Exploring electronic health records as a population health surveillance tool of cardiovascular disease risk factors

Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review

This study reviews the current utilization of electronic health records (EHRs) for chronic disease surveillance, discusses approaches that are used in obtaining EHR-derived disease prevalence estimates, and identifies health indicators that have been studied using EHR-based surveillance methods. PubMed was searched for relevant keywords: (electronic health records [Title/Abstract] AND surveillance [Title/Abstract]) OR (electronic medical records [Title/Abstract] AND surveillance [Title/Abstract]). Articles were assessed based on detailed inclusion and exclusion criteria and organized by common themes, as per the PRISMA review protocol. The study period was limited to 2015-2021 due to the wider adoption of EHR in the U.S. only since 2015. The review included only US studies and only those that focused on chronic disease surveillance. 17 studies were included in the review. The most common approaches the review identified focused on validating EHR-derived estimates against those from traditional national surveys. The most studied conditions were diabetes, obesity, and hypertension. The majority of reviewed studies demonstrated comparable prevalence estimates with traditional population health surveillance surveys. The most common approach for the estimation of chronic disease conditions was to use small-area estimation by geographic patterns, neighborhoods, or census tracts. The use of EHR-based surveillance systems for public health purposes is feasible, and the population health estimates appear comparable to those obtained through traditional surveillance surveys. The application of EHRs for public health surveillance appears promising and could offer a real-time alternative to traditional surveillance methods. A timely assessment of population health at local and regional levels would ensure a more targeted allocation of public health and healthcare resources as well as more effective intervention and prevention initiatives.

Cardiovascular disease risk prediction for people with type 2 diabetes in a population-based cohort and in electronic health record data

Objective

Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks.

Materials and methods

This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009–2017) and from the Health and Retirement Survey (HRS, 1995–2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores.

Results

The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR.

Discussion and conclusion

EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.

Population-Level Reach of Cardiovascular Disease Prevention Interventions in a Rural Community: Findings from the Heart of New Ulm Project

This study examines participation by residents of a rural community in programs implemented as part of The Heart of New Ulm (HONU) Project, a population-based cardiovascular disease (CVD) prevention initiative. The study compares participation rates for the various interventions to assess which were the most engaging in the priority community and identifies factors that differentiate participants vs. nonparticipants. Participation data were merged with electronic health record (EHR) data representing the larger community population to enable an analysis of participation in the context of the entire community. HONU individual-level interventions engaged 44% of adult residents in the community. Participation ranked as follows: (1) heart health screenings (37% of adult residents), (2) a year-long community weight loss intervention (12% of adult residents), (3) community health challenges (10% of adult residents), and (4) a phone coaching program for invited high CVD-risk residents (enrolled 6% of adult residents). Interventions that yielded the highest engagement were those that had significant staffing and recruited participants over several months, often with many opportunities to participate or register. Compared to nonparticipants, HONU participants were significantly older and a higher proportion were female, married, overweight or obese, and had high cholesterol. Participants also had a lower prevalence of smoking and diabetes than nonparticipants. Findings indicate community-based CVD prevention initiatives can be successful in engaging a high proportion of adult community members. Partnering with local health care systems can allow for use of EHR data to identify eligible participants and evaluate reach and engagement of the priority population.

Cardiovascular Health Trends in Electronic Health Record Data (2012-2015): A Cross-Sectional Analysis of The Guideline Advantage™

BACKGROUND

Electronic health record (EHR) data can measure cardiovascular health (CVH) of patient populations, but have limited generalizability when derived from one health care system.

OBJECTIVE

We used The Guideline Advantage™ (TGA) data repository, comprising EHR data of patients from 8 diverse health care systems, to describe CVH of adult patients and progress towards the American Heart Association's (AHA's) 2020 Impact Goals.

