Using electronic health record data for environmental and place based population health research: a systematic review

Application of Spatial Analysis on Electronic Health Records to Characterize Patient Phenotypes: Systematic Review

BACKGROUND

Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

OBJECTIVE

This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes.

METHODS

We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains.

RESULTS

A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited.

CONCLUSIONS

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.

A review of geospatial exposure models and approaches for health data integration

BACKGROUND

Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.

OBJECTIVE

Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.

METHODS

We conduct a literature review and synthesis.

RESULTS

First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.

Traveling Together: A Road Map for Researching Neighborhood Effects on Population Health and Health Inequities

As evidence of the relationship between place and health mounts, more epidemiologists and clinical science researchers are becoming interested in incorporating place-based measures and analyses into their examination of population health and health inequities. Given the extensive literature on place and health, it can be challenging for researchers new to this area to develop neighborhood-effects research questions and apply the appropriate measures and methods. This paper provides a road map for guiding health researchers through the conceptual and methodological stages of incorporating various dimensions of place into their quantitative health research. Synthesizing across reviews, commentaries, and empirical investigations, the road map consists of 4 broad stages for considering place and health: 1) why?: articulating the motivation for assessing place and health and grounding the motivation in theory; 2) what?: identifying the relevant place-based characteristics and specifying their link to health to build a conceptual framework; 3) how?: determining how to operationalize the conceptual framework by defining, measuring, and assessing place-based characteristics and quantifying their effect on health; and 4) now what?: discussing the implications of neighborhood research findings for future research, policy, and practice. This road map supports efforts to develop conceptually and analytically rigorous neighborhood research projects.

A Mobile Health Application Using Geolocation for Behavioral Activity Tracking

The increasing popularity of mHealth presents an opportunity for collecting rich datasets using mobile phone applications (apps). Our health-monitoring mobile application uses motion detection to track an individual's physical activity and location. The data collected are used to improve health outcomes, such as reducing the risk of chronic diseases and promoting healthier lifestyles through analyzing physical activity patterns. Using smartphone motion detection sensors and GPS receivers, we implemented an energy-efficient tracking algorithm that captures user locations whenever they are in motion. To ensure security and efficiency in data collection and storage, encryption algorithms are used with serverless and scalable cloud storage design. The database schema is designed around Mobile Advertising ID (MAID) as a unique identifier for each device, allowing for accurate tracking and high data quality. Our application uses Google's Activity Recognition Application Programming Interface (API) on Android OS or geofencing and motion sensors on iOS to track most smartphones available. In addition, our app leverages blockchain and traditional payments to streamline the compensations and has an intuitive user interface to encourage participation in research. The mobile tracking app was tested for 20 days on an iPhone 14 Pro Max, finding that it accurately captured location during movement and promptly resumed tracking after inactivity periods, while consuming a low percentage of battery life while running in the background.

Population-level SDOH and Pediatric Asthma Health Care Utilization: A Systematic Review

CONTEXT

Spatial analysis is a population health methodology that can determine geographic distributions of asthma outcomes and examine their relationship to place-based social determinants of health (SDOH).

OBJECTIVES

To systematically review US-based studies analyzing associations between SDOH and asthma health care utilization by geographic entities.

DATA SOURCES

Pubmed, Medline, Web of Science, Scopus, and Cumulative Index to Nursing and Allied Health Literature.

STUDY SELECTION

Empirical, observational US-based studies were included if (1) outcomes included asthma-related emergency department visits or revisits, and hospitalizations or rehospitalizations; (2) exposures were ≥1 SDOH described by the Healthy People (HP) SDOH framework; (3) analysis occurred at the population-level using a geographic entity (eg, census-tract); (4) results were reported separately for children ≤18 years.

DATA EXTRACTION

Two reviewers collected data on study information, demographics, geographic entities, SDOH exposures, and asthma outcomes. We used the HP SDOH framework's 5 domains to organize and synthesize study findings.

RESULTS

The initial search identified 815 studies; 40 met inclusion criteria. Zip-code tabulation areas (n = 16) and census-tracts (n = 9) were frequently used geographic entities. Ten SDOH were evaluated across all HP domains. Most studies (n = 37) found significant associations between ≥1 SDOH and asthma health care utilization. Poverty and environmental conditions were the most often studied SDOH. Eight SDOH-poverty, higher education enrollment, health care access, primary care access, discrimination, environmental conditions, housing quality, and crime - had consistent significant associations with asthma health care utilization.

CONCLUSIONS

Population-level SDOH are associated with asthma health care utilization when evaluated by geographic entities. Future work using similar methodology may improve this research's quality and utility.

Impacts of Individual Patient Language and Neighborhood Ethnic Enclave on COVID-19 Test Positivity Among Hispanic/Latinx Patients in San Francisco

BACKGROUND

Given the known disparities in COVID-19 within the Hispanic/Latinx community, we sought to examine the interaction between individual-level and neighborhood-level social determinants of health using linked electronic health record data.

METHODS

We examined electronic health record data linked to neighborhood data among Hispanic/Latinx patients tested for COVID-19 between March 1, 2020, and February 28, 2021, from 2 large health care systems in San Francisco. Hispanic/Latinx ethnic enclave is measured using an index of census-tract level indicators of ethnicity, nativity, and language. Multilevel logistic regression models examined associations between ethnic enclave and COVID-19 positivity (COVID-19+), adjusting for patient-level sociodemographic and clinical characteristics and health system. Cross-level interactions were used to test whether associations between ethnic enclave and COVID-19+ differed by patient language preference.

RESULTS

Among 26,871 patients, mean age was 37 years, 56% had Spanish-language preference, and 21% were COVID-19+. In unadjusted models, patients living in the highest versus lowest Hispanic/Latinx enclave had 3.2 higher odds of COVID-19+ (95% CI, 2.45-4.24). Adjusted, the relationship between ethnic enclave and COVID-19+ was attenuated, but not eliminated (odds ratio: 1.4; 95% CI, 1.13-1.17). Our results demonstrated a significant cross-level interaction, such that the influence of ethnic enclave was modified by patient language preference. For individuals with Spanish-language preference, risk of COVID-19+ was high regardless of neighborhood context, whereas for those with English preference, neighborhood ethnic enclave more than doubled the odds of infection.

CONCLUSIONS

Findings suggest that a multilevel and intersectional approach to the study of COVID-19 inequities may illuminate dimensions of health inequity that affect marginalized communities and offer insights for targeted clinical and community-based interventions.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

BACKGROUND

Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.

METHODS

This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.

RESULTS

Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.

