Defining Opioid-related Problems Using a Health Care Safety Net Institution's Inpatient Electronic Health Records: Limitations of Diagnosis-based Definitions.

BACKGROUND: Measuring clinically relevant opioid-related problems in health care systems is challenging due to the lack of standard definitions and coding practices. Well-defined, opioid-related health problems (ORHPs) would improve prevalence estimates and evaluation of clinical interventions, crisis response, and prevention activities. We sought to estimate prevalence of opioid use disorder (OUD), opioid misuse, and opioid poisoning among inpatients at a large, safety net, health care institution. METHODS: Our study included events documented in the electronic health records (EHRs) among hospitalized patients at Denver Health Medical Center during January 1, 2017 to December 31, 2018. Multiple EHR markers (ie, opioid-related diagnostic codes, clinical assessment, laboratory results, and free-text documentation) were used to develop diagnosis-based and extended definitions for OUD, opioid misuse, and opioid poisoning. We used these definitions to estimate number of hospitalized patients with these conditions. RESULTS: During a 2-year study period, 715 unique patients were identified solely using opioid-related diagnostic codes; OUD codes accounted for the largest proportion (499/715, 69.8%). Extended definitions identified an additional 973 unique patients (~136% increase), which includes 155/973 (15.9%) who were identified by a clinical assessment marker, 1/973 (0.1%) by a laboratory test marker, and 817/973 (84.0%) by a clinical documentation marker. CONCLUSIONS: Solely using diagnostic codes to estimate prevalence of clinically relevant ORHPs missed most patients with ORHPs. More inclusive estimates were generated using additional EHR markers. Improved methods to estimate ORHPs among a health care system's patients would more fully estimate organizational and economic burden to more efficiently allocate resources and ensure capacity to provide clinical services.

A process to deduplicate individuals for regional chronic disease prevalence estimates using a distributed data network of electronic health records.

Introduction: Learning health systems can help estimate chronic disease prevalence through distributed data networks (DDNs). Concerns remain about bias introduced to DDN prevalence estimates when individuals seeking care across systems are counted multiple times. This paper describes a process to deduplicate individuals for DDN prevalence estimates. Methods: We operationalized a two-step deduplication process, leveraging health information exchange (HIE)-assigned network identifiers, within the Colorado Health Observation Regional Data Service (CHORDS) DDN. We generated prevalence estimates for type 1 and type 2 diabetes among pediatric patients (0-17 years) with at least one 2017 encounter in one of two geographically-proximate DDN partners. We assessed the extent of cross-system duplication and its effect on prevalence estimates. Results: We identified 218 437 unique pediatric patients seen across systems during 2017, including 7628 (3.5%) seen in both. We found no measurable difference in prevalence after deduplication. The number of cases we identified differed slightly by data reconciliation strategy. Concordance of linked patients' demographic attributes varied by attribute. Conclusions: We implemented an HIE-dependent, extensible process that deduplicates individuals for less biased prevalence estimates in a DDN. Our null pilot findings have limited generalizability. Overlap was small and likely insufficient to influence prevalence estimates. Other factors, including the number and size of partners, the matching algorithm, and the electronic phenotype may influence the degree of deduplication bias. Additional use cases may help improve understanding of duplication bias and reveal other principles and insights. This study informed how DDNs could support learning health systems' response to public health challenges and improve regional health.

A Governance Framework to Integrate Longitudinal Clinical and Community Data in a Distributed Data Network: The Childhood Obesity Data Initiative.

