space-time permutation statistic.

DATA EXTRACTION METHODS

All VA service users with a diagnosis of bipolar disorder were included in the patient population. Individuals with any schizophrenia spectrum diagnoses were excluded. We also identified all clinicians who wrote a prescription for any bipolar disorder medication.

PRINCIPAL FINDINGS

The study population included 32,630 prescribers. Of these, 8,643 wrote qualifying prescriptions. We identified three clusters of aripiprazole prescribing centered in Massachusetts, Ohio, and the Pacific Northwest. Clusters were associated with prescribing by VA-employed (vs. contracted) prescribers. Nurses with prescribing privileges were more likely to make a prescription for aripiprazole in cluster locations compared with psychiatrists. Primary care physicians were less likely.

CONCLUSIONS

Early prescribing of aripiprazole for bipolar disorder clustered geographically and was associated with prescriber subgroups. These methods support prospective surveillance of practice changes and identification of associated health system characteristics.

Achieving 90% Adoption of Clinical Practice Guidelines Using the Delphi Consensus Method in a Large Orthopedic Group

BACKGROUND

Little is known about the implementation rate of clinical practice guidelines (CPGs). Our purpose was to report on the adoption rate of CPGs created and implemented by a large orthopedic group using the Delphi consensus method.

METHODS

The draft CPGs were created before the group's annual meeting by 5 teams each assigned a subset of topics. The draft guidelines included a statement and a summary of the available evidence. Each guideline was debated in both small-group and plenary sessions. Voting was anonymous and a 75% supermajority was required for passage. A Likert scale was used to survey the patient's experience with the process at 1 week, and the Kirkpatrick evaluation model was used to gauge the efficacy of the process over a 6-month time frame.

RESULTS

Eighty-five orthopedic surgeons attended the meeting. Fifteen guidelines grouped into 5 topics were created. All passed. Eighty-six percent of attendees found the process effective and 84% felt that participating in the process made it more likely that they would adopt the guidelines. At 1 week, an average of 62% of attendees stated they were practicing the guideline as written (range: 35%-72%), and at 6 months, 96% stated they were practicing them (range: 82%-100%).

CONCLUSION

We have demonstrated that a modified Delphi method for reaching consensus can be very effective in both creating CPGs and leading to their adoption. Further we have shown that the process is well received by participants and that an inclusionary approach can be highly successful.

Housing code violation density associated with emergency department and hospital use by children with asthma

Local agencies that enforce housing policies can partner with the health care system to target pediatric asthma care. These agencies retain data that can be used to pinpoint potential clusters of high asthma morbidity. We sought to assess whether the density of housing code violations in census tracts-the in-tract asthma-relevant violations (such as the presence of mold or cockroaches) divided by the number of housing units-was associated with population-level asthma morbidity and could be used to predict a hospitalized patient's risk of subsequent morbidity. We found that increased density in housing code violations was associated with population-level morbidity independent of poverty, and that the density explained 22 percent of the variation in rates of asthma-related emergency department visits and hospitalizations. Children who had been hospitalized for asthma had 1.84 greater odds of a revisit to the emergency department or a rehospitalization within twelve months if they lived in the highest quartile of housing code violation tracts, compared to those living in the lowest quartile. Integrating housing and health data could highlight at-risk areas and patients for targeted interventions.

Areas with High Rates of Police-Reported Violent Crime Have Higher Rates of Childhood Asthma Morbidity

OBJECTIVES

To assess whether population-level violent (and all) crime rates were associated with population-level child asthma utilization rates and predictive of patient-level risk of asthma reutilization after a hospitalization.

STUDY DESIGN

A retrospective cohort study of 4638 pediatric asthma-related emergency department visits and hospitalizations between 2011 and 2013 was completed. For population-level analyses, census tract asthma utilization rates were calculated by dividing the number of utilization events within a tract by the child population. For patient-level analyses, hospitalized patients (n = 981) were followed until time of first asthma-related reutilization. The primary predictor was the census tract rate of violent crime as recorded by the police; the all crime (violent plus nonviolent) rate was also assessed.

RESULTS

Census tract-level violent and all crime rates were significantly correlated with asthma utilization rates (both P < .0001). The violent crime rate explained 35% of the population-level asthma utilization variance and remained associated with increased utilization after adjustment for census tract poverty, unemployment, substandard housing, and traffic exposure (P = .002). The all crime rate explained 28% of the variance and was similarly associated with increased utilization after adjustment (P = .02). Hospitalized children trended toward being more likely to reutilize if they lived in higher violent (P = .1) and all crime areas (P = .01). After adjustment, neither relationship was significant.

CONCLUSIONS

Crime data could help facilitate early identification of potentially toxic stressors relevant to the control of asthma for populations and patients.