CONCLUSIONS

This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Mixed‐methods evaluation of a multifaceted heart failure intervention in general practice: the OSCAR‐HF pilot study

AIMS

Heart failure (HF) is an important health problem for which multidisciplinary care is recommended, yet few studies involve primary care practitioners in the multidisciplinary management of HF. We set up a multifaceted prospective observational trial, OSCAR-HF, piloting audit and feedback, natriuretic peptide testing at the point of care, and the assistance of a specialist HF nurse in primary care. The aim was to optimize HF care in general practice.

METHODS AND RESULTS

This is an analysis at 6 month follow-up of the study interventions of the OSCAR-HF pilot study, a nonrandomized, noncontrolled prospective observational trial conducted in eight Belgian general practices [51 general practitioners (GPs)]. Patients who were assessed by their GP to have HF constituted the OSCAR-HF study population. We used descriptive statistics and mixed-effects modelling for the quantitative analysis and thematic analysis of the focus group interviews. There was a 10.2% increase in the registered HF population after 6 months of follow-up (n = 593) compared with baseline (n = 538) and a 27% increase in objectified HF diagnoses (baseline n = 359 to 456 at T6 M). Natriuretic peptide testing (with or without referral) accounted for 54% (n = 60/111) of the newly registered HF diagnoses. There was no difference in the proportion of patients with HF with reduced ejection fraction who received their target dosage of renin-angiotensin-aldosterone system inhibitors or beta-blockers at 6 months compared with baseline (P = 0.9). Patients who received an HF nurse intervention (n = 53) had significantly worse quality of life at baseline [difference in Minnesota Living with Heart Failure Questionnaire (MLHFQ) score 9.2 points; 95% confidence interval (CI) 4.0, 14] and had a significantly greater improvement in quality-of-life scores at the 6 month follow-up [change in MLHFQ score -9.8 points; 95% CI -15, -4.5] than patients without an HF nurse intervention. GPs found audit and feedback valuable but time intensive. Natriuretic peptides were useful, but the point-of-care test was impractical, and the assistance of an HF nurse was a useful addition to routine HF care.

CONCLUSIONS

The use of audit and feedback combined with natriuretic peptide testing was a successful strategy to increase the number of registered and objectified HF diagnoses at 6 months. GPs and HF nurses selected patients with worse quality-of-life scores at baseline for the HF nurse intervention, which led to a significantly greater improvement in quality-of-life scores at the 6 month follow-up compared with patients without an HF nurse intervention. The interventions were deemed feasible and useful by the participating GPs with some specific remarks that can be used for optimization.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT02905786), registered on 14 September 2016 at https://clinicaltrials.gov/.

In-home environmental exposures predicted from geospatial characteristics of the built environment and electronic health records of children with asthma

PURPOSE

Children may be exposed to numerous in-home environmental exposures (IHEE) that trigger asthma exacerbations. Spatially linking social and environmental exposures to electronic health records (EHR) can aid exposure assessment, epidemiology, and clinical treatment, but EHR data on exposures are missing for many children with asthma. To address the issue, we predicted presence of indoor asthma trigger allergens, and estimated effects of their key geospatial predictors.

METHODS

Our study samples were comprised of children with asthma who provided self-reported IHEE data in EHR at a safety-net hospital in New England during 2004-2015. We used an ensemble machine learning algorithm and 86 multilevel features (e.g., individual, housing, neighborhood) to predict presence of cockroaches, rodents (mice or rats), mold, and bedroom carpeting/rugs in homes. We reduced dimensionality via elastic net regression and estimated effects by the G-computation causal inference method.

RESULTS

Our models reasonably predicted presence of cockroaches (area under receiver operating curves [AUC] = 0.65), rodents (AUC = 0.64), and bedroom carpeting/rugs (AUC = 0.64), but not mold (AUC = 0.54). In models adjusted for confounders, higher average household sizes in census tracts were associated with more reports of pests (cockroaches and rodents). Tax-exempt parcels were associated with more reports of cockroaches in homes. Living in a White-segregated neighborhood was linked with lower reported rodent presence, and mixed residential/commercial housing and newer buildings were associated with more reports of bedroom carpeting/rugs in bedrooms.

CONCLUSIONS

We innovatively applied a machine learning and causal inference mixture methodology to detail IHEE among children with asthma using EHR and geospatial data, which could have wide applicability and utility.

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).

Geocoding precision of birth records from 2008 to 2017 in Kentucky, USA

Maternal address information captured on birth records is increasingly used to estimate residential environmental exposures during pregnancy. However, there has been limited assessment of the geocoding precision of birth records, particularly since the adoption of the 2003 standard birth certificate in 2015. To address this gap, this study evaluated the geocoding precision of live and stillbirth records of Kentucky residents over ten years, from 2008 through 2017. This study summarized the demographic characteristics of imprecisely geocoded records and, using a bivariate logistic regression, identified covariates associated with poor geocoding precision among three population density designations-metro, non-metro, and rural. We found that in metro areas, after adjusting for area deprivation, education, and the race, age and education of both parents, records for Black mothers had 48% lower odds of imprecise geocoding (aOR=0.52, 95% CI: 0.48, 0.56), while Black women in rural areas had 96% higher odds of imprecise geocoding (aOr=1.96, 95% CI: 1.68, 2.28). This study also found that over the study period, rural and non-metro areas began with a high proportion of imprecisely geocoded records (38% in rural areas, 19% in non-metro), but both experienced an 8% decline in imprecisely geocoded records over the study period (aOr=0.92, 95% CI: 0.92, 0.94). This study shows that, while geocoding precision has improved in Kentucky, further work is needed to improve geocoding in rural areas and address racial and ethnic disparities.

Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review

BACKGROUND

With the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care.

OBJECTIVE

The aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care.

METHODS

Three databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes.

RESULTS

Of the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases.

CONCLUSIONS

Artificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.

Examining Neighborhood Socioeconomic Status as a Mediator of Racial/Ethnic Disparities in Hypertension Control Across Two San Francisco Health Systems

BACKGROUND

A contextual understanding of hypertension control can inform population health management strategies to mitigate cardiovascular disease events. This retrospective cohort study links neighborhood-level data with patients' health records to describe racial/ethnic differences in uncontrolled hypertension and determine if and to what extent these differences are mediated by neighborhood socioeconomic status (nSES).

METHODS

We conducted a mediation analysis using a sample of patients with hypertension from 2 health care delivery systems in San Francisco over 2 years (n=47 031). We used generalized structural equation modeling, adjusted for age, sex, and health care system, to estimate the contribution of nSES to disparities in uncontrolled hypertension between White patients and Black, Hispanic/Latino, and Asian patients, respectively. Sensitivity analysis removed adjustment for health care system.