METHODS

Our analysis included 203,488 patients with 677,733 encounters recorded in TGA from 2012 to 2015. Five measures from EHRs [cigarette smoking status, body mass index (BMI), blood pressure (BP), cholesterol, and diabetes mellitus (DM)] were categorized as poor/intermediate/ideal according to AHA's Life's Simple 7 algorithm. We presented distributions and trends of CVH for each metric over time, first using all available data, and then in a subsample (n = 1,890) of patients with complete data on all metrics.

RESULTS

Among all patients, the greatest stride towards ideal CVH attainment from 2012 to 2015 was for cigarette smoking (50.6 percent to 65 percent), followed by DM (17.3 percent to 20.7 percent) and BP (21.1 percent to 23.2 percent). Overall, prevalence of ideal CVH did not increase for any metric in the subsample. Males slightly improved in ideal CVH for BMI and cholesterol; meanwhile, females saw no improvement in ideal CVH for any metric. As ideal CVH for BP and cholesterol increased slightly among white patients, ideal CVH for BP, cholesterol, BMI, and DM worsened for non-whites.

CONCLUSION

Despite improvements in some CVH metrics in the outpatient setting, more tangible progress is needed to meet AHA's 2020 Impact Goals.

Population-level changes in lifestyle risk factors for cardiovascular disease in the Heart of New Ulm Project

Lifestyle significantly influences development of cardiovascular disease (CVD), but limited data exists demonstrating lifestyle improvements in community-based interventions. This study aims to document how lifestyle risk factors changed at the population level in the context of Heart of New Ulm (HONU), a community-based CVD prevention initiative in Minnesota.

HONU intervened across worksites, healthcare and the community/environment to reduce CVD risk factors. HONU collected behavioral measures including smoking, physical activity, fruit/vegetable consumption, alcohol use and stress at heart health screenings from 2009 to 2014. All screenings were documented in the electronic health record (EHR). Changes at the community level for the target population (age 40–79) were estimated using weights created from EHR data and modeled using generalized estimating equation models.

Screening participants were similar to the larger patient population with regard to age, race, and marital status, but were slightly healthier in regards to BMI, LDL cholesterol, blood pressure, and less likely to smoke. Community-level improvements were significant for physical activity (62.8% to 70.5%, p < 0.001) and 5+ daily fruit/vegetable servings (16.9% to 28.1%, p < 0.001), with no significant change in smoking, stress, alcohol or BMI.

By leveraging local EHR data and integrating it with patient-reported outcomes, improvements in nutrition and physical activity were identified in the HONU population, but limited changes were noted for smoking, alcohol consumption and stress. Systematically documenting behaviors in the EHR will help healthcare systems impact the health of the communities they serve, both at the individual and population level.

Assessing the impact of the heart of New Ulm Project on cardiovascular disease risk factors: A population-based program to reduce cardiovascular disease

The Heart of New Ulm Project (HONU), is a population-based project designed to reduce modifiable cardiovascular disease (CVD) risk factors in the rural community of New Ulm, MN. HONU interventions address multiple levels of the social-ecological model. The community is served by one health system, enabling the use of electronic health record (EHR) data for surveillance. The purpose of this study was to assess if trends in CVD risk factors and healthcare utilization differed between a cohort of New Ulm residents age 40-79 and matched controls selected from a similar community, using EHR data from baseline (2008-2009) through three follow up time periods (2010-2011, 2012-2013, 2014-2015). Matching, using covariate balance sparse technique, yielded a sample of 4077 New Ulm residents and 4077 controls. We used mixed effects longitudinal models to examine trends over time between the two groups. Blood pressure, total cholesterol, low-density lipoprotein-cholesterol, and triglycerides showed better management in New Ulm over time compared to the controls. The proportion of residents in New Ulm with controlled blood pressure increased by 6.2 percentage points compared to an increase of 2 points in controls (p < 0.0001). As the cohort aged, 10-year ASCVD risk scores increased less in New Ulm (5.1) than the comparison community (5.9). The intervention and control community did not differ with regard to inpatient stays, smoking, or glucose. Findings suggest efficacy for the HONU project interventions for some outcomes.