Using electronic health record data for substance use Screening, Brief Intervention, and Referral to Treatment among adults with type 2 diabetes: Design of a National Drug Abuse Treatment Clinical Trials Network study

BACKGROUND

The Affordable Care Act encourages healthcare systems to integrate behavioral and medical healthcare, as well as to employ electronic health records (EHRs) for health information exchange and quality improvement. Pragmatic research paradigms that employ EHRs in research are needed to produce clinical evidence in real-world medical settings for informing learning healthcare systems. Adults with comorbid diabetes and substance use disorders (SUDs) tend to use costly inpatient treatments; however, there is a lack of empirical data on implementing behavioral healthcare to reduce health risk in adults with high-risk diabetes. Given the complexity of high-risk patients' medical problems and the cost of conducting randomized trials, a feasibility project is warranted to guide practical study designs.

METHODS

We describe the study design, which explores the feasibility of implementing substance use Screening, Brief Intervention, and Referral to Treatment (SBIRT) among adults with high-risk type 2 diabetes mellitus (T2DM) within a home-based primary care setting. Our study includes the development of an integrated EHR datamart to identify eligible patients and collect diabetes healthcare data, and the use of a geographic health information system to understand the social context in patients' communities. Analysis will examine recruitment, proportion of patients receiving brief intervention and/or referrals, substance use, SUD treatment use, diabetes outcomes, and retention.

DISCUSSION

By capitalizing on an existing T2DM project that uses home-based primary care, our study results will provide timely clinical information to inform the designs and implementation of future SBIRT studies among adults with multiple medical conditions.

Implementation of management strategies for diabetes and hypertension: from local to global health in cardiovascular diseases

Diabetes and hypertension are chronic conditions that are growing in prevalence as major causal factors of cardiovascular disease (CVD). The need for chronic-illness surveillance, population-risk management, and successful treatment interventions are crucial for reducing the burden of future CVD. Addressing these problems will require population-risk stratification, task-sharing and -shifting, and community-as well as network-based care. Information technology tools also provide new opportunities for identifying those at risk and for implementing comprehensive approaches to achieving the goal of improved health locally, regionally, nationally, and globally. This article discusses ongoing efforts at one university health center in the implementation of management strategies for diabetes and hypertension at the local, regional, national, and global levels.

Geographic Variation in Hospitalization for Lower Respiratory Tract Infections Across One County

IMPORTANCE

Bronchiolitis and pneumonia are leading causes of pediatric hospitalizations. Identifying geographic patterns in hospitalization rates across small geographic areas could be particularly relevant to targeted patient-level and population-level health care.

OBJECTIVE

To determine whether lower respiratory tract infection hospitalization rates varied geographically across a single county and whether such variability was associated with socioeconomic conditions.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional, population-based study of children hospitalized at one institution for lower respiratory tract infections between January 1, 2010, and December 31, 2013. The setting was Cincinnati Children's Hospital Medical Center, a large, academic, stand-alone pediatric facility located in Hamilton County, Ohio. During the study period, 99.6% of in-county children hospitalized for lower respiratory tract infections were admitted to Cincinnati Children's Hospital Medical Center. Participants were children younger than 2 years who were hospitalized with bronchiolitis and children younger than 18 years who were hospitalized with pneumonia. Patients were identified using discharge diagnosis codes and then geocoded to their home census tract.

EXPOSURES

Primary exposures, linked to each geocoded patient, included census tract-level socioeconomic measures obtained from the 2008 to 2012 American Community Survey (eg, adult educational attainment, unemployment, and poverty). Patient-level variables examined included demographics, presence of a complex chronic condition, length of stay, and cost.

MAIN OUTCOMES AND MEASURES

We calculated bronchiolitis and pneumonia hospitalization rates for Hamilton County and for each of 222 in-county census tracts. Associations between hospitalization rate quintiles and underlying socioeconomic conditions were assessed using the Kruskal-Wallis test. Geographic clustering was assessed using the Getis-Ord Gi* statistic.

RESULTS

There were 1495 bronchiolitis hospitalizations and 1231 pneumonia hospitalizations during the study period. The county rates were 17.5 (range across census tracts, 0-71.4) hospitalizations per 1000 children per year for bronchiolitis and 1.6 (range across census tracts, 0-4.3) hospitalizations per 1000 children per year for pneumonia. There was significant variation in the median hospitalization rates by census tract quintile for bronchiolitis (32.8, 20.8, 14.0, 10.4, and 5.1 per 1000) and for pneumonia (3.3, 2.1, 1.4, 0.9, and 0.3 per 1000). There were also significant, graded differences in socioeconomic measures by hospitalization rate quintile. Hot spots were localized to inner-city, impoverished neighborhoods.

CONCLUSIONS AND RELEVANCE

Bronchiolitis and pneumonia hospitalization rates varied considerably in ways that were related to underlying socioeconomic conditions. Clinical and public health interventions, targeted accordingly, could improve patient-level and population-level management of acute conditions at a reduced cost.

The pharmacy-level asthma medication ratio and population health

BACKGROUND AND OBJECTIVES

Community pharmacies may be positioned for an increased role in population health. We sought to develop a population-level measure of asthma medication fills and assess its relationship to asthma-related utilization.

METHODS

We conducted a retrospective, ecological study (2010-2012). Medication data from a chain of pharmacies (n = 27) within 1 county were used to calculate a Pharmacy-level Asthma Medication Ratio (Ph-AMR), defined as controller fills divided by controller plus rescue fills. Higher values are superior because they indicate more controller compared with rescue fills. The outcome was the asthma-related utilization rate among children in the same census tract as the pharmacy, calculated by dividing all emergency visits and hospitalizations by the number of children in that tract. Covariates, including ecological measures of poverty and access to care, were used in multivariable linear regression.