RESULTS

Over half the cohort (62%) experienced uncontrolled hypertension during the study period. Racial/ethnic groups showed substantial differences in prevalence of uncontrolled hypertension and distribution of nSES quintiles. Compared with White patients, Black, and Hispanic/Latino patients had higher adjusted odds of uncontrolled hypertension: odds ratio, 1.79 [95% CI, 1.67-1.91] and odds ratio, 1.38 [95% CI, 1.29-1.47], respectively and nSES accounted for 7% of the disparity in both comparisons. Asian patients had slightly lower adjusted odds of uncontrolled hypertension when compared with White patients: odds ratio, 0.95 [95% CI, 0.89-0.99] and the mediating effect of nSES did not change the direction of the relationship. Sensitivity analysis increased the proportion mediated by nSES to 11% between Black and White patients and 13% between Hispanic/Latino and White patients, but did not influence differences between Asian and White patients.

CONCLUSIONS

Among patients with hypertension in this study, nSES mediated a small proportion of racial/ethnic disparities in uncontrolled hypertension. Population health management strategies may be most effective by focusing on additional structural and interpersonal pathways such as racism and discrimination in health care settings.

Social Epidemiology: Past, Present, and Future

In a context where epidemiologic research has been heavily influenced by a biomedical and individualistic approach, the naming of “social epidemiology” allowed explicit emphasis on the social production of disease as a powerful explanatory paradigm and as critically important for interventions to improve population health. This review briefly highlights key substantive areas of focus in social epidemiology over the past 30 years, reflects on major advances and insights, and identifies challenges and possible future directions. Future opportunities for social epidemiology include grounding research in theoretically based and systemic conceptual models of the fundamental social drivers of health; implementing a scientifically rigorous yet realistic approach to drawing conclusions about social causes; using complementary methods to generate valid explanations and identify effective actions; leveraging the power of harmonization, replication, and big data; extending interdisciplinarity and diversity; advancing emerging critical approaches to understanding the health impacts of systemic racism and its policy implications; going global; and embracing a broad approach to generating socially useful research.

Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Evaluation of the ASSIGN open-source deterministic address-matching algorithm for allocating unique property reference numbers to general practitioner-recorded patient addresses

INTRODUCTION

Linking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers.

OBJECTIVES

To describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses.

METHODS

Best practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables.

RESULTS

We found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population.The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations.

CONCLUSIONS

ASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other address-based datasets including those with information relevant to the wider determinants of health.

Using Electronic Medical Records to Identify Potentially Eligible Study Subjects for Lung Cancer Screening with Biomarkers

Lung cancer screening trials using low-dose computed tomography (LDCT) show reduced late-stage diagnosis and mortality rates. These trials have identified high-risk groups that would benefit from screening. However, these sub-populations can be difficult to access and retain in trials. Implementation of national screening programmes further suggests that there is poor uptake in eligible populations. A new approach to participant selection may be more effective. Electronic medical records (EMRs) are a viable alternative to population-based or health registries, as they contain detailed clinical and demographic information. Trials have identified that e-screening using EMRs has improved trial retention and eligible subject identification. As such, this paper argues for greater use of EMRs in trial recruitment and screening programmes. Moreover, this opinion paper explores the current issues in and approaches to lung cancer screening, whether records can be used to identify eligible subjects for screening and the challenges that researchers face when using EMR data.

Gendering Neighbourhood Marginalization Metrics in Mental Health Services Research: A Cross-Sectional Exploration of a Rural and Small Urban Population

Background: Little is known about the extent to which socioenvironmental characteristics may influence mental health outcomes in smaller population centres or differently among women and men. This study used a gender-based analysis approach to explore individual- and neighbourhood-level sex differences in mental health service use in a context of uniquely smaller urban and rural settlements. Methods: This cross-sectional analysis leveraged multiple person-based administrative health datasets linked with geospatial datasets among the population aged 1 and over in the province of New Brunswick, Canada. We used multinomial logistic regression to examine associations between neighbourhood characteristics with risk of service contacts for mood and anxiety disorders in 2015/2016, characterizing the areal measures among all residents (gender neutral) and by males and females separately (gender specific), and controlling for age group. Results: Among the province’s 707,575 eligible residents, 10.7% (females: 14.0%; males: 7.3%) used mental health services in the year of observation. In models adjusted for gender-neutral neighbourhood characteristics, service contacts were significantly more likely among persons residing in the most materially deprived areas compared with the least (OR = 1.09 [95% CI: 1.05–1.12]); when stratified by individuals’ sex, the risk pattern held for females (OR = 1.13 [95% CI: 1.09–1.17]) but not males (OR = 1.00 [95% CI: 0.96–1.05]). Residence in the most female-specific materially deprived neighbourhoods was independently associated with higher risk of mental health service use among individual females (OR = 1.08 [95% CI: 1.02–1.14]) but not among males (OR = 1.02 [95% CI: 0.95–1.10]). Conclusion: These findings emphasize that research needs to better integrate sex and gender in contextual measures aiming to inform community interventions and neighbourhood designs, notably in small urban and rural settings, to reduce socioeconomic inequalities in the burden of mental disorders.

Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

OBJECTIVES

Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters.

METHODS

We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years.

RESULTS

The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years.

CONCLUSION

National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.

Identifying urban built environment factors in pregnancy care and maternal mental health outcomes

Backgrounds

Risk factors related to the built environment have been associated with women’s mental health and preventive care. This study sought to identify built environment factors that are associated with variations in prenatal care and subsequent pregnancy-related outcomes in an urban setting.

Methods

In a retrospective observational study, we characterized the types and frequency of prenatal care events that are associated with the various built environment factors of the patients’ residing neighborhoods. In comparison to women living in higher-quality built environments, we hypothesize that women who reside in lower-quality built environments experience different patterns of clinical events that may increase the risk for adverse outcomes. Using machine learning, we performed pattern detection to characterize the variability in prenatal care concerning encounter types, clinical problems, and medication prescriptions. Structural equation modeling was used to test the associations among built environment, prenatal care variation, and pregnancy outcome. The main outcome is postpartum depression (PPD) diagnosis within 1 year following childbirth. The exposures were the quality of the built environment in the patients’ residing neighborhoods. Electronic health records (EHR) data of pregnant women (n = 8,949) who had live delivery at an urban academic medical center from 2015 to 2017 were included in the study.

