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

Evaluation of an Emergency Department Visit Data Mental Health Dashboard

CONTEXT

Local health departments (LHDs) need timely county-level and subcounty-level data to monitor health-related trends, identify health disparities, and inform areas of highest need for interventions as part of their ongoing assessment responsibilities; yet, many health departments rely on secondary data that are not timely and cannot provide subcounty insights.

OBJECTIVE

We developed and evaluated a mental health dashboard in Tableau for an LHD audience featuring statewide syndromic surveillance emergency department (ED) data in North Carolina from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT).

DESIGN

We developed a dashboard that provides counts, crude rates, and ED visit percentages at statewide and county levels, as well as breakdowns by zip code, sex, age group, race, ethnicity, and insurance coverage for 5 mental health conditions. We evaluated the dashboards through semistructured interviews and a Web-based survey that included the standardized usability questions from the System Usability Scale.

PARTICIPANTS

Convenience sample of LHD public health epidemiologists, health educators, evaluators, and public health informaticians.

RESULTS

Six semistructured interview participants successfully navigated the dashboard but identified usability issues when asked to compare county-level trends displayed in different outputs (eg, tables vs graphs). Thirty respondents answered all questions on the System Usability Scale for the dashboard, which received an above average score of 86.

CONCLUSIONS

The dashboards scored well on the System Usability Scale, but more research is needed to identify best practices in disseminating multiyear syndromic surveillance ED visit data on mental health conditions to LHDs.

Prevalence of early childhood obesity in Ireland: Differences over time, between sexes and across child growth criteria

BACKGROUND

Various child growth criteria exist for monitoring overweight and obesity prevalence in young children.

OBJECTIVES

To estimate early overweight and obesity prevalence in Ireland and compare the differences in prevalence across ages, growth criteria and sexes.

METHODS

Longitudinal body mass index data from the nationally representative Growing Up in Ireland infant cohort (n = 11 134) were categorized ('under-/normal weight', 'risk of overweight', 'overweight', 'obesity') using the sex- and age-specific International Obesity Task Force growth reference, World Health Organization growth standard and World Health Organization growth reference criteria. Differences in prevalences between criteria and sexes, and changes in each weight category and criterion across ages (9 months, 3 years, 5 years), were investigated.

RESULTS

Across criteria, 11%-40% of children had overweight or obesity at 9 months, 14%-46% at 3 years and 8%-32% at 5 years of age. Prevalence estimates were highest using the World Health Organization growth reference, followed by International Obesity Task Force estimates. Within each criterion, prevalence decreased significantly over time (p < 0.05). However, when combining both World Health Organization criteria, as recommended for population studies, prevalence increased, due to differences in definitions between them. Significantly more boys than girls had overweight/obesity using either World Health Organization criterion, which was reversed using the International Obesity Task Force growth reference.

CONCLUSIONS

To increase transparency and comparability, studies of childhood obesity need to consider differences in prevalence estimates across growth criteria. Effective prevention, intervention and policy-making are needed to control Ireland's high overweight and obesity prevalence.

The use of electronic health records to inform cancer surveillance efforts: a scoping review and test of indicators for public health surveillance of cancer prevention and control

INTRODUCTION

State cancer prevention and control programs rely on public health surveillance data to set objectives to improve cancer prevention and control, plan interventions, and evaluate state-level progress towards achieving those objectives. The goal of this project was to evaluate the validity of using electronic health records (EHRs) based on common data model variables to generate indicators for surveillance of cancer prevention and control for these public health programs.

METHODS

Following the methodological guidance from the PRISMA Extension for Scoping Reviews, we conducted a literature scoping review to assess how EHRs are used to inform cancer surveillance. We then developed 26 indicators along the continuum of the cascade of care, including cancer risk factors, immunizations to prevent cancer, cancer screenings, quality of initial care after abnormal screening results, and cancer burden. Indicators were calculated within a sample of patients from the New York City (NYC) INSIGHT Clinical Research Network using common data model EHR data and were weighted to the NYC population using post-stratification. We used prevalence ratios to compare these estimates to estimates from the raw EHR of NYU Langone Health to assess quality of information within INSIGHT, and we compared estimates to results from existing surveillance sources to assess validity.

RESULTS

Of the 401 identified articles, 15% had a study purpose related to surveillance. Our indicator comparisons found that INSIGHT EHR-based measures for risk factor indicators were similar to estimates from external sources. In contrast, cancer screening and vaccination indicators were substantially underestimated as compared to estimates from external sources. Cancer screenings and vaccinations were often recorded in sections of the EHR that were not captured by the common data model. INSIGHT estimates for many quality-of-care indicators were higher than those calculated using a raw EHR.

CONCLUSION

Common data model EHR data can provide rich information for certain indicators related to the cascade of care but may have substantial biases for others that limit their use in informing surveillance efforts for cancer prevention and control programs.

Neighborhood-Level Chronic Disease Surveillance: Utility of Primary Care Electronic Health Records and Emergency Department Claims Data

CONTEXT

Disease burden may vary substantively across neighborhoods in an urban setting. Yet, data available for monitoring chronic conditions at the neighborhood level are scarce. Large health care data sets have potential to complement population health surveillance. Few studies have examined the utility of health care data for neighborhood-level surveillance.

