Electronic health records and community health surveillance of childhood obesity

Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity

Real-world data (RWD) like electronic health records (EHR) has great potential for secondary use by health systems and researchers. However, collected primarily for efficient health care, EHR data may not equitably represent local regions and populations, impacting the generalizability of insights learned from it. We assessed the geospatial representativeness of regions in a large health system EHR data using a spatial analysis workflow, which provides a data-driven way to quantify geospatial representation and identify adequately represented regions. We applied the workflow to investigate geospatial patterns of overweight/obesity and depression patients to find regional "hotspots" for potential targeted interventions. Our findings show the presence of geospatial bias in EHR and demonstrate the workflow to identify spatial clusters after adjusting for bias due to the geospatial representativeness. This work highlights the importance of evaluating geospatial representativeness in RWD to guide targeted deployment of limited healthcare resources and generate equitable real-world evidence.

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.

Online two-way estimation and inference via linear mixed-effects models

In this article, we tackle the estimation and inference problem of analyzing distributed streaming data that is collected continuously over multiple data sites. We propose an online two-way approach via linear mixed-effects models. We explicitly model the site-specific effects as random-effect terms, and tackle both between-site heterogeneity and within-site correlation. We develop an online updating procedure that does not need to re-access the previous data and can efficiently update the parameter estimate, when either new data sites, or new streams of sample observations of the existing data sites, become available. We derive the non-asymptotic error bound for our proposed online estimator, and show that it is asymptotically equivalent to the offline counterpart based on all the raw data. We compare with some key alternative solutions both analytically and numerically, and demonstrate the advantages of our proposal. We further illustrate our method with two data applications.

Linking Electronic Health Records to the American Community Survey: Feasibility and Process

Objectives. To assess linkages of patient data from a health care system in the southeastern United States to microdata from the American Community Survey (ACS) with the goal of better understanding health disparities and social determinants of health in the population. Methods. Once a data use agreement was in place, a stratified random sample of approximately 200 000 was drawn of patients aged 25 to 74 years with at least 2 visits between January 1, 2016, and December 31, 2019. Information from the sampled electronic health records (EHRs) was transferred securely to the Census Bureau, put through the Census Person Identification Validation System to assign Protected Identification Keys (PIKs) as unique identifiers wherever possible. EHRs with PIKs assigned were then linked to 2001-2017 ACS records with a PIK. Results. PIKs were assigned to 94% of the sampled patients. Of patients with PIKs, 15.5% matched to persons sampled in the ACS. Conclusions. Linking data from EHRs to ACS records is feasible and, with adjustments for differential coverage, will advance understanding of social determinants and enhance the ability of integrated delivery systems to reflect and affect the health of the populations served. (Am J Public Health. 2022;112(6):923-930. https://doi.org/10.2105/AJPH.2022.306783).

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.

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

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

An electronic medical records study of population obesity prevalence in El Paso, Texas

BACKGROUND

In this study, we determine the feasibility of using electronic medical record (EMR) data to determine obesity prevalence at the census tract level in El Paso County, Texas, located on the U.S.-Mexico border.

METHODS

2012-2018 Body Mass Index (BMI kg/m²) data from a large university clinic system in was geocoded and aggregated to a census tract level. After cleaning and removing duplicate EMR and unusable data, 143,524 patient records were successful geocoded. Maps were created to assess representativeness of EMR data across census tracts, within El Paso County. Additionally, maps were created to display the distribution of obesity across the same geography.

RESULTS

EMR data represented all but one El Paso census tract. Representation ranged from 0.7% to 34.9%. Greatest representation were among census tracts in and around clinics. The mean EMR data BMI (kg/m²) was 30.1, this is approximately 6% less than the 36.0% estimated for El Paso County using the Behavioral Risk Factor Surveillance Study (BRFSS) estimate. At the census tract level, obesity prevalence ranged from 26.6 to 57.6%. The highest obesity prevalence were in areas that tended to be less affluent, with a higher concentration of immigrants, poverty and Latino ethnic concentration.

CONCLUSIONS

EMR data use for obesity surveillance is feasible in El Paso County, Texas, a U.S.-Mexico border community. Findings indicate substantial obesity prevalence variation between census tracts within El Paso County that may be associated with population distributions related to socioeconomics.

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.

Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient Electronic Health Records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes.