Results

We discovered prenatal care patterns that were summarized into three common types. Women who experienced the prenatal care pattern with the highest rates of PPD were more likely to reside in neighborhoods with homogeneous land use, lower walkability, lower air pollutant concentration, and lower retail floor ratios after adjusting for age, neighborhood average education level, marital status, and income inequality.

Conclusions

In an urban setting, multi-purpose and walkable communities were found to be associated with a lower risk of PPD. Findings may inform urban design policies and provide awareness for care providers on the association of patients’ residing neighborhoods and healthy pregnancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-021-04056-1.

An Ecological Model to Frame the Delivery of Pediatric Preventive Care

Screening and surveillance are integral aspects of child health promotion and disease prevention. The American Academy of Pediatrics recommends that primary care clinicians screen children and adolescents for a broad array of conditions, conduct surveillance of growth and development, identify social determinants of health, and identify protective and risk factors that might impact health over time. However, access to and outcomes of preventive services vary based on features of children’s social ecology, including family and community contexts. The proposed five-stage socio-ecological model considers multiple contextual dimensions of pediatric screening: (1) individual, (2) interpersonal, (3) organizational, (4) community/population, and (5) public policy. Incorporating this model into routine care might improve outcomes at the individual and population level. Future endeavors should focus on integration of this model with validated risk screening tools as part of a supportive electronic health record, culture, and incentive structure. Further research assessing the contributors and outcomes of differences in beliefs, resources, practices, and opportunities among individuals, families, providers, primary care organizations, communities, health systems, and policy partners will be essential in advancing knowledge and policies to improve preventive services delivery.

Creative Approaches for Assessing Long-term Outcomes in Children

Advances in new technologies, when incorporated into routine health screening, have tremendous promise to benefit children. The number of health screening tests, many of which have been developed with machine learning or genomics, has exploded. To assess efficacy of health screening, ideally, randomized trials of screening in youth would be conducted; however, these can take years to conduct and may not be feasible. Thus, innovative methods to evaluate the long-term outcomes of screening are needed to help clinicians and policymakers make informed decisions. These methods include using longitudinal and linked-data systems to evaluate screening in clinical and community settings, school data, simulation modeling approaches, and methods that take advantage of data available in the digital and genomic age. Future research is needed to evaluate how longitudinal and linked-data systems drawing on community and clinical settings can enable robust evaluations of the effects of screening on changes in health status. Additionally, future studies are needed to benchmark participating individuals and communities against similar counterparts and to link big data with natural experiments related to variation in screening policies. These novel approaches have great potential for identifying and addressing differences in access to screening and effectiveness of screening across population groups and communities.

Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records

BACKGROUND

Extensive data available in electronic health records (EHRs) have the potential to improve asthma care and understanding of factors influencing asthma outcomes. However, this work can be accomplished only when the EHR data allow for accurate measures of severity, which at present are complex and inconsistent.

OBJECTIVE

Our aims were to create and evaluate a standardized pediatric asthma severity phenotype based in clinical asthma guidelines for use in EHR-based health initiatives and studies and also to examine the presence and absence of these data in relation to patient characteristics.

METHODS

We developed an asthma severity computable phenotype and compared the concordance of different severity components contributing to the phenotype to trends in the literature. We used multivariable logistic regression to assess the presence of EHR data relevant to asthma severity.

RESULTS

The asthma severity computable phenotype performs as expected in comparison with national statistics and the literature. Severity classification for a child is maximized when based on the long-term medication regimen component and minimized when based only on the symptom data component. Use of the severity phenotype results in better, clinically grounded classification. Children for whom severity could be ascertained from these EHR data were more likely to be seen for asthma in the outpatient setting and less likely to be older or Hispanic. Black children were less likely to have lung function testing data present.

CONCLUSION

We developed a pragmatic computable phenotype for pediatric asthma severity that is transportable to other EHRs.

Governing Personalized Health: A Scoping Review

Genetic research is advancing rapidly. One important area for the application of the results from this work is personalized health. These are treatments and preventive interventions tailored to the genetic profile of specific groups or individuals. The inclusion of personalized health in existing health systems is a challenge for policymakers. In this article, we present the results of a thematic scoping review of the literature dealing with governance and policy of personalized health. Our analysis points to four governance challenges that decisionmakers face against the background of personalized health. First, researchers have highlighted the need to further extend and harmonize existing research infrastructures in order to combine different types of genetic data. Second, decisionmakers face the challenge to create trust in personalized health applications, such as genetic tests. Third, scholars have pointed to the importance of the regulation of data production and sharing to avoid discrimination of disadvantaged groups and to facilitate collaboration. Fourth, researchers have discussed the challenge to integrate personalized health into regulatory-, financing-, and service provision structures of existing health systems. Our findings summarize existing research and help to guide further policymaking and research in the field of personalized health governance.

Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions

BACKGROUND

Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions.

OBJECTIVE

Identify existing approaches to inclusion of a broad set of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions.

METHODS

A systematic review of scientific literature published and indexed in PubMed, Web of Science, Association of Computing Machinery (ACM) and SCOPUS from 2010 through October 2020 was performed. To be included, articles had to include search terms related to Electronic Health Record (EHR) data Neighborhood-Level Risk Factors (NLRFs), and Machine Learning (ML) Methods. Citations of relevant articles were also reviewed for additional articles for inclusion. Articles were reviewed and coded by two independent reviewers to capture key information including data sources, linkage of EHR to NRLFs, methods, and results. Articles were assessed for quality using a modified Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO).

RESULTS

A total of 334 articles were identified for abstract review. 36 articles were identified for full review with 19 articles included in the final analysis. All but two of the articles included socio-demographic data derived from the U.S. Census and we found great variability in sources of NLRFs beyond the Census. The majority or the articles (14 of 19) included broader clinical (e.g. medications, labs and co-morbidities) and demographic information about the individual from the EHR in addition to the clinical outcome variable. Half of the articles (10) had a stated goal to predict the outcome(s) of interest. While results of the studies reinforced the correlative association of NLRFs to clinical outcomes, only one article found that adding NLRFs into a model with other data added predictive power with the remainder concluding either that NLRFs were of mixed value depending on the model and outcome or that NLRFs added no predictive power over other data in the model. Only one article scored high on the quality assessment with 13 scoring moderate and 4 scoring low.