CONTEXT: Integrating longitudinal data from community-based organizations (eg, physical activity programs) with electronic health record information can improve capacity for childhood obesity research. OBJECTIVE: A governance framework that protects individual privacy, accommodates organizational data stewardship requirements, and complies with laws and regulations was developed and implemented to support the harmonization of data from disparate clinical and community information systems. PARTICIPANTS AND SETTING: Through the Childhood Obesity Data Initiative (CODI), 5 Colorado-based organizations collaborated to expand an existing distributed health data network (DHDN) to include community-generated data and assemble longitudinal patient records for research. DESIGN: A governance work group expanded an existing DHDN governance infrastructure with CODI-specific data use and exchange policies and procedures that were codified in a governance plan and a delegated-authority, multiparty, reciprocal agreement. RESULTS: A CODI governance work group met from January 2019 to March 2020 to conceive an approach, develop documentation, and coordinate activities. Governance requirements were synthesized from the CODI use case, and a customized governance approach was constructed to address governance gaps in record linkage, a procedure to request data, and harmonizing community and clinical data. A Master Sharing and Use Agreement (MSUA) and Memorandum of Understanding were drafted and executed to support creation of linked longitudinal records of clinical- and community-derived childhood obesity data. Furthermore, a multiparty infrastructure protocol was approved by the local institutional review board (IRB) to expedite future CODI research by simplifying IRB research applications. CONCLUSION: CODI implemented a clinical-community governance strategy that built trust between organizations and allowed efficient data exchange within a DHDN. A thorough discovery process allowed CODI stakeholders to assess governance capacity and reveal regulatory and organizational obstacles so that the governance infrastructure could effectively leverage existing knowledge and address challenges. The MSUA and complementary governance documents can inform similar efforts.

Associations Between Socioeconomic Context and Congenital Heart Disease Related Outcomes in Adolescents and Adults.

Little is known about the relation between socioeconomic factors and health outcomes in adults and adolescents with congenital heart defects (CHD). Population-level data from the Colorado CHD surveillance system from 2011 to 2013 was used to examine the association between area deprivation and outcomes including hospitalizations, emergency department visits, cardiac procedures, all-cause and cardiac-related mortality, and major adverse cardiac events. Socioeconomic context was measured by the Area Deprivation Index at census tract level. Missing race/ethnicity was imputed using the Bayesian Improved Surname Geocoding algorithm. Generalized linear models were utilized to examine health disparities across deprivation quintiles after adjusting for insurance type, race/ethnicity, age, gender, urbanicity, and CHD severity in 5,748 patients. Cases residing in the most deprived quintile had 51% higher odds of inpatient admission, 74% higher odds of emergency department visit, 41% higher odds of cardiac surgeries, and 45% higher odds of major adverse cardiac events compared with cases in the least deprived quintile. Further, rates of hospitalizations, emergency department admissions, and cardiac surgeries were elevated in the most deprived compared with the least deprived quintile. Mortality was not significantly different across quintiles. In conclusion, findings suggest significant health equity issues for adolescent and adults with CHD based on area-based deprivation.

Evaluating Population Coverage in a Regional Distributed Data Network: Implications for Electronic Health Record-Based Public Health Surveillance.

OBJECTIVE: Electronic health records (EHRs) hold promise as a public health surveillance tool, but questions remain about how EHR patients compare with populations in health and demographic surveys. We compared population characteristics from a regional distributed data network (DDN), which securely and confidentially aggregates EHR data from multiple health care organizations in the same geographic region, with population characteristics from health and demographic surveys. METHODS: Ten health care organizations participating in a Colorado DDN contributed data for coverage estimation. We aggregated demographic and geographic data from 2017 for patients aged ≥18 residing in 7 counties. We used a cross-sectional design to compare DDN population size, by county, with the following survey-estimated populations: the county population, estimated by the American Community Survey (ACS); residents seeking any health care, estimated by the Colorado Health Access Survey; and residents seeking routine (eg, primary) health care, estimated by the Behavioral Risk Factor Surveillance System. We also compared data on the DDN and survey populations by sex, age group, race/ethnicity, and poverty level to assess surveillance system representativeness. RESULTS: The DDN population included 609 840 people in 7 counties, corresponding to 25% coverage of the general adult population. Population coverage ranged from 15% to 35% across counties. Demographic distributions generated by DDN and surveys were similar for many groups. Overall, the DDN and surveys assessing care-seeking populations had a higher proportion of women and older adults than the ACS population. The DDN included higher proportions of Hispanic people and people living in high-poverty neighborhoods compared with the surveys. CONCLUSION: The DDN population is not a random sample of the regional adult population; it is influenced by health care use patterns and organizations participating in the DDN. Strengths and limitations of DDNs complement those of survey-based approaches. The regional DDN is a promising public health surveillance tool.