Disparities in Elevated Body Mass Index in Youth Receiving Care at Community Health Centers

Childhood obesity has increased significantly in the United States. Racial subgroups are often grouped into categories in research, limiting our understanding of disparities. This study describes the prevalence of obesity among youth of diverse racial and ethnic backgrounds receiving care at community health centers (CHCs). This cross-sectional study describes the prevalence of elevated body mass index (BMI) (≥85th percentile) and obesity (≥95th percentile) in youth aged 9 to 19 years receiving care in CHCs in 2014. Multilevel logistic regression estimated the prevalence of elevated BMI and obesity by age, race/ethnicity, and sex. Among 64 925 youth, 40% had elevated BMI and 22% were obese. By race, obesity was lowest in the combined Asian/Pacific Islander category (13%); however, when subgroups were separated, the highest prevalence was among Native Hawaiians (33%) and Other Pacific Islanders (42%) and the lowest in Asians. By sex, Black females and Hispanic and Asian males were more likely to be obese. By age, the highest prevalence of obesity was among those aged 9 to 10 years (25%). Youth served by CHCs have a high prevalence of obesity, with significant differences observed by race, sex, and age. Combining race categories obscures disparities. The heterogeneity of communities warrants research that describes different populations to address obesity.

The prevalence and incidence rate of overweight and obesity among 2.5 million children and adolescents in Spain

INTRODUCTION AND OBJECTIVES

Childhood obesity trends are plateauing in Spain, but limited information is available about how they differ by region. This study assessed childhood and adolescent the prevalence and incidence of overweight and obesity from 2005 to 2017 across 8 Spanish regions.

METHODS

This longitudinal study used height and weight measurements from 2.5 million children aged 2 to 17 years to calculate overweight and obesity, according to the World Health Organization (WHO) guidelines. Data were obtained from The Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria, and the Information System for Research in Primary Care. Prevalence and incidence rates and trends from 2005 to 2017 were calculated and stratified by age, sex, and region.

RESULTS

The overall obesity prevalence increased in boys and girls from age 2 (0.8%; 95%CI, 0.8-0.9 in both sexes) until peaking at age 7 in girls (17.3%; 95%CI, 17.1-17.5) and age 9 in boys (24.1%; 95%CI 23.9-24.3). The highest and lowest obesity prevalences were observed in Murcia and Navarre. Overall obesity prevalence trends decreased from 2005 to 2017 in all age-sex groups and in most regions. Highest obesity incidence rates were found in children aged 6 to 7 years, (4.5 [4.5-4.5] and 3.5 [3.5-3.5] new obesity cases per 100 person-years in boys and girls, respectively). Boys had higher prevalence and incidence rates than girls across all regions. Overweight/obesity prevalence and incidence rates and their trends were consistently higher than the obesity results, although a similar pattern was observed across sex and age.

CONCLUSIONS

Overweight and obesity prevalence slightly decreased in Spain from 2005 to 2017, but regional, sex, and age differences persisted. Because incidence peaked around the age of 6 years, it may be important to begin health promotion programs at an early age.

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.

Small area estimation and childhood obesity surveillance using electronic health records

There is an urgent need for childhood surveillance systems to design, implement, and evaluate interventions at the local level. We estimated obesity prevalence for individuals aged 5–17 years using a southcentral Wisconsin EHR data repository, Public Health Information Exchange (PHINEX, 2007–2012). The prevalence estimates were calculated by aggregating the estimated probability of each individual being obese, which was obtained via a generalized linear mixed model. We incorporated the random effects at the area level into our model. A weighted procedure was employed to account for missingness in EHR data. A non-parametric kernel smoothing method was used to obtain the prevalence estimates for locations with no or little data (<20 individuals) from the EHR. These estimates were compared to results from newly available obesity atlas (2015–2016) developed from various EHRs with greater statewide representation. The mean of the zip code level obesity prevalence estimates for males and females aged 5–17 years is 16.2% (SD 2.72%); 17.9% (SD 2.14%) for males and 14.4% (SD 2.00%) for females. The results were comparable to the Wisconsin Health Atlas (WHA) estimates, a much larger dataset of local community EHRs in Wisconsin. On average, prevalence estimates were 2.12% lower in this process than the WHA estimates, with lower estimation occurring more frequently for zip codes without data in PHINEX. Using this approach, we can obtain estimates for local areas that lack EHRs data. Generally, lower prevalence estimates were produced for those locations not represented in the PHINEX database when compared to WHA estimates. This underscores the need to ensure that the reference EHRs database can be made sufficiently similar to the geographic areas where synthetic estimates are being created.