CONCLUSIONS

In spite of growing interest in combining NLRFs with EHR data for clinical prediction, we found limited evidence that NLRFs improve predictive power in clinical risk models. We found these data and methods are being used in four ways. First, early approaches to include broad NLRFs to predict clinical risk primarily focused on dimension reduction for feature selection or as a data preparation step to input into regression analysis. Second, more recent work incorporates NLRFs into more advanced predictive models, such as Neural Networks, Random Forest, and Penalized Lasso to predict clinical outcomes or predict value of interventions. Third, studies that test how inclusion of NLRFs predict clinical risk have shown mixed results regarding the value of these data over EHR or claims data alone and this review surfaced evidence of potential quality challenges and biases inherent to this approach. Finally, NLRFs were used with unsupervised learning to identify underlying patterns in patient populations to recommend targeted interventions. Further access to computable, high quality data is needed along with careful study design, including sub-group analysis, to better determine how these data and methods can be used to support decision making in a clinical setting.

Cross-Sectional Associations of Built and Social Neighborhood Environment Variables with Body Mass Index in a Large Sample of Urban Predominantly African American Children

Background and Objectives: Neighborhood environments may play a role in the development of child obesity by providing or limiting opportunities for children to be physically active and access healthy food near the home. This study quantifies associations between the neighborhood built and social environment and age- and sex- standardized body mass index (BMI) z-scores in a predominantly African American urban sample. Methods: Electronic health record data from a pediatric integrated delivery system (N = 26,460 children, 6 to 19 years old in Philadelphia in 2014) were linked to eight built and social neighborhood environment characteristics. Generalized estimating equations were used to obtain adjusted associations between neighborhood features and age- and sex-adjusted BMI Z-score. Interactions between built and social exposures were examined, as well as effect modification by age, sex, neighborhood socioeconomic status, and population density. Results: Of 26,460 children, 17% were overweight and 21% were obese. After adjustment for individual- and neighborhood-level confounders, higher neighborhood greenness and higher walkability were associated with lower BMI z-score [mean difference per standard deviation (SD): -0.069 (95% confidence interval: [-0.108 to -0.031] and -0.051 [-0.085, -0.017], respectively)]. Higher levels of neighborhood food and physical activity resources were associated with higher BMI z-score [mean difference per SD 0.031 (0.012 and 0.050)]. We observed no interaction between the built and social neighborhood measures. Conclusion: Policies to promote walkability and greening of urban neighborhoods may contribute to preventing obesity in children.

Reviewing the reliability of Land Use and Land Cover data in studies relating human health to the environment

BACKGROUND

In recent years, research has been increasingly devoted to understanding the complex human health-environment relationship. Nevertheless, many different measurements have been applied to characterize the environment. Among them, the application of Land Use and Land Cover (LULC) data is becoming more noticeable over time.

AIMS

This research aims to analyse the reliability of Land Use and Land Cover data (LULC) data as a suitable describer of the environment in studies relating human health to the environment. With a specific focus on the methodologies using LULC data, we also examine the study designs and analytical methods that have been commonly performed so far.

MATERIALS AND METHODS

We gathered studies relating human health outcomes to Land Use and Land Cover (LULC) data. A Boolean search limited to reviews was conducted in February 2019 using Web of Science Core Collection search engines. Five reviews were selected as our preliminary starting set of literature and from those, two backward snowballing searches were conducted. The first backward snowballing search used the reference lists of the first 5 reviews and revealed 17 articles. From these, the second search gathered 24 new articles also fulfilling the inclusion criteria established. In total, 41 articles were examined.

RESULTS

Our main results reported that Land Use and Land Cover (LULC) data national level data was preferred over LULC international level data. However, this tendency seems to be strongly related to the specific aims of the articles. They essentially defined the living environment either through buffer zones, using the administrative boundaries wherein the individuals reside, or using the specific location of the individuals assessed. As for the characterization of the environment, authors performed 4 principal methodologies: extracting the percentage of green space, computing the "Land Use mix", recording the type of land cover, and using the percentage of tree canopy. Besides, all the articles included measurements in urban contexts and most of them evaluated the accessibility of individuals to their surroundings. Furthermore, it was clearly stated that the complexity of the topic and the challenging data leads authors to carry out advanced statistical methods and mostly cross-sectional designs with no causal relations.

DISCUSSION AND CONCLUSIONS

Land Use and Land Cover (LULC) data has been demonstrated to be a versatile tool supporting both local-focused studies with few individuals involved and broad territorial-scoped studies with huge populations. Promising synergy has been highlighted between Electronic Health Records (EHR) and LULC data in studies dealing with massive information and broader scopes with regards to the assessment of territorial realities. As this emerging topic matures, investigators should (1) elucidate subjects of ongoing debate such as the measurement of the living environment and its characterization; (2) explore the whole potential of LULC data, using methodologies that encompass both their biophysical and socioeconomic information; (3) perform innovative designs that are able to establish causal relationships among the studied variables (for example, Cellular Automata models), and (4) expand the current set of studied health outcomes leveraging comprehensive and trustworthy health data sources such as EHR.

Investigating associations between physical activity-related neighborhood built environment features and child weight status to inform local practice

Despite evidence of the importance of neighborhood built environment features in relation to physical activity and obesity, research has been limited in informing localized practice due to small sample sizes and limited geographic coverage. This demonstration study integrated data from a local pediatric health system with nationally available neighborhood built environment data to inform local decision making around neighborhood environments and childhood obesity. Height/weight from clinic visits and home neighborhood measures from the U.S. Environmental Protections Agency and WalkScore were obtained for 15,989 6-17 year olds. Multilevel models accounted for the nested data structure and were adjusted for neighborhood income and child sociodemographics. In 9-17 year olds, greater street connectivity and walkability were associated with a 0.01-0.04 lower BMIz (Ps = .009-.017) and greater residential density, street connectivity, and walkability were associated 5-7% lower odds of being overweight/obese (Ps = .004-.044) per standard deviation increase in environment variable. 45.9% of children in the lowest walkability tertile were overweight or obese, whereas 43.1% of children in the highest walkability tertile were overweight or obese. Maps revealed areas with low walkability and a high income-adjusted percent of children overweight/obese. In the Kansas City area, data showed that fewer children were overweight/obese in more walkable neighborhoods. Integrating electronic health records with neighborhood environment data is a replicable process that can inform local practice by highlighting the importance of neighborhood environment features locally and pointing to areas most in need of interventions.

