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.

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.

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.

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.

Distributed Data Networks That Support Public Health Information Needs.

INTRODUCTION: Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems. METHODS: We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability. RESULTS: Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability. CONCLUSION: Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.

Data for Community Health Assessment in Rural Colorado: A Comparison of Electronic Health Records to Public Health Surveys to Describe Childhood Obesity.

CONTEXT: Community-level data are necessary to inform community health assessments and to plan for appropriate interventions. However, data derived from public health surveys may be limited or unavailable in rural locations. OBJECTIVE: We compared 2 sources of data for community health assessment in rural Colorado, electronic health records (EHRs) and routine public health surveys. DESIGN: Comparison of cross-sectional measures of childhood/youth obesity prevalence and data quality. SETTING: Two rural Colorado counties, La Plata and Prowers. PARTICIPANTS: The EHR cohort comprised patients 2 to 19 years of age who underwent a visit with the largest health care provider in each county. These data included sex, age, weight, height, race, ethnicity, and insurance status. Public health survey data were obtained from 2 surveys, the Colorado Child Health Survey (2-14 years of age) and the Healthy Kids Colorado Survey (15-19 years of age) and included caregiver and self-reported height and weight estimates. MAIN OUTCOME MEASURES: We calculated body mass index percentile for each patient and survey respondent and determined overweight/obesity prevalence by county. We evaluated data source quality indicators according to a rubric developed for this analysis. RESULTS: The EHR sample captured approximately 35% (n = 3965) and 70% (n = 2219) of all children living in La Plata and Prowers Counties, respectively. The EHR prevalence estimates of overweight/obesity were greater in precision than survey data in both counties among children 2 to 14 years of age. In addition, the EHR data were more timely and geographically representative than survey data and provided directly measured height and weight. Conversely, survey data were easier to access and more demographically representative of the overall population. CONCLUSIONS: Electronic health records describing the prevalence of obesity among children/youth living in rural Colorado may complement public health survey data for community health assessment and health improvement planning.

Creating value: unifying silos into public health business intelligence.

INTRODUCTION: Through September 2014, federal investments in health information technology have been unprecedented, with more than 25 billion dollars in incentive funds distributed to eligible hospitals and providers. Over 85 percent of eligible United States hospitals and 60 percent of eligible providers have used certified electronic health record (EHR) technology and received Meaningful Use incentive funds (HITECH Act1). TECHNOLOGY: Certified EHR technology could create new public health (PH) value through novel and rapidly evolving data-use opportunities, never before experienced by PH. The long-standing "silo" approach to funding has fragmented PH programs and departments,2 but the components for integrated business intelligence (i.e., tools and applications to help users make informed decisions) and maximally reuse data are available now. SYSTEMS: Challenges faced by PH agencies on the road to integration are plentiful, but an emphasis on PH systems and services research (PHSSR) may identify gaps and solutions for the PH community to address. CONCLUSION: Technology and system approaches to leverage this information explosion to support a transformed health care system and population health are proposed. By optimizing this information opportunity, PH can play a greater role in the learning health system.

Sociodemographic and clinical characteristics are not clinically useful predictors of refill adherence in patients with hypertension.

BACKGROUND: Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in 2 healthcare delivery systems. METHODS AND RESULTS: We performed retrospective cohort studies of hypertension registries in an inner-city healthcare delivery system (n=17 176) and a health maintenance organization (n=94 297) in Denver, Colo. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than nonadherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed fewer than 3 antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from nonadherent patients (C statistic=0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from nonadherent individuals (C statistic=0.576). CONCLUSIONS: Although certain sociodemographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment.

The association of adherence to antiretroviral therapy with healthcare utilization and costs for medical care.