Simultaneous spatial smoothing and outlier detection using penalized regression, with application to childhood obesity surveillance from electronic health records

Electronic health records (EHRs) have become a platform for data-driven granular-level surveillance in recent years. In this paper, we make use of EHRs for early prevention of childhood obesity. The proposed method simultaneously provides smooth disease mapping and outlier information for obesity prevalence that are useful for raising public awareness and facilitating targeted intervention. More precisely, we consider a penalized multilevel generalized linear model. We decompose regional contribution into smooth and sparse signals, which are automatically identified by a combination of fusion and sparse penalties imposed on the likelihood function. In addition, we weigh the proposed likelihood to account for the missingness and potential nonrepresentativeness arising from the EHR data. We develop a novel alternating minimization algorithm, which is computationally efficient, easy to implement, and guarantees convergence. Simulation studies demonstrate superior performance of the proposed method. Finally, we apply our method to the University of Wisconsin Population Health Information Exchange database.

Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data

Background

Electronic health records (EHRs) are potentially important components in addressing pediatric obesity in clinical settings and at the population level. This work aims to identify temporal condition patterns surrounding obesity incidence in a large pediatric population that may inform clinical care and childhood obesity policy and prevention efforts.

Methods

EHR data from healthcare visits with an initial record of obesity incidence (index visit) from 2009 through 2016 at the Children’s Hospital of Philadelphia, and visits immediately before (pre-index) and after (post-index), were compared with a matched control population of patients with a healthy weight to characterize the prevalence of common diagnoses and condition trajectories. The study population consisted of 49,694 patients with pediatric obesity and their corresponding matched controls. The SPADE algorithm was used to identify common temporal condition patterns in the case population. McNemar’s test was used to assess the statistical significance of pattern prevalence differences between the case and control populations.

Results

SPADE identified 163 condition patterns that were present in at least 1% of cases; 80 were significantly more common among cases and 45 were significantly more common among controls (p < 0.05). Asthma and allergic rhinitis were strongly associated with childhood obesity incidence, particularly during the pre-index and index visits. Seven conditions were commonly diagnosed for cases exclusively during pre-index visits, including ear, nose, and throat disorders and gastroenteritis.

Conclusions

The novel application of SPADE on a large retrospective dataset revealed temporally dependent condition associations with obesity incidence. Allergic rhinitis and asthma had a particularly high prevalence during pre-index visits. These conditions, along with those exclusively observed during pre-index visits, may represent signals of future obesity. While causation cannot be inferred from these associations, the temporal condition patterns identified here represent hypotheses that can be investigated to determine causal relationships in future obesity research.

The effect of number of healthcare visits on study sample selection in electronic health record data

Introduction

Few studies have addressed how to select a study sample when using electronic health record (EHR) data.

Objective

To examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence.

Methods

Year 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medical College of Wisconsin, all regional tertiary health care systems including hospitals and clinics) was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence. In 2016, each of these health systems saw between 160,000 and 420,000 unique patients. Curated collections of ICD-9, ICD-10, and SNOMED codes (from CMS-approved electronic clinical quality measures) were used to define three diseases: acute myocardial infarction, asthma, and diabetic nephropathy).

Results

Across all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases.

Conclusion

In addition to providing thorough descriptions of case definitions, when using EHR data authors must carefully describe how a study sample is identified and report data for a range of sample definitions, including minimum number of visits, so that others can assess the sensitivity of reported results to sample definition in EHR data.

Key words

Electronic Health Records, Sampling Studies, Prevalence, Methods

Weight Loss for Patients With Obesity: An Analysis of Long-Term Electronic Health Record Data

BACKGROUND

Numerous studies have reported that losing as little as 5% of one's total body weight (TBW) can improve health, but no studies have used electronic health record data to examine long-term changes in weight, particularly for adults with severe obesity [body mass index (BMI) ≥35 kg/m].

OBJECTIVE

To measure long-term weight changes and examine their predictors for adults in a large academic health care system.

RESEARCH DESIGN

Observational study.

SUBJECTS

We included 59,816 patients aged 18-70 years who had at least 2 BMI measurements 5 years apart. Patients who were underweight, pregnant, diagnosed with cancer, or had undergone bariatric surgery were excluded.

MEASURES

Over a 5-year period: (1) ≥5% TBW loss; (2) weight loss into a nonobese BMI category (BMI <30 kg/m); and (3) predictors of %TBW change via quantile regression.