Prevalence of Multiple Chronic Conditions Among Older Adults in Florida and the United States: Comparative Analysis of the OneFlorida Data Trust and National Inpatient Sample

Background

Older patients with multiple chronic conditions are often faced with increased health care needs and subsequent higher medical costs, posing significant financial burden to patients, their caregivers, and the health care system. The increasing adoption of electronic health record systems and the proliferation of clinical data offer new opportunities for prevalence studies and for population health assessment. The last few years have witnessed an increasing number of clinical research networks focused on building large collections of clinical data from electronic health records and claims to make it easier and less costly to conduct clinical research.

Objective

The aim of this study was to compare the prevalence of common chronic conditions and multiple chronic conditions in older adults between Florida and the United States using data from the OneFlorida Clinical Research Consortium and the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS).

Methods

We first analyzed the basic demographic characteristics of the older adults in 3 datasets—the 2013 OneFlorida data, the 2013 HCUP NIS data, and the combined 2012 to 2016 OneFlorida data. Then we analyzed the prevalence of each of the 25 chronic conditions in each of the 3 datasets. We stratified the analysis of older adults with hypertension, the most prevalent condition. Additionally, we examined trends (ie, overall trends and then by age, race, and gender) in the prevalence of discharge records representing multiple chronic conditions over time for the OneFlorida (2012-2016) and HCUP NIS cohorts (2003-2013).

Results

The rankings of the top 10 prevalent conditions are the same across the OneFlorida and HCUP NIS datasets. The most prevalent multiple chronic conditions of 2 conditions among the 3 datasets were—hyperlipidemia and hypertension; hypertension and ischemic heart disease; diabetes and hypertension; chronic kidney disease and hypertension; anemia and hypertension; and hyperlipidemia and ischemic heart disease. We observed increasing trends in multiple chronic conditions in both data sources.

Conclusions

The results showed that chronic conditions and multiple chronic conditions are prevalent in older adults across Florida and the United States. Even though slight differences were observed, the similar estimates of prevalence of chronic conditions and multiple chronic conditions across OneFlorida and HCUP NIS suggested that clinical research data networks such as OneFlorida, built from heterogeneous data sources, can provide rich data resources for conducting large-scale secondary data analyses.

Adult weight management across the community: population-level impact of the LOSE IT to WIN IT challenge

Objective

Excess body weight negatively impacts health, but there are few evaluations of low-intensity weight management challenge programs in defined populations. This study examined weight change in adults who participated in the LOSE IT to WIN IT (LIWI) health challenge in a US community. The community-level impact on body mass index was also explored.

Methods

Body weight was analysed over 1 year in the cohort of LIWI enrolees, stratified by participants who were healthy weight or overweight/obese at baseline. Secondarily, a multiple cross-sectional analysis compared the 2.5-year trends in body mass index between community adults who did vs. did not participate in LIWI.

Results

LOSE IT to WIN IT participants who were overweight/obese lost a mean (95% confidence interval) 1.6 (1.2, 2.0) kg (~2%) over 1 year (p < 0.001), whereas healthy weight participants lost 0.7 (0.3, 1.1) kg. Across the community, LIWI participants and non-participants both gained 0.4 kg m-2 over the 2.5-year study period (p = 0.884).

Conclusions

LOSE IT to WIN IT was modestly effective among enrolees, resulting in a small weight loss of 2% over 1 year among those who were overweight/obese. However, LIWI did not impact weight gain in the community. To slow such community-level weight gain trends, weight management challenges must reach larger fractions of the populations that they target.

Generalizability of Indicators from the New York City Macroscope Electronic Health Record Surveillance System to Systems Based on Other EHR Platforms

Introduction:

The New York City (NYC) Macroscope is an electronic health record (EHR) surveillance system based on a distributed network of primary care records from the Hub Population Health System. In a previous 3-part series published in eGEMS, we reported the validity of health indicators from the NYC Macroscope; however, questions remained regarding their generalizability to other EHR surveillance systems.