Studying pediatric health outcomes with electronic health records using Bayesian clustering and trajectory analysis

Use of routinely collected data from electronic health records (EHR) can expedite longitudinal studies that investigate childhood exposures and rare pediatric health outcomes. For instance, characteristics of the body mass index (BMI) trajectory early in life may be associated with subsequent development of type 2 diabetes. Past studies investigating these relationships have used longitudinal cohort data collected over the course of many years to investigate the connection between BMI trajectory and subsequent development of diabetes. In contrast, EHR data from routine clinical care can provide longitudinal information on early-life BMI trajectories as well as subsequent health outcomes without requiring any additional data collection. In this study, we introduce a Bayesian joint phenotyping and BMI trajectory model to address data quality challenges in an EHR-based study of early-life BMI and type 2 diabetes in adolescence. We compared this joint modeling approach to traditional approaches using a computable phenotype for type 2 diabetes or separately estimated BMI trajectories and type 2 diabetes phenotypes. In a sample of 49,062 children derived from the PEDSnet consortium of pediatric healthcare systems, a median 8 (interquartile range [IQR] 5-13) BMI measurements were available to characterize the early-life BMI trajectory. The joint modeling and computable phenotype approaches found that age at adiposity rebound between 5 and 9 years was associated with higher odds of type 2 diabetes in adolescence compared to age at adiposity rebound between 2 and 5 years (joint model odds ratio [OR] = 1.77; computable phenotype OR = 1.88) and that BMI in excess of 140% of the 95th percentile for age and sex at age 9 years was associated with higher odds of type 2 diabetes in adolescence relative to children with BMI from 100 to 120% of the 95th percentile (joint model OR = 6.22; computable phenotype OR = 13.25). Estimates from the separate phenotyping and trajectory model were substantially attenuated towards the null. These results demonstrate that EHR data coupled with modern methodologic approaches can improve efficiency and timeliness of studies of childhood exposures and rare health outcomes.

Evaluation of Neighborhood Socioeconomic Characteristics and Advance Care Planning Among Older Adults

IMPORTANCE

Advance care planning (ACP) is low among older adults with socioeconomic disadvantage. There is a need for tailored community-based approaches to increase ACP, but community patterns of ACP are poorly understood.

OBJECTIVE

To examine the association between neighborhood socioeconomic status (nSES) and ACP and to identify communities with both low nSES and low rates of ACP.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study examined University of California San Francisco electronic health record (EHR) data and place-based data from 9 San Francisco Bay Area counties. Participants were primary care patients aged 65 years or older and living in the San Francisco Bay Area in July 2017. Statistical analysis was performed from May to June 2020.

EXPOSURES

Patients' home addresses were geocoded and assigned to US Census tracts. The primary factor, nSES, an index combining area-level measures of income, education, poverty, employment, occupation, and housing or rent values, was divided into quintiles scaled to the distribution of all US Census tracts in the Bay Area (Q1 = lowest nSES). Covariates were from the EHR and included health care use (primary care, outpatient specialty, emergency department, and inpatient encounters in the prior year).

MAIN OUTCOMES AND MEASURES

ACP was defined as a scanned document (eg, advance directive), ACP Current Procedural Terminology code, or ACP note type in the EHR.

RESULTS

There were 13 104 patients included in the cohort-mean (SD) age was 75 (8) years, with 7622 female patients (58.2%), 897 patients (6.8%) identified as Black, 913 (7.0%) as Latinx, 3788 (28.9%) as Asian/Pacific Islander, and 748 (5.7%) as other minority race/ethnicity, and 2393 (18.3%) self-reported that they preferred to speak a non-English language. Of these, 3827 patients (29.2%) had documented ACP. The cohort was distributed across all 5 quintiles of nSES (Q1: 1426 patients [10.9%]; Q2: 1792 patients [13.7%]; Q3: 2408 patients [18.4%]; Q4: 3330 patients [25.4%]; Q5: 4148 patients [31.7%]). Compared with Q5 and after adjusting for health care use, all lower nSES quintiles showed a lower odds of ACP in a graded fashion (Q1: adjusted odds ratio [aOR] = 0.71 [95% CI, 0.61-0.84], Q2: aOR = 0.74 [95% CI, 0.64-0.86], Q3: aOR = 0.81 [95% CI, 0.71-0.93], Q4: aOR = 0.82 [95% CI, 0.72-0.93]. A bivariable map of ACP by nSES allowed identification of 5 neighborhoods with both low nSES and ACP.

CONCLUSIONS AND RELEVANCE

In this study, lower nSES was associated with lower ACP documentation after adjusting for health care use. Using EHR and place-based data, communities of older adults with both low nSES and low ACP were identified. This is a first step in partnering with communities to develop targeted, community-based interventions to meaningfully increase ACP.

Using electronic health records for population health sciences: a case study to evaluate the associations between changes in left ventricular ejection fraction and the built environment

OBJECTIVE

Electronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients.

MATERIALS AND METHODS

We identified 1287 HF patients with at least 2 left ventricular EF measurements that are minimally 1 year apart. EHR data were obtained at an academic medical center in New York for patients who visited between 2012 and 2017. Longitudinal clinical information was linked with address-based built environment metrics related to transportation, air quality, land use, and accessibility by GIS. The primary outcome is the increase in the severity of EF categories. Statistical analyses were performed using mixed-effects models, including a subgroup analysis of patients who initially had normal EF measurements.

RESULTS

Previously reported effects from the built environment among HF patients were identified. Increased daily nitrogen dioxide concentration was associated with the outcome while controlling for known HF risk factors including sex, comorbidities, and medication usage. In the subgroup analysis, the outcome was significantly associated with decreased distance to subway stops and increased distance to parks.

CONCLUSIONS

Population health studies using EHR data may drive efficient hypothesis generation and enable novel information technology-based interventions. The availability of more precise outcome measurements and home locations, and frequent collection of individual-level social determinants of health may further drive the use of EHR data in population health studies.

A scoping review to explore the experiences and outcomes of younger people with disabilities in residential aged care facilities

OBJECTIVE

In Australia, over 6,000 adults younger than 65 have been inappropriately placed in nursing homes designed to accommodate older adults. The primary aim of this review was to map the literature on the experiences and outcomes of young people with disability who are placed in aged care.

METHODS

A scoping review of the published literature from 2009-2018 was conducted using Embase, Medline, PsycINFO and Scopus.

RESULTS

Eleven articles were identified (7 qualitative, 3 mixed methods, 1 quantitative). Results demonstrated the inability of aged care facilities to meet the basic human needs of young people (e.g., privacy, physical, sexual, social, nutritional, emotional need) and highlighted the lack of choice young people with disability have in regards to rehabilitation and housing. There was limited data relating to the trajectory and support needs of young people placed in aged care facilities.

CONCLUSIONS

This review highlights the negative outcomes young people experience while living in aged care. Future research should investigate the trajectory and support needs of young people in aged care facilities. Systemic changes are required to meet the needs of young people with complex needs at risk of admission to aged care including timely rehabilitation and housing and support options.