Population-level surveillance of congenital heart defects among adolescents and adults in Colorado: Implications of record linkage.

BACKGROUND: The objective was to describe the design of a population-level electronic health record (EHR) and insurance claims-based surveillance system of adolescents and adults with congenital heart defects (CHDs) in Colorado and to evaluate the bias introduced by duplicate cases across data sources. METHODS: The Colorado CHD Surveillance System ascertained individuals aged 11-64 years with a CHD based on International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic coding between 2011 and 2013 from a diverse network of health care systems and an All Payer Claims Database (APCD). A probability-based identity reconciliation algorithm identified duplicate cases. Logistic regression was conducted to investigate bias introduced by duplicate cases on the relationship between CHD severity (severe compared to moderate/mild) and adverse outcomes including all-cause mortality, inpatient hospitalization, and major adverse cardiac events (myocardial infarction, congestive heart failure, or cerebrovascular event). Sensitivity analyses were conducted to investigate bias introduced by the sole use or exclusion of APCD data. RESULTS: A total of 12,293 unique cases were identified, of which 3,476 had a within or between data source duplicate. Duplicate cases were more likely to be in the youngest age group and have private health insurance, a severe heart defect, a CHD comorbidity, and higher health care utilization. We found that failure to resolve duplicate cases between data sources would inflate the relationship between CHD severity and both morbidity and mortality outcomes by ~15%. Sensitivity analyses indicate that scenarios in which APCD was excluded from case finding or relied upon as the sole source of case finding would also result in an overestimation of the relationship between a CHD severity and major adverse outcomes. DISCUSSION: Aggregated EHR- and claims-based surveillance systems of adolescents and adults with CHD that fail to account for duplicate records will introduce considerable bias into research findings. CONCLUSION: Population-level surveillance systems for rare chronic conditions, such as congenital heart disease, based on aggregation of EHR and claims data require sophisticated identity reconciliation methods to prevent bias introduced by duplicate cases.

Postpartum Contraceptive Use Among Denver-Based Adolescents and Young Adults: Association with Subsequent Repeat Delivery.

STUDY OBJECTIVE: To determine the association of postpartum contraceptive use with repeat deliveries among adolescents and youth. DESIGN: Retrospective, observational analysis of electronic health record data. SETTING: Single, urban facility in Denver, Colorado, United States. PARTICIPANTS: Women aged 10-24 years who gave birth between January 1, 2011 and December 31, 2015. INTERVENTIONS AND MAIN OUTCOME MEASURES: Postpartum contraceptive use and time to subsequent delivery. RESULTS: Among 4068 women, 1735 (43%) used postpartum contraception. In adjusted analyses, characteristics associated with contraceptive use included Hispanic ethnicity (relative risk [RR], 1.1; P = .03), incremental prenatal visits (RR, 1.01; P = .047), and attendance at postpartum care (RR, 1.60; P < .001). Long-acting reversible contraceptive (LARC) use was higher among women younger than 15 years (reference: 20-24 years; RR, 1.12; P < .001) and lower among women aged 18-19 years (RR, 0.93; P = .009). Hispanic women had higher rates of LARC use than non-Hispanic women (RR, 1.07; P = .02). Compared with inpatient LARC placement, outpatient placement (1-4 weeks and 5 or more weeks) rates were lower (RR, 0.77 and RR, 0.89, respectively; P < .001). Time to subsequent delivery was shorter in non-LARC users (median, 659 days) and contraception nonusers (median, 624 days) compared with LARC users (median, 790 days; P < .001); non-LARC postpartum contraceptive use did not significantly alter time to repeat delivery compared with that in women who used no method (P = .24). CONCLUSION: Postpartum LARC use reduced the risk of repeat pregnancy with a significant increase in time to the next delivery. Non-LARC use was not different from no contraceptive use in terms of time to repeat delivery.