RESULTS

Of those with class 2 or 3 obesity, 24.2% and 27.8%, respectively, lost at least 5% TBW. Only 3.2% and 0.2% of patients with class 2 and 3 obesity, respectively, lost enough weight to attain a BMI <30 kg/m. In quantile regression, the median weight change for the population was a net gain of 2.5% TBW.

CONCLUSIONS

Although adults with severe obesity were more likely to lose at least 5% TBW compared with overweight patients and patients with class 1 obesity, sufficient weight loss to attain a nonobese weight class was very uncommon. The pattern of ongoing weight gain found in our study population requires solutions at societal and health systems levels.

Population-Based Registry Linkages to Improve Validity of Electronic Health Record-Based Cancer Research

BACKGROUND

There is tremendous potential to leverage the value gained from integrating electronic health records (EHR) and population-based cancer registry data for research. Registries provide diagnosis details, tumor characteristics, and treatment summaries, while EHRs contain rich clinical detail. A carefully conducted cancer registry linkage may also be used to improve the internal and external validity of inferences made from EHR-based studies.

METHODS

We linked the EHRs of a large, multispecialty, mixed-payer health care system with the statewide cancer registry and assessed the validity of our linked population. For internal validity, we identify patients that might be "missed" in a linkage, threatening the internal validity of an EHR study population. For generalizability, we compared linked cases with all other cancer patients in the 22-county EHR catchment region.

RESULTS

From an EHR population of 4.5 million, we identified 306,554 patients with cancer, 26% of the catchment region patients with cancer; 22.7% of linked patients were diagnosed with cancer after they migrated away from our health care system highlighting an advantage of system-wide linkage. We observed demographic differences between EHR patients and non-EHR patients in the surrounding region and demonstrated use of selection probabilities with model-based standardization to improve generalizability.

CONCLUSIONS

Our experiences set the foundation to encourage and inform researchers interested in working with EHRs for cancer research as well as provide context for leveraging linkages to assess and improve validity and generalizability.

IMPACT

Researchers conducting linkages may benefit from considering one or more of these approaches to establish and evaluate the validity of their EHR-based populations.See all articles in this CEBP Focus section, "Modernizing Population Science."

Use of electronic medical records to conduct surveillance of malaria among Peace Corps volunteers

Objective

The Peace Corps’ disease surveillance for Peace Corps Volunteers (PCVs) was incorporated into an electronic medical records (EMR) system in 2015. We evaluated this EMR-based surveillance system, focusing particularly on malaria as it is deadly but preventable.

Materials and Methods

In 2016, we administered a survey to Peace Corps Medical Officers (PCMOs), who manage PCVs’ medical care, and semistructured phone interviews to headquarters staff. We assessed the structure of the surveillance system and its utility to stakeholders, evaluated surveillance case definitions for malaria, and compared clinical information in the EMR for malaria cases captured by surveillance during the first half of 2016.

Results

Of 131 PCMOs, 77 (59%) completed the survey. Of 53 respondents in malaria-endemic nations, 98% believed most PCVs contact them about possible malaria. Of 134 cases with a malaria clinical diagnosis in the EMR between January and August 2016, 58 (43% sensitivity) were reported to the surveillance system by PCMOs. The remaining cases in the surveillance system were added during data cleaning, which is time-intensive. Among the 48 malaria cases identified by surveillance between January and June 2016, positive predictive value was 67%.

Discussion

Areas for improvement include streamlining PCMO documentation, refining case definitions, and improving data quality. With such improvements, surveillance data can be used to inform epidemiological analysis, clinical care, health education, and policy.

Conclusion

The EMR is an important tool for malaria surveillance among PCVs and, with the refinements mentioned, could serve as a framework for other multinational organizations to monitor their staff.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Assessing the Potential for Integrating Routine Data Collection on Complementary Feeding to Child Health Visits: A Mixed-Methods Study