Methods:

We abstracted primary care chart data from more than 20 EHR software systems for 142 participants of the 2013–14 NYC Health and Nutrition Examination Survey who did not contribute data to the NYC Macroscope. We then computed the sensitivity and specificity for indicators, comparing data abstracted from EHRs with survey data.

Results:

Obesity and diabetes indicators had moderate to high sensitivity (0.81–0.96) and high specificity (0.94–0.98). Smoking status and hypertension indicators had moderate sensitivity (0.78–0.90) and moderate to high specificity (0.88–0.98); sensitivity improved when the sample was restricted to records from providers who attested to Stage 1 Meaningful Use. Hyperlipidemia indicators had moderate sensitivity (≥0.72) and low specificity (≤0.59), with minimal changes when restricting to Stage 1 Meaningful Use.

Discussion:

Indicators for obesity and diabetes used in the NYC Macroscope can be adapted to other EHR surveillance systems with minimal validation. However, additional validation of smoking status and hypertension indicators is recommended and further development of hyperlipidemia indicators is needed.

Conclusion:

Our findings suggest that many of the EHR-based surveillance indicators developed and validated for the NYC Macroscope are generalizable for use in other EHR surveillance systems.

Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records

INTRODUCTION

Electronic health record (EHR) systems provide an opportunity to use a novel data source for population health surveillance. Validation studies that compare prevalence estimates from EHRs and surveys most often use difference testing, which can, because of large sample sizes, lead to detection of significant differences that are not meaningful. We explored a novel application of the two one-sided t test (TOST) to assess the equivalence of prevalence estimates in 2 population-based surveys to inform margin selection for validating EHR-based surveillance prevalence estimates derived from large samples.

METHODS

We compared prevalence estimates of health indicators in the 2013 Community Health Survey (CHS) and the 2013-2014 New York City Health and Nutrition Examination Survey (NYC HANES) by using TOST, a 2-tailed t test, and other goodness-of-fit measures.

RESULTS

A ±5 percentage-point equivalence margin for a TOST performed well for most health indicators. For health indicators with a prevalence estimate of less than 10% (extreme obesity [CHS, 3.5%; NYC HANES, 5.1%] and serious psychological distress [CHS, 5.2%; NYC HANES, 4.8%]), a ±2.5 percentage-point margin was more consistent with other goodness-of-fit measures than the larger percentage-point margins.

CONCLUSION

A TOST with a ±5 percentage-point margin was useful in establishing equivalence, but a ±2.5 percentage-point margin may be appropriate for health indicators with a prevalence estimate of less than 10%. Equivalence testing can guide future efforts to validate EHR data.

Monitoring Prevalence, Treatment, and Control of Metabolic Conditions in New York City Adults Using 2013 Primary Care Electronic Health Records: A Surveillance Validation Study

Introduction:

Electronic health records (EHRs) can potentially extend chronic disease surveillance, but few EHR-based initiatives tracking population-based metrics have been validated for accuracy. We designed a new EHR-based population health surveillance system for New York City (NYC) known as NYC Macroscope. This report is the third in a 3-part series describing the development and validation of that system. The first report describes governance and technical infrastructure underlying the NYC Macroscope. The second report describes validation methods and presents validation results for estimates of obesity, smoking, depression and influenza vaccination. In this third paper we present validation findings for metabolic indicators (hypertension, hyperlipidemia, diabetes).

Methods:

We compared EHR-based estimates to those from a gold standard surveillance source - the 2013–2014 NYC Health and Nutrition Examination Survey (NYC HANES) - overall and stratified by sex and age group, using the two one-sided test of equivalence and other validation criteria.

Results:

EHR-based hypertension prevalence estimates were highly concordant with NYC HANES estimates. Diabetes prevalence estimates were highly concordant when measuring diagnosed diabetes but less so when incorporating laboratory results. Hypercholesterolemia prevalence estimates were less concordant overall. Measures to assess treatment and control of the 3 metabolic conditions performed poorly.