Geospatial Monitoring of Body Mass Index: Use of Electronic Health Record Data Across Health Care Systems.

OBJECTIVES: The Colorado BMI Monitoring System was developed to assess geographic (ie, census tract) patterns of obesity prevalence rates among children and adults in the Denver-metropolitan region. This project also sought to assess the feasibility of a surveillance system that integrates data across multiple health care and governmental organizations. MATERIALS AND METHODS: We extracted data on height and weight measures, obtained through routine clinical care, from electronic health records (EHRs) at multiple health care sites. We selected sites from 5 Denver health care systems and collected data from visits that occurred between January 1, 2013, and December 31, 2015. We produced shaded maps showing observed obesity prevalence rates by census tract for various geographic regions across the Denver-metropolitan region. RESULTS: We identified clearly distinguishable areas by higher rates of obesity among children than among adults, with several pockets of lower body mass index. Patterns for adults were similar to patterns for children: the highest obesity prevalence rates were concentrated around the central part of the metropolitan region. Obesity prevalence rates were moderately higher along the western and northern areas than in other parts of the study region. PRACTICE IMPLICATIONS: The Colorado BMI Monitoring System demonstrates the feasibility of combining EHRs across multiple systems for public health and research. Challenges include ensuring de-duplication across organizations and ensuring that geocoding is performed in a consistent way that does not pose a risk for patient privacy.

Estimating the prevalence of congenital heart disease among adolescents and adults in Colorado adjusted for incomplete case ascertainment.

BACKGROUND: Congenital heart defects (CHDs), the most common type of birth defect in the United States, are increasing in prevalence in the general population. Though CHD prevalence at birth has been well described in the United States at about 1%, little is known about long-term survival and prevalence of CHDs beyond childhood. This study aimed to estimate the prevalence of CHDs among adolescents and adults in Colorado. METHODS: The prevalence of CHDs among adolescents and adults residing in Colorado during 2011 to 2013 was estimated using log-linear capture-recapture methods to account for incomplete case ascertainment. Five case-finding data sources were used for this analysis including electronic health record data from 4 major health systems and a state-legislated all payer claims database. RESULTS: Twelve thousand two hundred ninety-three unique individuals with CHDs (2481 adolescents and 9812 adults) were identified in one or more primary data sources. We estimated the crude prevalence of CHDs in adolescents and adults in Colorado to be 3.22 per 1000 individuals (95% CI 3.19-3.53). After accounting for incomplete case ascertainment, the final capture-recapture model yielded an estimated total adolescent and adult CHD population of 23,194 (95% CI 22,419-23,565) and an adjusted prevalence of 6.07 per 1000 individuals (95% CI 5.86-6.16), indicating 47% of the cases in the catchment area were not identified in the case-identifying data sources. CONCLUSION: This statewide study yielded new information on the prevalence of CHDs in adolescents and adults. These high prevalence rates underscore the need for additional specialized care facilities for this population with CHDs.

The Detection of Opioid Misuse and Heroin Use From Paramedic Response Documentation: Machine Learning for Improved Surveillance.