There is no routine data collection in the UK on infant dietary diversity during the transition to solid foods, and health visitors (HVs) (nurses or midwives with specialist training in children and family health) have the potential to play a key role in nutrition surveillance. We aimed to assess items for inclusion in routine data collection, their suitability for collecting informative data, and acceptability among HVs. A mixed-methods study was undertaken using: (i) an online survey testing potential questionnaire items among parents/caregivers, (ii) questionnaire redevelopment in collaboration with community staff, and (iii) a survey pilot by HVs followed by qualitative data collection. Preliminary online questionnaires (n = 122) were collected to identify useful items on dietary diversity. Items on repeated exposure to foods, aversive feeding behaviors, flavor categories, and sugar intake were selected to correspond to nutrition recommendations, and be compatible with electronic records via tablet. HVs surveyed 187 parents of infants aged 12 months. Semi-structured interviews indicated that HVs found the questionnaire comparable with standard nutrition conversations, which prompted helpful discussions, but questions on eating behavior did not prompt such useful discussions and, in some cases, caused confusion about what was 'normal.' Lack of time among HVs, internet connectivity issues, and fear of losing rapport with parents were barriers to completing electronic questionnaires, with 91% submitted by paper. Routine nutrition data collection via child health records seems feasible and could inform quality improvement projects.

Not so implausible: impact of longitudinal assessment of implausible anthropometric measures on obesity prevalence and weight change in children and adolescents

PURPOSE

Implausible anthropometric measures are typically identified using population outlier definitions, conflating implausible and extreme measures. We determined the impact of a longitudinal outlier approach on prevalence of body mass index (BMI) categories and mean change in anthropometric measures in pediatric electronic health record data.

METHODS

We examined 996,131 observations from 147,375 children (10-18 years) in the ADVANCE Clinical Data Research Network, a national network of community health centers. Sex-stratified, mixed effects, linear spline regression modeled weight, height, and BMI as a function of age. Longitudinal outliers were defined as observations with studentized residual greater than |6|; population outliers were defined by Centers for Disease Control-defined z-score thresholds.

RESULTS

At least 99.7% of anthropometric measures were not extreme by longitudinal or population definitions (agreement ≥ 0.995). BMI category prevalence after excluding longitudinal or population outliers differed by less than 0.1%. Among children greater than 85th percentile at baseline, annual mean changes in anthropometric measures were larger in data that excluded longitudinal (girls: 1.24 inches, 12.39 pounds, 1.53 kg/m²; boys: 2.34, 14.08, 1.07) versus population outliers (girls: 0.61 inches, 8.22 pounds, 0.75 kg/m²; boys: 1.53, 11.61, 0.48).

CONCLUSIONS

Longitudinal outlier methods may reduce underestimation of anthropometric change in children with elevated baseline values.

The use of Electronic Health Records to Support Population Health: A Systematic Review of the Literature

Electronic health records (EHRs) have emerged among health information technology as "meaningful use" to improve the quality and efficiency of healthcare, and health disparities in population health. In other instances, they have also shown lack of interoperability, functionality and many medical errors. With proper implementation and training, are electronic health records a viable source in managing population health? The primary objective of this systematic review is to assess the relationship of electronic health records' use on population health through the identification and analysis of facilitators and barriers to its adoption for this purpose. Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE (PubMed), 10/02/2012-10/02/2017, core clinical/academic journals, MEDLINE full text, English only, human species and evaluated the articles that were germane to our research objective. Each article was analyzed by multiple reviewers. Group members recognized common facilitators and barriers associated with EHRs effect on population health. A final list of articles was selected by the group after three consensus meetings (n = 55). Among a total of 26 factors identified, 63% (147/232) of those were facilitators and 37% (85/232) barriers. About 70% of the facilitators consisted of productivity/efficiency in EHRs occurring 33 times, increased quality and data management each occurring 19 times, surveillance occurring 17 times, and preventative care occurring 15 times. About 70% of the barriers consisted of missing data occurring 24 times, no standards (interoperability) occurring 13 times, productivity loss occurring 12 times, and technology too complex occurring 10 times. The analysis identified more facilitators than barriers to the use of the EHR to support public health. Wider adoption of the EHR and more comprehensive standards for interoperability will only enhance the ability for the EHR to support this important area of surveillance and disease prevention. This review identifies more facilitators than barriers to using the EHR to support public health, which implies a certain level of usability and acceptance to use the EHR in this manner. The public-health industry should combine their efforts with the interoperability projects to make the EHR both fully adopted and fully interoperable. This will greatly increase the availability, accuracy, and comprehensiveness of data across the country, which will enhance benchmarking and disease surveillance/prevention capabilities.

Circannual variation in relative weight of children 5 to 16 years of age

BACKGROUND

Summer weight gain in children has been reported; however, this is usually based on two time points. Our objective was to investigate monthly variation in weight status.

METHODS

Cross-sectional, de-identified health records including height, weight and demographics, collected between 2007 and 2012 from South Central Wisconsin in 70 531 children age 5-16 years were analysed. The monthly averages in body mass index (BMI) z-score were analysed cross-sectionally followed by a paired analysis for a subset with one visit each during school and summer months.