Discussion:

While indicator performance was variable, findings here confirm that a carefully constructed EHR-based surveillance system can generate prevalence estimates comparable to those from gold-standard examination surveys for certain metabolic conditions such as hypertension and diabetes.

Conclusions:

Standardized EHR metrics have potential utility for surveillance at lower annual costs than surveys, especially as representativeness of contributing clinical practices to EHR-based surveillance systems increases.

Rapid identification of familial hypercholesterolemia from electronic health records: The SEARCH study

BACKGROUND

Little is known about prevalence, awareness, and control of familial hypercholesterolemia (FH) in the United States.

OBJECTIVE

To address these knowledge gaps, we developed an ePhenotyping algorithm for rapid identification of FH in electronic health records (EHRs) and deployed it in the Screening Employees And Residents in the Community for Hypercholesterolemia (SEARCH) study.

METHODS

We queried a database of 131,000 individuals seen between 1993 and 2014 in primary care practice to identify 5992 (mean age 52 ± 13 years, 42% men) patients with low-density lipoprotein cholesterol (LDL-C) ≥190 mg/dL, triglycerides <400 mg/dL and without secondary causes of hyperlipidemia.

RESULTS

Our EHR-based algorithm ascertained the Dutch Lipid Clinic Network criteria for FH using structured data sets and natural language processing for family history and presence of FH stigmata on physical examination. Blinded expert review revealed positive and negative predictive values for the SEARCH algorithm at 94% and 97%, respectively. The algorithm identified 32 definite and 391 probable cases with an overall FH prevalence of 0.32% (1:310). Only 55% of the FH cases had a diagnosis code relevant to FH. Mean LDL-C at the time of FH ascertainment was 237 mg/dL; at follow-up, 70% (298 of 423) of patients were on lipid-lowering treatment with 80% achieving an LDL-C ≤100 mg/dL. Of treated FH patients with premature CHD, only 22% (48 of 221) achieved an LDL-C ≤70 mg/dL.

CONCLUSIONS

In a primary care setting, we found the prevalence of FH to be 1:310 with low awareness and control. Further studies are needed to assess whether automated detection of FH in EHR improves patient outcomes.

Changes in cardiovascular risk factors after 5 years of implementation of a population-based program to reduce cardiovascular disease: The Heart of New Ulm Project

BACKGROUND

Population-based interventions aimed at reducing cardiovascular disease (CVD) hold significant potential and will be increasingly relied upon as the model for health care changes in the United States.

METHODS

The Heart of New Ulm Project is a population-based project with health care, community, and workplace interventions addressing multiple levels of the social-ecological model designed to reduce modifiable CVD risk factors in rural New Ulm, MN. The community is served by one health system, enabling the use of electronic health record data for surveillance. Electronic health record data were extracted at baseline (2008-2009) and 2 follow-up periods (2010-2011, 2012-2013) for residents aged 40 to 79 years. Generalized estimating equations were used to fit longitudinal models of the risk factors.

RESULTS

Of 7,855 residents in the target population, 80% had electronic health record data for each period. The prevalence of at goal (blood pressure [BP] <140/90 mm Hg) and (low-density lipoprotein cholesterol [LDL-C] <130 mg/dL) increased from 79.3% to 86.4% and 68.9% to 71.1%, respectively, from baseline to 5 years, with the largest reductions in BP and LDL-C seen in individuals not at goal at baseline. Blood pressure and lipid-lowering medication use increased from 41.8% to 44.0% and 25.3% to 29.1%, respectively. The proportion at goal for glucose increased from 46.9% to 48.2%. The prevalence body mass index <30 kg/m(2) (55%) did not change, whereas the proportion at-goal for high-density lipoprotein decreased from 63.8% to 58%, and smoking showed an increase from 11.3% to 13.6%.

CONCLUSION

In a community participating in a multifaceted, population-based project aimed at reducing modifiable CVD risk factors, significant improvements in BP, LDL-C, and glucose were observed for 5 years, and body mass index remained stable in a state where obesity was increasing.