BACKGROUND: Timely, precise, and localized surveillance of nonfatal events is needed to improve response and prevention of opioid-related problems in an evolving opioid crisis in the United States. Records of naloxone administration found in prehospital emergency medical services (EMS) data have helped estimate opioid overdose incidence, including nonhospital, field-treated cases. However, as naloxone is often used by EMS personnel in unconsciousness of unknown cause, attributing naloxone administration to opioid misuse and heroin use (OM) may misclassify events. Better methods are needed to identify OM. OBJECTIVE: This study aimed to develop and test a natural language processing method that would improve identification of potential OM from paramedic documentation. METHODS: First, we searched Denver Health paramedic trip reports from August 2017 to April 2018 for keywords naloxone, heroin, and both combined, and we reviewed narratives of identified reports to determine whether they constituted true cases of OM. Then, we used this human classification as reference standard and trained 4 machine learning models (random forest, k-nearest neighbors, support vector machines, and L1-regularized logistic regression). We selected the algorithm that produced the highest area under the receiver operating curve (AUC) for model assessment. Finally, we compared positive predictive value (PPV) of the highest performing machine learning algorithm with PPV of searches of keywords naloxone, heroin, and combination of both in the binary classification of OM in unseen September 2018 data. RESULTS: In total, 54,359 trip reports were filed from August 2017 to April 2018. Approximately 1.09% (594/54,359) indicated naloxone administration. Among trip reports with reviewer agreement regarding OM in the narrative, 57.6% (292/516) were considered to include information revealing OM. Approximately 1.63% (884/54,359) of all trip reports mentioned heroin in the narrative. Among trip reports with reviewer agreement, 95.5% (784/821) were considered to include information revealing OM. Combined results accounted for 2.39% (1298/54,359) of trip reports. Among trip reports with reviewer agreement, 77.79% (907/1166) were considered to include information consistent with OM. The reference standard used to train and test machine learning models included details of 1166 trip reports. L1-regularized logistic regression was the highest performing algorithm (AUC=0.94; 95% CI 0.91-0.97) in identifying OM. Tested on 5983 unseen reports from September 2018, the keyword naloxone inaccurately identified and underestimated probable OM trip report cases (63 cases; PPV=0.68). The keyword heroin yielded more cases with improved performance (129 cases; PPV=0.99). Combined keyword and L1-regularized logistic regression classifier further improved performance (146 cases; PPV=0.99). CONCLUSIONS: A machine learning application enhanced the effectiveness of finding OM among documented paramedic field responses. This approach to refining OM surveillance may lead to improved first-responder and public health responses toward prevention of overdoses and other opioid-related problems in US communities.

Estimating Childhood Obesity Prevalence in Communities Through Multi-institutional Data Sharing.

CONTEXT: Although local childhood obesity prevalence estimates would be valuable for planning and evaluating obesity prevention efforts in communities, these data are often unavailable. OBJECTIVE: The primary objective was to create a multi-institutional system for sharing electronic health record (EHR) data to produce childhood obesity prevalence estimates at the census tract level. A secondary objective was to adjust obesity prevalence estimates to population demographic characteristics. DESIGN/SETTING/PARTICIPANTS: The study was set in Denver County, Colorado. Six regional health care organizations shared EHR-derived data from 2014 to 2016 with the state health department for children and adolescents 2 to 17 years of age. The most recent height and weight measured during routine care were used to calculate body mass index (BMI); obesity was defined as BMI of 95th percentile or more for age and sex. Census tract location was determined using residence address. Race/ethnicity was imputed when missing, and obesity prevalence estimates were adjusted by sex, age group, and race/ethnicity. MAIN OUTCOME MEASURE(S): Adjusted obesity prevalence estimates, overall, by demographic characteristics and by census tract. RESULTS: BMI measurements were available for 89 264 children and adolescents in Denver County, representing 73.9% of the population estimate from census data. Race/ethnicity was missing for 4.6%. The county-level adjusted childhood obesity prevalence estimate was 13.9% (95% confidence interval, 13.6-14.1). Adjusted obesity prevalence was higher among males, those 12 to 17 years of age, and those of Hispanic race/ethnicity. Adjusted obesity prevalence varied by census tract (range, 0.4%-24.7%). Twelve census tracts had an adjusted obesity prevalence of 20% or more, with several contiguous census tracts with higher childhood obesity occurring in western areas of the city. CONCLUSIONS: It was feasible to use a system of multi-institutional sharing of EHR data to produce local childhood obesity prevalence estimates. Such a system may provide useful information for communities when implementing obesity prevention programs.