RESULTS

BMI z-scores during the summer months (June-August) were lower than values during the school year (September-May). Of note, there was a rapid decrease in BMI z-scores from May to June, with June BMI z-score values being 0.065 units less (95% CI 0.046-0.085) than those in May, little change from June to August and a rapid increase between the August and September BMI z-scores.

CONCLUSION

The monthly pattern does not fully agree with previous two-point school-based studies. Results raise concern that the use of two time point measures of BMIs (early fall and late spring) is suboptimal for evaluation of circannual variation. We suggest that future evaluation of the effect of school-based or summer interventions utilizes additional measures in those periods so that a seasonal analysis can be performed.

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.

Addressing Bias in Electronic Health Record-Based Surveillance of Cardiovascular Disease Risk: Finding the Signal Through the Noise

PURPOSE OF REVIEW

Use of the electronic health record (EHR) for CVD surveillance is increasingly common. However, these data can introduce systematic error that influences the internal and external validity of study findings. We reviewed recent literature on EHR-based studies of CVD risk to summarize the most common types of bias that arise. Subsequently, we recommend strategies informed by work from others as well as our own to reduce the impact of these biases in future research.

RECENT FINDINGS

Systematic error, or bias, is a concern in all observational research including EHR-based studies of CVD risk surveillance. Patients captured in an EHR system may not be representative of the general population, due to issues such as informed presence bias, perceptions about the healthcare system that influence entry, and access to health services. Further, the EHR may contain inaccurate information or be missing key data points of interest due to loss to follow-up or over-diagnosis bias. Several strategies, including implementation of unique patient identifiers, adoption of standardized rules for inclusion/exclusion criteria, statistical procedures for data harmonization and analysis, and incorporation of patient-reported data have been used to reduce the impact of these biases.

SUMMARY

EHR data provide an opportunity to monitor and characterize CVD risk in populations. However, understanding the biases that arise from EHR datasets is instrumental in planning epidemiological studies and interpreting study findings. Strategies to reduce the impact of bias in the context of EHR data can increase the quality and utility of these data.

Electronic Health Record Data Versus the National Health and Nutrition Examination Survey (NHANES): A Comparison of Overweight and Obesity Rates

BACKGROUND

Estimating population-level obesity rates is important for informing policy and targeting treatment. The current gold standard for obesity measurement in the United States-the National Health and Nutrition Examination Survey (NHANES)-samples <0.1% of the population and does not target state-level or health system-level measurement.

OBJECTIVE

To assess the feasibility of using body mass index (BMI) data from the electronic health record (EHR) to assess rates of overweight and obesity and compare these rates to national NHANES estimates.

RESEARCH DESIGN

Using outpatient data from 42 clinics, we studied 388,762 patients in a large health system with at least 1 primary care visit in 2011-2012.

MEASURES

We compared crude and adjusted overweight and obesity rates by age category and ethnicity (white, black, Hispanic, Other) between EHR and NHANES participants. Adjusted overweight (BMI≥25) and obesity rates were calculated by a 2-step process. Step 1 accounted for missing BMI data using inverse probability weighting, whereas step 2 included a poststratification correction to adjust the EHR population to a nationally representative sample.

RESULTS

Adjusted rates of obesity (BMI≥30) for EHR patients were 37.3% [95% confidence interval (95% CI), 37.1-37.5] compared with 35.1% (95% CI, 32.3-38.1) for NHANES patients. Among the 16 different obesity class, ethnicity, and sex strata that were compared between EHR and NHANES patients, 14 (87.5%) contained similar obesity estimates (ie, overlapping 95% CIs).

CONCLUSIONS

EHRs may be an ideal tool for identifying and targeting patients with obesity for implementation of public health and/or individual level interventions.

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.

Evaluating the Use of Electronic Health Records for Type 2 Diabetes Surveillance in 2 California Counties, 2010-2014

OBJECTIVES

Electronic health records (EHRs) and electronic laboratory records (ELRs) are increasingly seen as a rich source of data for performing public health surveillance activities and monitoring community health status. Their potential for surveillance of chronic illness, however, may be underused. Our objectives were to (1) evaluate the use of EHRs and ELRs for diabetes surveillance in 2 California counties and (2) examine disparities in diabetes prevalence by geography, income, and race/ethnicity.