RESULTS

Overall, 35 467 medications were filled. The median Ph-AMR was 0.53 (range 0.38-0.66). The median utilization rate across included census tracts was 22.4 visits per 1000 child-years (range 1.3-60.9). Tracts with Ph-AMR <0.5 had significantly higher utilization rates than those with Ph-AMR ≥0.5 (26.1 vs 9.9; P = .001). For every 0.1 increase in Ph-AMR, utilization rates decreased by 9.5 (P = .03), after adjustment for underlying poverty and access. Seasonal variation in fills was evident, but pharmacies in high-utilizing tracts filled more rescue than controller medications at nearly every point during the study period.

CONCLUSIONS

Ph-AMR was independently associated with ecological childhood asthma morbidity. Pharmacies may be a community-based leverage point for improving population-level asthma control through targeted interventions.

Beyond HbA1c: Environmental Risk Factors for Diabetic Retinopathy

Diabetic retinopathy affects 4.2 million people in the United States and is the leading cause of blindness in working-aged people. As the prevalence of diabetes continues to rise, cost-effective interventions to decrease blindness from diabetic retinopathy will be paramount. While HbA_1c and duration of disease are known risk factors, they account for only 11% of the risk of developing microvascular complications from the disease. The assessment of environmental risk factors for diabetic eye disease allows for the determination of modifiable population-level challenges that may be addressed to facilitate the end of blindness from diabetes.

Utility of socioeconomic status in predicting 30-day outcomes after heart failure hospitalization

BACKGROUND

An individual's socioeconomic status (SES) is associated with health outcomes and mortality, yet it is unknown whether accounting for SES can improve risk-adjustment models for 30-day outcomes among Centers for Medicare & Medicaid Services beneficiaries hospitalized with heart failure.

METHODS AND RESULTS

We linked clinical data on hospitalized patients with heart failure in the Get With The Guidelines-Heart Failure database (January 2005 to December 2011) with Centers for Medicare & Medicaid Services claims and county-level SES data from the 2012 Area Health Resources Files. We compared the discriminatory capabilities of multivariable models that adjusted for SES, patient, and hospital characteristics to determine whether county-level SES data improved prediction or changed hospital rankings for 30-day all-cause mortality and rehospitalization. After adjusting for patient and hospital characteristics, median household income (per $5000 increase) was inversely associated with odds of 30-day mortality (odds ratio, 0.97; 95% confidence interval, 0.95-1.00; P=0.032) and the percentage of people with at least a high school diploma (per 5 U increase) was associated with lower odds of 30-day rehospitalization (odds ratio, 0.95; 95% confidence interval, 0.91-0.99). After adjustment for county-level SES data, relative to whites, Hispanic ethnicity (odds ratio, 0.70; 95% confidence interval, 0.58-0.83) and black race (odds ratio, 0.57; 95% confidence interval, 0.50-0.65) remained significantly associated with lower 30-day mortality, but had similar 30-day rehospitalization. County-level SES did not improve risk adjustment or change hospital rankings for 30-day mortality or rehospitalization.

CONCLUSIONS

County-level SES data are modestly associated with 30-day outcomes for Centers for Medicare & Medicaid Services beneficiaries hospitalized with heart failure, but do not improve risk adjustment models based on patient characteristics alone.

Methods and initial findings from the Durham Diabetes Coalition: Integrating geospatial health technology and community interventions to reduce death and disability

OBJECTIVE

The Durham Diabetes Coalition (DDC) was established in response to escalating rates of disability and death related to type 2 diabetes mellitus, particularly among racial/ethnic minorities and persons of low socioeconomic status in Durham County, North Carolina. We describe a community-based demonstration project, informed by a geographic health information system (GHIS), that aims to improve health and healthcare delivery for Durham County residents with diabetes.

MATERIALS AND METHODS

A prospective, population-based study is assessing a community intervention that leverages a GHIS to inform community-based diabetes care programs. The GHIS integrates clinical, social, and environmental data to identify, stratify by risk, and assist selection of interventions at the individual, neighborhood, and population levels.

RESULTS

The DDC is using a multifaceted approach facilitated by GHIS to identify the specific risk profiles of patients and neighborhoods across Durham County. A total of 22,982 patients with diabetes in Durham County were identified using a computable phenotype. These patients tended to be older, female, African American, and not covered by private health insurance, compared with the 166,041 persons without diabetes. Predictive models inform decision-making to facilitate care and track outcomes. Interventions include: 1) neighborhood interventions to improve the context of care; 2) intensive team-based care for persons in the top decile of risk for death or hospitalization within the coming year; 3) low-intensity telephone coaching to improve adherence to evidence-based treatments; 4) county-wide communication strategies; and 5) systematic quality improvement in clinical care.

CONCLUSIONS

To improve health outcomes and reduce costs associated with type 2 diabetes, the DDC is matching resources with the specific needs of individuals and communities based on their risk characteristics.