Monitoring opioid addiction and treatment: Do you know if your population is engaged?

BACKGROUND: Assessment of people affected by opioid-related problems and those receiving care is challenging due to lack of common definitions and scattered information. We sought to fill this gap by demonstrating a method to describe a continuum of opioid addiction care in a large, public safety-net institution. METHODS: Using 2017 clinical and administrative data from Denver Health (DH), we created operational definitions for opioid use disorder (OUD), opioid misuse (OM), and opioid poisoning (OP). Six stages along a continuum of patient engagement in opioid addiction care were developed, and operational definitions assigned patients to stages for a specific time point of analysis. National data was used to estimate the Denver population affected by OUD, OM and OP. RESULTS: In 2017, an estimated 6688 people aged ≥12 years were affected by OUD, OM, or OP in Denver; 48.4% (3238/6688) were medically diagnosed in DH. Of those, 32.5% (1051/3238) were in the medication assisted treatment stage, and, of those, 59.8% (629/1051) in the adhered to treatment stage. Among that latter group, 78.4% (493/629) adhered at least 90 days and 52.3% (329/629) for more than one year. Among patients who received medication assisted treatment, less than one third (31.3%, 329/1051) were adherent for more than one year. CONCLUSIONS: A health-system level view of the continuum of opioid addiction care identified improvement opportunities to better monitor accuracy of diagnosis, treatment capacity, and effectiveness of patient engagement. Applied longitudinally at local, state and national levels, the model could better synergize responses to the opioid crisis.

Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data Service.

Electronic health records (EHRs) provide an alternative to traditional public health surveillance surveys and administrative data for measuring the prevalence and impact of chronic health conditions in populations. As the infrastructure for secondary use of EHR data improves, many stakeholders are poised to benefit from data partnerships for regional access to information. Electronic health records can be transformed into a common data model that facilitates data sharing across multiple organizations and allows data to be used for surveillance. The Colorado Health Observation Regional Data Service, a regional distributed data network, has assembled diverse data partnerships, flexible infrastructure, and transparent governance practices to better understand the health of communities through EHR-based, public health surveillance. This article describes attributes of regional distributed data networks using EHR data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. Colorado Health Observation Regional Data Service and our experience may serve as a model for other regions interested in similar surveillance efforts. While benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.

Home Visitations for Delivering an Early Childhood Obesity Intervention in Denver: Parent and Patient Navigator Perspectives.

Objective This qualitative study explored parent and patient navigator perspectives of home visitation as part of a childhood obesity program in a low-income, largely Latino population. Methods Three patient navigators and 25 parents who participated in a home-based, childhood obesity program participated in focus groups or interviews. Emergent themes were identified through content analysis of qualitative data. Results Three overall themes were identified. Patient navigators and parents perceived: (1) enabling characteristics of home-based program delivery which facilitated family participation and/or behavior change (i.e., convenience, increased accountability, inclusion of household members, delivery in a familiar, intimate setting, and individualized pace and content); (2) logistic and cultural challenges to home-based delivery which reduced family participation and program reach (i.e., difficulties scheduling visits, discomfort with visitors in the home, and confusion about the patient navigator's role); and (3) remediable home-based delivery challenges which could be ameliorated by additional study staff (e.g., supervision of children, safety concerns) or through organized group sessions. Both patient navigators and participating parents discussed an interest in group classes with separate, supervised child-targeted programming and opportunities to engage with other families for social support. Conclusions for Practice A home visitation program delivering a pediatric obesity prevention curriculum in Denver was convenient and held families accountable, but posed scheduling difficulties and raised safety concerns. Conducting home visits in pairs, adding obesity prevention curriculum to existing home visiting programs, or pairing the convenience of home visits with group classes may be future strategies to explore.

Monitoring Depression Rates in an Urban Community: Use of Electronic Health Records.