Heavy precipitation and asthma exacerbation risk among children: A case-crossover study using electronic health records linked with geospatial data

Extreme precipitation events may be an important environmental trigger for asthma exacerbations in children. We used a time stratified case-crossover design and data from a large electronic health record database at the Children's Hospital of Philadelphia (CHOP) to estimate associations of daily heavy precipitation (defined as > 95th percentile of the summertime distribution) with asthma exacerbation among children. We defined control days as those falling on the same day of the week within the same month and year as the case. We restricted our primary analyses to the summer months in years 2011-2016 and used conditional logistic regression models to estimate associations between heavy precipitation and acute asthma exacerbations in both outpatient (primary care, specialty care, and emergency department) and inpatient settings. We investigated numerous individual-level (e.g., age, sex, eczema diagnosis) and environmental measures (e.g., greenspace, particulate matter) as potential effect modifiers. The analysis include 13,483 asthma exacerbations in 10,434 children. Odds of asthma exacerbation were 11% higher on heavy precipitation vs. no precipitation days (95% CI: 1.02-1.21). There was little evidence of effect modification by most measures. These results suggest that heavy summertime precipitation events may contribute to asthma exacerbations. Further research using larger datasets from other health systems is needed to confirm these results, and to explore underlying mechanisms.

Information Loss in Harmonizing Granular Race and Ethnicity Data: Descriptive Study of Standards

BACKGROUND

Data standards for race and ethnicity have significant implications for health equity research.

OBJECTIVE

We aim to describe a challenge encountered when working with a multiple-race and ethnicity assessment in the Eastern Caribbean Health Outcomes Research Network (ECHORN), a research collaborative of Barbados, Puerto Rico, Trinidad and Tobago, and the US Virgin Islands.

METHODS

We examined the data standards guiding harmonization of race and ethnicity data for multiracial and multiethnic populations, using the Office of Management and Budget (OMB) Statistical Policy Directive No. 15.

RESULTS

Of 1211 participants in the ECHORN cohort study, 901 (74.40%) selected 1 racial category. Of those that selected 1 category, 13.0% (117/901) selected Caribbean; 6.4% (58/901), Puerto Rican or Boricua; and 13.5% (122/901), the mixed or multiracial category. A total of 17.84% (216/1211) of participants selected 2 or more categories, with 15.19% (184/1211) selecting 2 categories and 2.64% (32/1211) selecting 3 or more categories. With aggregation of ECHORN data into OMB categories, 27.91% (338/1211) of the participants can be placed in the "more than one race" category.

CONCLUSIONS

This analysis exposes the fundamental informatics challenges that current race and ethnicity data standards present to meaningful collection, organization, and dissemination of granular data about subgroup populations in diverse and marginalized communities. Current standards should reflect the science of measuring race and ethnicity and the need for multidisciplinary teams to improve evolving standards throughout the data life cycle.

Development of an electronic health records datamart to support clinical and population health research

Introduction:

Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research; however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research Datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators.

Methods:

The CRDM was designed to (1) contain most of the patient-level data elements needed for research studies; (2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members); (3) be queried via a code-based system to promote reproducibility and consistency across studies; and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information.

Results:

We provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource.

Conclusions:

The CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.

Defining gentrification for epidemiologic research: A systematic review

Neighborhoods have a profound impact on individual health. There is growing interest in the role of dynamic changes to neighborhoods-including gentrification-on the health of residents. However, research on the association between gentrification and health is limited, partly due to the numerous definitions used to define gentrification. This article presents a systematic review of the current state of literature describing the association between gentrification and health. In addition, it provides a novel framework for addressing important next steps in this research. A total of 1393 unique articles were identified, 122 abstracts were reviewed, and 36 articles published from 2007-2020 were included. Of the 36 articles, 9 were qualitative, 24 were quantitative, and 3 were review papers. There was no universally accepted definition of gentrification; definitions often used socioeconomic variables describing demographics, housing, education, and income. Health outcomes associated with gentrification included self-reported health, preterm birth, mental health conditions, alcohol use, psychosocial factors, and health care utilization, though the direction of this association varied. The results of this review also suggest that the impact of gentrification on health is not uniform across populations. For example, marginalized populations, such as Black residents and the elderly, were impacted more than White and younger residents. In addition, we identified multiples gaps in the research, including the need for a conceptual model, future mechanistic studies, and interventions.

Role of Health Information Technology in Addressing Health Disparities: Patient, Clinician, and System Perspectives

Over the last decade, health information technology (IT) has dramatically transformed medical practice in the United States. On May 11-12, 2017, the National Institute on Minority Health and Health Disparities, in partnership with the National Science Foundation and the National Health IT Collaborative for the Underserved, convened a scientific workshop, "Addressing Health Disparities with Health Information Technology," with the goal of ensuring that future research guides potential health IT initiatives to address the needs of health disparities populations. The workshop examined patient, clinician, and system perspectives on the potential role of health IT in addressing health disparities. Attendees were asked to identify and discuss various health IT challenges that confront underserved communities and propose innovative strategies to address them, and to involve these communities in this process. Community engagement, cultural competency, and patient-centered care were highlighted as key to improving health equity, as well as to promoting scalable, sustainable, and effective health IT interventions. Participants noted the need for more research on how health IT can be used to evaluate and address the social determinants of health. Expanding public-private partnerships was emphasized, as was the importance of clinicians and IT developers partnering and using novel methods to learn how to improve health care decision-making. Finally, to advance health IT and promote health equity, it will be necessary to record and capture health disparity data using standardized terminology, and to continuously identify system-level deficiencies and biases.

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."

Variation in methods, results and reporting in electronic health record-based studies evaluating routine care in gout: A systematic review

OBJECTIVE

To perform a systematic review examining the variation in methods, results, reporting and risk of bias in electronic health record (EHR)-based studies evaluating management of a common musculoskeletal disease, gout.

METHODS

Two reviewers systematically searched MEDLINE, Scopus, Web of Science, CINAHL, PubMed, EMBASE and Google Scholar for all EHR-based studies published by February 2019 investigating gout pharmacological treatment. Information was extracted on study design, eligibility criteria, definitions, medication usage, effectiveness and safety data, comprehensiveness of reporting (RECORD), and Cochrane risk of bias (registered PROSPERO CRD42017065195).

RESULTS

We screened 5,603 titles/abstracts, 613 full-texts and selected 75 studies including 1.9M gout patients. Gout diagnosis was defined in 26 ways across the studies, most commonly using a single diagnostic code (n = 31, 41.3%). 48.4% did not specify a disease-free period before 'incident' diagnosis. Medication use was suboptimal and varied with disease definition while results regarding effectiveness and safety were broadly similar across studies despite variability in inclusion criteria. Comprehensiveness of reporting was variable, ranging from 73% (55/75) appropriately discussing the limitations of EHR data use, to 5% (4/75) reporting on key data cleaning steps. Risk of bias was generally low.