BACKGROUND: The association between antiretroviral adherence, healthcare utilization and medical costs has not been well studied. OBJECTIVE: To examine the relationship of adherence to antiretroviral medications to healthcare utilization and healthcare costs. METHODS: A retrospective cohort study was conducted using data from 325 previously antiretroviral medication-naive HIV-infected individuals initiating first antiretroviral therapy from 1997 through 2003. The setting was an inner-city safety net hospital and HIV clinic in the US. Adherence was assessed using pharmacy refill data. The average wholesale price was used for prescription costs. Healthcare utilization data and medical costs were obtained from the hospital billing database, and differences according to quartile of adherence were compared using analysis of variance (ANOVA). Multivariate logistic regression was used to assess predictors of higher annual medical costs. Sensitivity analyses were used to examine alternative antiretroviral pricing schemes. The perspective was that of the healthcare provider, and costs were in year 2005 values. RESULTS: In 325 patients followed for a mean (+/- SD) 3.2 (1.9) years, better adherence was associated with lower healthcare utilization but higher total medical costs. Annual non-antiretroviral medical costs were $US 7,612 in the highest adherence quartile versus $US 10,190 in the lowest adherence quartile. However, antiretroviral costs were significantly higher in the highest adherence quartile ($US 17,513 vs $US 8,690), and therefore the total annual medical costs were also significantly higher in the highest versus lowest adherence quartile ($US 25,125 vs $US 18,880). In multivariate analysis, for every 10% increase in adherence, the odds of having annual medical costs in the highest versus lowest quartile increased by 87% (odds ratio 1.87; 95% CI 1.45, 2.40). In sensitivity analyses, very low antiretroviral prices (as seen in resource-limited settings) inverted this relationship - excellent adherence was cost saving. CONCLUSION: Better adherence to antiretroviral medication was associated with decreased healthcare utilization and associated costs; however, because of the high cost of antiretroviral therapy, total medical costs were increased. Combination antiretroviral therapy is known to be cost effective; lower antiretroviral costs may make it cost saving as well.

School-based health centers: improving access and quality of care for low-income adolescents.

OBJECTIVES: We sought to compare visit rates, emergency care use, and markers of quality of care between adolescents who use school-based health centers and those who use other community centers within a safety-net health care system for low-income and uninsured patients. PATIENTS AND METHODS: In this retrospective cohort study we used Denver Health electronic medical chart data, the Denver Health immunization registry, and Denver Public Schools enrollment data for the period from August 1, 2002, to July 31, 2003. The cohort included all 14- to 17-year-old Denver Public Schools high school enrollees who were active Denver Health patients and were either uninsured or insured by Medicaid or the State Children's Health Insurance Program. "School-based health center users" were those who had used a Denver Health school-based health center; "other users" were those who had used a Denver Health community clinic but not a school-based health center. Markers of quality included having a health maintenance visit and receipt of an influenza vaccine, tetanus booster, and hepatitis B vaccine if indicated. Multiple logistic regression analysis that controlled for gender, race/ethnicity, insurance status, chronic illness, and visit rate was used to compare school-based health center users to other users. RESULTS: Although school-based health center users (n = 790) were less likely than other users (n = 925) to be insured (37% vs 73%), they were more likely to have made > or = 3 primary care visits (52% vs 34%), less likely to have used emergency care (17% vs 34%), and more likely to have received a health maintenance visit (47% vs 33%), an influenza vaccine (45% vs 18%), a tetanus booster (33% vs 21%), and a hepatitis B vaccine (46% vs 20%). CONCLUSIONS: These findings suggest that, within a safety-net system, school-based health centers augment access to care and quality of care for underserved adolescents compared with traditional outpatient care sites.

Individually significant risk factors do not provide an accurate clinical prediction rule for infant underimmunization in one disadvantaged urban area.