OBJECTIVES: Depression is the most common mental health disorder and mediates outcomes for many chronic diseases. Ability to accurately identify and monitor this condition, at the local level, is often limited to estimates from national surveys. This study sought to compare and validate electronic health record (EHR)-based depression surveillance with multiple data sources for more granular demographic subgroup and subcounty measurements. DESIGN/SETTING: A survey compared data sources for the ability to provide subcounty (eg, census tract [CT]) depression prevalence estimates. Using 2011-2012 EHR data from 2 large health care providers, and American Community Survey data, depression rates were estimated by CT for Denver County, Colorado. Sociodemographic and geographic (residence) attributes were analyzed and described. Spatial analysis assessed for clusters of higher or lower depression prevalence. MAIN OUTCOME MEASURE(S): Depression prevalence estimates by CT. RESULTS: National and local survey-based depression prevalence estimates ranged from 7% to 17% but were limited to county level. Electronic health record data provided subcounty depression prevalence estimates by sociodemographic and geographic groups (CT range: 5%-20%). Overall depression prevalence was 13%; rates were higher for women (16% vs men 9%), whites (16%), and increased with age and homeless patients (18%). Areas of higher and lower EHR-based, depression prevalence were identified. CONCLUSIONS: Electronic health record-based depression prevalence varied by CT, gender, race/ethnicity, age, and living status. Electronic health record-based surveillance complements traditional methods with greater timeliness and granularity. Validation through subcounty-level qualitative or survey approaches should assess accuracy and address concerns about EHR selection bias. Public health agencies should consider the opportunity and evaluate EHR system data as a surveillance tool to estimate subcounty chronic disease prevalence.

The Public Health Community Platform, Electronic Case Reporting, and the Digital Bridge.

At the intersection of new technology advancements, ever-changing health policy, and fiscal constraints, public health agencies seek to leverage modern technical innovations and benefit from a more comprehensive and cooperative approach to transforming public health, health care, and other data into action. State health agencies recognized a way to advance population health was to integrate public health with clinical health data through electronic infectious disease case reporting. The Public Health Community Platform (PHCP) concept of bidirectional data flow and knowledge management became the foundation to build a cloud-based system connecting electronic health records to public health data for a select initial set of notifiable conditions. With challenges faced and lessons learned, significant progress was made and the PHCP grew into the Digital Bridge, a national governance model for systems change, bringing together software vendors, public health, and health care. As the model and technology advance together, opportunities to advance future connectivity solutions for both health care and public health will emerge.

Evaluation of Syndromic Surveillance Systems in 6 US State and Local Health Departments.

OBJECTIVE: Evaluating public health surveillance systems is critical to ensuring that conditions of public health importance are appropriately monitored. Our objectives were to qualitatively evaluate 6 state and local health departments that were early adopters of syndromic surveillance in order to (1) understand the characteristics and current uses, (2) identify the most and least useful syndromes to monitor, (3) gauge the utility for early warning and outbreak detection, and (4) assess how syndromic surveillance impacted their daily decision making. DESIGN: We adapted evaluation guidelines from the Centers for Disease Control and Prevention and gathered input from the Centers for Disease Control and Prevention subject matter experts in public health surveillance to develop a questionnaire. PARTICIPANTS: We interviewed staff members from a convenience sample of 6 local and state health departments with syndromic surveillance programs that had been in operation for more than 10 years. RESULTS: Three of the 6 interviewees provided an example of using syndromic surveillance to identify an outbreak (ie, cluster of foodborne illness in 1 jurisdiction) or detect a surge in cases for seasonal conditions (eg, influenza in 2 jurisdictions) prior to traditional, disease-specific systems. Although all interviewees noted that syndromic surveillance has not been routinely useful or efficient for early outbreak detection or case finding in their jurisdictions, all agreed that the information can be used to improve their understanding of dynamic disease control environments and conditions (eg, situational awareness) in their communities. CONCLUSION: In the jurisdictions studied, syndromic surveillance may be useful for monitoring the spread and intensity of large outbreaks of disease, especially influenza; enhancing public health awareness of mass gatherings and natural disasters; and assessing new, otherwise unmonitored conditions when real-time alternatives are unavailable. Future studies should explore opportunities to strengthen syndromic surveillance by including broader access to and enhanced analysis of text-related data from electronic health records. Health departments may accelerate the development and use of syndromic surveillance systems, including the improvement of the predictive value and strengthening the early outbreak detection capability of these systems. These efforts support getting the right information to the right people at the right time, which is the overarching goal of CDC's Surveillance Strategy.