OBJECTIVE

We examined the use of primary care electronic health records (EHRs) and emergency department (ED) claims for identifying neighborhoods with higher chronic disease burden and neighborhood-level prevalence estimation.

DESIGN

Comparison of hypertension and diabetes estimates from EHRs and ED claims with survey-based estimates.

SETTING

Forty-two United Hospital Fund neighborhoods in New York City.

PARTICIPANTS

The EHR sample comprised 708 452 patients from the Hub Population Health System (the Hub) in 2015, and the ED claim sample comprised 1 567 870 patients from the Statewide Planning and Research Cooperative System in 2015. We derived survey-based estimates from 2012 to 2016 Community Health Survey (n = 44 189).

MAIN OUTCOME MEASURE

We calculated hypertension and diabetes prevalence estimates by neighborhood from each data source. We obtained Pearson correlation and absolute difference between EHR-based or claims-based estimates and survey-based estimates.

RESULTS

Both EHR-based and claims-based estimates correlated strongly with survey-based estimates for hypertension (0.91 and 0.72, respectively) and diabetes (0.83 and 0.82, respectively) and identified similar neighborhoods of higher burden. For hypertension, 10 and 17 neighborhoods from the EHRs and ED claims, respectively, had an absolute difference of more than 5 percentage points from the survey-based estimate. For diabetes, 15 and 4 neighborhoods from the EHRs and ED claims, respectively, differed from the survey-based estimate by more than 5 percentage points.

CONCLUSIONS

Both EHRs and ED claims data are useful for identifying neighborhoods with greater disease burden and have potential for monitoring chronic conditions at the neighborhood level.

Completeness and representativeness of body mass index in children's electronic general practice records: Linked cross-sectional study in an ethnically-diverse urban population in the United Kingdom

OBJECTIVE

To assess completeness and accuracy of children's body mass index (BMI) recorded in general practice electronic health records (GP-EHRs).

METHODS

We linked National Child Measurement Programme (NCMP) records from 29 839 5-year-olds and 26 660 11-year-olds attending state schools in inner London to GP-EHRs (95% linked; 49.1% girls). We estimated adjusted odds (aOR) of at least one GP-BMI record by sex, ethnic background, area-level deprivation, weight-status and long-term conditions. We examined within-child BMI differences and compared obesity prevalence from these sources.

RESULTS

10.5% (2964/28330) and 26.0% (6598/25365) of 5- and 11-year-olds respectively had at least one GP-BMI record. Underweight (aOR;95% CI:1.71;1.34,2.19), obesity (1.45;1.27,1.65), South Asian background (1.55;1.38,1.74), presence of a long-term condition (8.15;7.31,9.10), and residence in deprived areas (Wald statistic 38.73; P-value<0.0001) were independently associated with at least one GP-BMI record. NCMP-BMI and GP-BMI differed by +0.45(95% Limits of Agreement -1.60,+2.51) and + 0.16(-2.86,+3.18) in 5- and 11-year-olds, respectively. The prevalence of obesity based on GP-BMI was 18.2%(16.1,20.5) and 35.9%(33.9,38.0) in 5- and 11-year-olds respectively, compared to 12.9%(12.5,13.3) and 26.9%(26.4,27.4) based on NCMP-BMI.

CONCLUSION

Child BMI is not comprehensively recorded in urban general practice. Linkage to school measurement records is feasible and enables assessment of health outcomes of obesity.

Chronic Disease Surveillance Using Electronic Health Records From Health Centers in a Large Urban Setting

OBJECTIVES

To assess the validity of electronic health records (EHRs) from a network of health centers for chronic disease surveillance among an underserved population in an urban setting.

DESIGN

EHRs from a network of health centers were used to calculate the prevalence of chronic disease among adult and child patient populations during 2016. Two population-based surveys with local estimates of chronic disease prevalence were compared with the EHR prevalences.

SETTING

A network of health centers that provides health care services to an underserved population in a large urban setting.

PARTICIPANTS

A total of 187 292 patients who had at least 1 health care visit recorded in the Philadelphia health center network.

MAIN OUTCOME MEASURE

Chronic disease indicator (CDI) prevalence of adult obesity, adult smoking, adult diabetes, adult hypertension, child obesity, and child asthma. Health center CDI proportions were compared with survey estimates.

RESULTS

Overall consistency between the health center estimates and surveys varied by CDI. With the exception of childhood obesity, all health center CDI proportions fell within the 95% CI for at least 1 comparison survey estimate. Statistically significant differences were observed and varied by CDI.

CONCLUSIONS

This analysis presents a novel use of existing EHR data to estimate chronic disease prevalence among underserved populations. With the increased use of EHRs in health centers, data from health center networks may supplement chronic disease surveillance efforts, if used appropriately.

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.

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.

Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities

Objective:  To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations.

Methods:  The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords “public health informatics” and “social determinants of health”. The “social determinants of health” search was refined to include articles that contained the keywords “public health”, “population health” or “surveillance”.

Results:  Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health.

Conclusions:  Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this “bridging” is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.