OBJECTIVE: To define a clinical prediction rule for underimmunization in children of low socioeconomic status. METHODS: We assessed a cohort of 1160 infants born from July 1998 through June 1999 at an urban safety net hospital that received primary care at 4 community health centers. The main outcome measure was up-to-date status with the 3:2:2:2 infant vaccine series at 12 months of age. RESULTS: Latino infants (n = 959, 83% of cohort) had immunization rates of 74%, at least 18% higher than any other racial/ethnic group. Multivariate logistic regression demonstrated the following independent associations (relative risk, 95% confidence interval) for inadequate immunization: non-Latino ethnicity (1.7, 1.4-2.0), maternal smoking (1.3, 1.1-1.7), no health insurance (1.9, 1.4-2.3), late prenatal care (1.9, 1.5-2.3), no pediatric chronic condition (2.1, 1.2-3.1), and no intent to breast-feed (1.3, 1.1-1.6). However, the index of concordance (c-index) for this model was only 0.69. Neither excluding infants who left the health care system nor accounting for infants who were "late starters" for their first vaccines improved the predictive accuracy of the model. CONCLUSIONS: In this predominantly Latino population of low socioeconomic status, Latino infants have higher immunization rates than other infants. However, we were unable to develop a model to reliably predict which infants in this population were underimmunized. Models to predict underimmunization should be tested in other settings. In this population, interventions to improve immunization rates must be targeted at all children without respect to individual risk factors.

Podiatric problems are associated with worse health status in persons with severe mental illness.

The objective of this study was to determine the prevalence of self-reported podiatric impairments and their effect on health status in persons with severe mental illness. A sample of psychiatric outpatients (N=309) underwent interviews assessing medical conditions and health status with the Medical Outcomes Study Short Form-36 (SF-36). Podiatric health was assessed using nine items from the National Health Interview Survey (NHIS). Eighty percent of patients reported at least one podiatric problem. The most common problems were foot pain (48%), nail disorders (35%) and corns/calluses (28%). Prevalence rates were 4-11 times higher than those reported by the general population in the 1990 NHIS. The total number of podiatric problems was inversely related to eight self-reported health status domains and both summary SF-36 scores (all P<==.0001). After controlling for sociodemographic factors, psychiatric illness and medical conditions, the total number of podiatric limitations remained significantly associated with lower patient ratings in four of the eight SF-36 domains and both summary scores. We concluded that persons with severe and persistent mental illness have markedly elevated rates of podiatric problems when compared to the general population group. These problems are associated with worsened self-perceived health status. Addressing podiatric health may be a successful way to improve the overall health of this population.

Strategies to improve immunization rates and well-child care in a disadvantaged population: a cluster randomized controlled trial.

OBJECTIVE: To measure the effect of a multimodal intervention on well-child care visit (WCV) and immunization rates in an inner-city population. DESIGN: Cluster randomized controlled trial. SETTING AND PARTICIPANTS: One-year cohort of 2843 infants born at a hospital in an integrated inner-city health care system. INTERVENTIONS: Eleven clinics were randomly allocated to 1 of 3 study arms: WCV intervention (n = 3), immunization intervention (n = 4), and controls (n = 4). Interventions to improve immunization and WCV rates included both patient-based and clinic-based activities. MAIN OUTCOME MEASURES: Up-to-date status with childhood immunizations and WCVs by age 12 months (primary) and health care utilization and charges (secondary). RESULTS: Compared with the control arm, the WCV and immunization arms had 5% to 6% higher immunization rates and 7% to 8% higher WCV rates. In multivariate analyses that accounted for the clustered nature of the data, the number of immunizations received was greater in the WCV arm than in controls. However, neither the WCV nor the immunization intervention increased WCV or immunization up-to-date rates. The WCV arm had slightly higher health care charges. Neither intervention affected emergency, urgent care or inpatient utilization. CONCLUSIONS: This multimodal intervention produced a small increase in the number of childhood immunizations delivered. However, patient- and clinic-based methods did not lead to significant increases in WCV or immunization up-to-date rates after controlling for other factors. Methods found in some settings to increase immunization up-to-date rates may not be as effective in a population of inner-city socioeconomically disadvantaged children.