Managing public health data: mobile applications and mass vaccination campaigns.

In response to data collection challenges during mass immunization events, Denver Public Health developed a mobile application to support efficient public health immunization and prophylaxis activities. The Handheld Automated Notification for Drugs and Immunizations (HANDI) system has been used since 2012 to capture influenza vaccination data during Denver Health's annual employee influenza campaign. HANDI has supported timely and efficient administration and reporting of influenza vaccinations through standardized data capture and database entry. HANDI's mobility allows employee work locations and schedules to be accommodated without the need for a paper-based data collection system and subsequent manual data entry after vaccination. HANDI offers a readily extensible model for mobile data collection to streamline vaccination documentation and reporting, while improving data quality and completeness.

Associations of Adolescent Weight Status and Meeting National Obesity-Related Recommendations.

BACKGROUND: Adolescent overweight and obesity are serious health risks, with prevalence varying by sociodemographic group. Studies link children's weight status and sex/race-ethnic differences with meeting recommendations for physical activity and diet. But, research examining the intersection of sociodemographic characteristics, behavior, and weight status is limited. This paper aims to identify sociodemographic differences in the association between adolescent weight status and meeting 6 national obesity-related recommendations. METHODS: In 2011-2012, the Healthy Kids Colorado Survey was administered to all Denver high school students. Using descriptive and multivariate modeling, we examined subgroup associations between students' self-reported weight status and physical activity and diet. RESULTS: Students (N = 6652) who met at least 1 recommendation were less likely to be at an unhealthy weight (OR = 0.87); also true for students who met at least 1 physical activity recommendation (OR = 0.80). However, the association varied across subgroups. The association between weight status and meeting at least 1 nutritional recommendation (OR = 0.91) was inconsistent across subgroups. Unexpected patterns also emerged in subgroup associations between meeting specific recommendations and weight status. CONCLUSIONS: Identifying subgroup differences in meeting recommendations and the association with weight status is important in identifying high risk groups and improving policy and programs that target childhood obesity prevention.

A Multilevel Analysis of Individual, Health System, and Neighborhood Factors Associated with Depression within a Large Metropolitan Area.

Depression prevalence is known to vary by individual factors (gender, age, race, medical comorbidities) and by neighborhood factors (neighborhood deprivation). However, the combination of individual- and neighborhood-level data is rarely available to assess their relative contribution to variation in depression across neighborhoods. We geocoded depression diagnosis and demographic data from electronic health records for 165,600 patients seen in two large health systems serving the Denver population (Kaiser Permanente and Denver Health) to Denver's 144 census tracts, and combined these data with indices of neighborhood deprivation obtained from the American Community Survey. Non-linear mixed models examined the relationships between depression rates and individual and census tract variables, stratified by health system. We found higher depression rates associated with greater age, female gender, white race, medical comorbidities, and with lower rates of home owner occupancy, residential stability, and higher educational attainment, but not with economic disadvantage. Among the Denver Health cohort, higher depression rates were associated with higher crime rates and a lower percent of foreign born residents and single mother households. Our findings suggest that individual factors had the strongest associations with depression. Neighborhood risk factors associated with depression point to low community cohesion, while the role of education is more complex. Among the Denver Health cohort, language and cultural barriers and competing priorities may attenuate the recognition and treatment of depression.