CONCLUSION

The wide variation in case definitions and medication-related analysis among EHR-based studies has implications for reported medication use. This is amplified by variable reporting comprehensiveness and the limited consideration of EHR-relevant biases (e.g. data adequacy) in study assessment tools. We recommend accounting for these biases and performing a sensitivity analysis on case definitions, and suggest changes to assessment tools to foster this.

Chronic Kidney Disease in Agricultural Communities

Patients residing in agricultural communities have a high risk of developing chronic kidney disease. In the Great Plains, geo-environmental risk factors (eg, variable climate, temperature, air quality, water quality, and drought) combine with agro-environmental risk factors (eg, exposure to fertilizers, soil conditioners, herbicides, fungicides, and pesticides) to increase risk for toxic nephropathy. However, research defining the specific influence of agricultural chemicals on the progression of kidney disease in rural communities has been somewhat limited. By linking retrospective clinical data within electronic medical records to environmental data from sources like US Environmental Protection Agency, analytical models are beginning to provide insight into the impact of agricultural practices on the rate of progression for kidney disease in rural communities.

Prediction of 30-day pediatric unplanned hospitalizations using the Johns Hopkins Adjusted Clinical Groups risk adjustment system

Background

The Johns Hopkins ACG System is widely used to predict patient healthcare service use and costs. Most applications have focused on adult populations. In this study, we evaluated the use of the ACG software to predict pediatric unplanned hospital admission in a given month, based on the past year’s clinical information captured by electronic health records (EHRs).

Methods and findings

EHR data from a multi-state pediatric integrated delivery system were obtained for 920,051 patients with at least one physician visit during January 2009 to December 2016. Over this interval an average of 0.36% of patients each month had an unplanned hospitalization. In a 70% training sample, we used the generalized linear mixed model (GLMM) to generate regression coefficients for demographic, clinical predictors derived from the ACG system, and prior year hospitalizations. Applying these coefficients to a 30% test sample to generate risk scores, we found that the area under the receiver operator characteristic curve (AUC) was 0.82. Omitting prior hospitalizations decreased the AUC from 0.82 to 0.80, and increased under-estimation of hospitalizations at the greater risk levels. Patients in the top 5% of risk scores accounted for 43% and the top 1% of risk scores accounted for 20% of all unplanned hospitalizations.

Conclusions

A predictive model based on 12-months of demographic and clinical data using the ACG system has excellent predictive performance for 30-day pediatric unplanned hospitalization. This model may be useful in population health and care management applications targeting patients likely to be hospitalized. External validation at other institutions should be done to confirm our results.

Using informatics and mobile health to improve medication safety monitoring in kidney transplant recipients

Purpose

The development, testing, and preliminary validation of a technology-enabled, pharmacist-led intervention aimed at improving medication safety and outcomes in kidney transplant recipients are described.

Summary

Medication safety issues, encompassing medication errors (MEs), medication nonadherence, and adverse drug events (ADEs), are a predominant cause of poor outcomes after kidney transplantation. However, a limited number of clinical trials assessing the effectiveness of technology in improving medication safety and outcomes in transplant recipients have been conducted. Through an iterative, evidence-based approach, a technology-enabled intervention aimed at improving posttransplant medication safety outcomes was developed, tested, and preliminarily validated. Early acceptability and feasibility results from a prospective, randomized controlled trial assessing the effectiveness of this system are reported here. Of the 120 patients enrolled into the trial at the time of writing, 60 were randomly assigned to receive the intervention. At a mean ± S.D. follow-up of 5.8 ± 4.0 months, there were 2 patient dropouts in the intervention group, resulting in a retention rate of 98%, which was higher than the expected 90% retention rate.

Conclusion

The development and deployment of a comprehensive medication safety monitoring dashboard for kidney transplant recipients is feasible and acceptable to patients in the current healthcare environment. An ongoing randomized controlled clinical trial is assessing whether such a system reduces MEs and ADRs, leading to improved patient outcomes.

Identifying neighborhood characteristics associated with diabetes and hypertension control in an urban African-American population using geo-linked electronic health records

For health care providers, information on community-level social determinants of health is most valuable when it is specific to the populations and health outcomes for which they are responsible. Diabetes and hypertension are highly prevalent conditions whose management requires an interplay of clinical treatment and behavioral modifications that may be sensitive to community conditions. We used geo-linked electronic health records from 2016 of African American patients of a network of federally qualified health centers in Philadelphia, PA to examine cross-sectional associations between characteristics of patients' residential neighborhoods and hypertension and diabetes control (n = 1061 and n = 2633, respectively). Hypertension and diabetes control were defined to align with the Health Resources and Services Administration (HRSA) Uniform Data System (UDS) reporting requirements for HRSA-funded health centers. We examined associations with nine measures of neighborhood socioeconomic status (poverty, education, deprivation index), social environment (violent crime, perceived safety and social capital, racial segregation), and built environment (land-use mix, intersection density). In demographics-adjusted log-binomial regression models accounting for neighborhood-level clustering, poor diabetes and hypertension control were more common in highly segregated neighborhoods (i.e., high proportion of African American residents relative to the mean for Philadelphia; prevalence ratio = 1.27 [1.02-1.57] for diabetes, 1.22 [1.12-1.33] for hypertension) and less common in more walkable neighborhoods (i.e., higher retail land use). Neighborhood deprivation was also weakly associated with poor hypertension control. An important consideration in making geographic information actionable for providers is understanding how specific community-level determinants affect the patient population beyond individual-level determinants.

Social Determinants of Mental Health: Where We Are and Where We Need to Go

PURPOSE OF REVIEW

The present review synthesizes recent literature on social determinants and mental health outcomes and provides recommendations for how to advance the field. We summarize current studies related to changes in the conceptualization of social determinants, how social determinants impact mental health, what we have learned from social determinant interventions, and new methods to collect, use, and analyze social determinant data.

RECENT FINDINGS

Recent research has increasingly focused on interactions between multiple social determinants, interventions to address upstream causes of mental health challenges, and use of simulation models to represent complex systems. However, methodological challenges and inconsistent findings prevent a definitive understanding of which social determinants should be addressed to improve mental health, and within what populations these interventions may be most effective. Recent advances in strategies to collect, evaluate, and analyze social determinants suggest the potential to better appraise their impact and to implement relevant interventions.