METHODS

We obtained data on a clinical diagnosis of diabetes and hemoglobin A1c (HbA1c) test results for adult members of Kaiser Permanente Northern California living in Contra Costa County or Solano County at any time during 2010-2014. We evaluated the validity of using HbA1c test results to determine diabetes prevalence, using clinical diagnoses as a gold standard. We estimated disparities in diabetes prevalence by combining HbA1c test results with US Census data on income, race, and ethnicity.

RESULTS

When compared with a clinical diagnosis of diabetes, data on a patient's 5-year maximum HbA1c value ≥6.5% yielded the best combination of sensitivity (87.4%) and specificity (99.2%). The prevalence of 5-year maximum HbA1c ≥6.5% decreased with increasing median family income and increased with greater proportions of residents who were either non-Hispanic black or Hispanic.

CONCLUSIONS

Timely diabetes surveillance data from ELRs can be used to document disparities, target interventions, and evaluate changes in population health. ELR data may be easier to access than a patient's entire EHR, but outcome metric validation with diabetes diagnoses would need to be ongoing. Future research should validate ELR and EHR data across multiple providers.

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data

INTRODUCTION

Electronic health records (EHRs) offer potential for population health surveillance but EHR-based surveillance measures require validation prior to use. We assessed the validity of obesity, smoking, depression, and influenza vaccination indicators from a new EHR surveillance system, the New York City (NYC) Macroscope. This report is the second in a 3-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the infrastructure underlying the NYC Macroscope; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. This second report, which addresses concerns related to sampling bias and data quality, describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods described in this report to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.

METHODS

NYC Macroscope prevalence estimates, overall and stratified by sex and age group, were compared to reference survey estimates for adult New Yorkers who reported visiting a doctor in the past year. Agreement was evaluated against 5 a priori criteria. Sensitivity and specificity were assessed by examining individual EHR records in a subsample of 48 survey participants.

RESULTS

Among adult New Yorkers in care, the NYC Macroscope prevalence estimate for smoking (15.2%) fell between estimates from NYC HANES (17.7 %) and CHS (14.9%) and met all 5 a priori criteria. The NYC Macroscope obesity prevalence estimate (27.8%) also fell between the NYC HANES (31.3%) and CHS (24.7%) estimates, but met only 3 a priori criteria. Sensitivity and specificity exceeded 0.90 for both the smoking and obesity indicators. The NYC Macroscope estimates of depression and influenza vaccination prevalence were more than 10 percentage points lower than the estimates from either reference survey. While specificity was > 0.90 for both of these indicators, sensitivity was < 0.70.

DISCUSSION

Through this work we have demonstrated that EHR data from a convenience sample of providers can produce acceptable estimates of smoking and obesity prevalence among adult New Yorkers in care; gained a better understanding of the challenges involved in estimating depression prevalence from EHRs; and identified areas for additional research regarding estimation of influenza vaccination prevalence. We have also shared lessons learned about how EHR indicators should be constructed and offer methodologic suggestions for validating them.

CONCLUSIONS

This work adds to a rapidly emerging body of literature about how to define, collect and interpret EHR-based surveillance measures and may help guide other jurisdictions.

Design of the New York City Macroscope: Innovations in Population Health Surveillance Using Electronic Health Records

Introduction:

Electronic health records (EHRs) have the potential to offer real-time, inexpensive standardized health data about chronic health conditions. Despite rapid expansion, EHR data evaluations for chronic disease surveillance have been limited. We present design and methods for the New York City (NYC) Macroscope, an EHR-based chronic disease surveillance system.

This methods report is the first in a three part series describing the development and validation of the NYC Macroscope. This report describes in detail the infrastructure underlying the NYC Macroscope; indicator definitions; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. The second report describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia.

Methods:

We designed the NYC Macroscope for comparison to a local “gold standard,” the 2013–14 NYC Health and Nutrition Examination Survey, and the telephonic 2013 Community Health Survey. NYC Macroscope indicators covered prevalence, treatment, and control of diabetes, hypertension, and hyperlipidemia; and prevalence of influenza vaccination, obesity, depression and smoking. Indicators were stratified by age, sex, and neighborhood poverty, and weighted to the in-care NYC population and limited to primary care patients. Indicator queries were distributed to a virtual network of primary care practices; 392 practices and 716,076 adult patients were retained in the final sample.

Findings:

The NYC Macroscope covered 10% of primary care providers and 15% of all adult patients in NYC in 2013 (8–47% of patients by neighborhood). Data completeness varied by domain from 98% for blood pressure among patients with hypertension to 33% for depression screening.

Discussion:

Design and validation efforts undertaken by NYC are described here to provide one potential blueprint for leveraging EHRs for population health monitoring. To replicate a model like NYC Macroscope, jurisdictions should establish buy-in; build informatics capacity; use standard, simple case defnitions; establish documentation quality thresholds; restrict to primary care providers; and weight the sample to a target population.

Prevalence and Predictors of Unhealthy Weight Gain in Pregnancy

IMPORTANCE

Weight gain during pregnancy affects obesity risk in offspring.

OBJECTIVE

To assess weight gain among UW Health prenatal patients and to identify predictors of unhealthy gestational weight gain.

METHODS

Retrospective cohort study of women delivering at UW Health during 2007-2012. Data are from the UW eHealth Public Health Information Exchange (PHINEX) project. The proportion of women with excess and insufficient (ie, unhealthy) gestational weight gain was computed based on 2009 Institute of Medicine guidelines. Multivariable logistic regression was used to identify risk factors associated with excess and insufficient gestational weight gain.

RESULTS

Gestational weight gain of 7,385 women was analyzed. Fewer than 30% of prenatal patients gained weight in accordance with Institute of Medicine guidelines. Over 50% of women gained excess weight and 20% gained insufficient weight during pregnancy. Pre-pregnancy weight and smoking status predicted excess weight gain. Maternal age, race/ethnicity, smoking status, and having Medicaid insurance predicted insufficient weight gain.

CONCLUSIONS AND RELEVANCE

Unhealthy weight gain during pregnancy is the norm for Wisconsin women. Clinical and community interventions that promote healthy weight gain during pregnancy will not only improve the health of mothers, but also will reduce the risk of obesity in the next generation.

Using Electronic Health Records to Examine Disease Risk in Small Populations: Obesity Among American Indian Children, Wisconsin, 2007-2012

Introduction

Tribe-based or reservation-based data consistently show disproportionately high obesity rates among American Indian children, but little is known about the approximately 75% of American Indian children living off-reservation. We examined obesity among American Indian children seeking care off-reservation by using a database of de-identified electronic health records linked to community-level census variables.

Methods

Data from electronic health records from American Indian children and a reference sample of non-Hispanic white children collected from 2007 through 2012 were abstracted to determine obesity prevalence. Related community-level and individual-level risk factors (eg, economic hardship, demographics) were examined using logistic regression.

Results

The obesity rate for American Indian children (n = 1,482) was double the rate among non-Hispanic white children (n = 81,042) (20.0% vs 10.6%, P < .001). American Indian children were less likely to have had a well-child visit (55.9% vs 67.1%, P < .001) during which body mass index (BMI) was measured, which may partially explain why BMI was more likely to be missing from American Indian records (18.3% vs 14.6%, P < .001). Logistic regression demonstrated significantly increased obesity risk among American Indian children (odds ratio, 1.8; 95% confidence interval, 1.6–2.1) independent of age, sex, economic hardship, insurance status, and geographic designation.

Conclusion

An electronic health record data set demonstrated high obesity rates for nonreservation-based American Indian children, rates that had not been previously assessed. This low-cost method may be used for assessing health risk for other understudied populations and to plan and evaluate targeted interventions.

Suicidal Ideation and Behaviours Among Adolescents Receiving Bariatric Surgery: A Case-Control Study

OBJECTIVE

This study examined the prevalence and correlates of suicidal ideation and behaviour (SI/B) among adolescents receiving bariatric surgery.

METHOD

Charts of 206 adolescents receiving bariatric surgery were reviewed. Cases with SI/B (current/lifetime reported at baseline or event occurring in the programme n = 31, 15%) were case matched on gender, age and surgery type to 31 adolescents reporting current or past psychiatric treatment and 31 adolescents denying lifetime SI/B or psychiatric treatment.

RESULTS

Before surgery, adolescents with SI/B reported significantly lower total levels of health-related quality of life (p = 0.01) and greater depressive symptoms (p = 0.004) in comparison with candidates who never received psychiatric treatment. No significant differences were found between groups for the change in depressive symptoms or body mass index following surgery.

CONCLUSIONS

As in studies of adults, a notable subset of adolescents receiving bariatric surgery indicated pre-operative or post-operative SI/B. It is critical that clinicians evaluate and monitor adolescent patients undergoing bariatric surgery for risk of SI/B.