scholarly journals Racial Residential Segregation and Hypertensive Disorder of Pregnancy Among Women in Chicago: Analysis of Electronic Health Record Data

2018 ◽  
Vol 31 (11) ◽  
pp. 1221-1227 ◽  
Author(s):  
Stephanie L Mayne ◽  
Disha Yellayi ◽  
Lindsay R Pool ◽  
William A Grobman ◽  
Kiarri N Kershaw
Keyword(s):  
Black Women ◽  
Residential Segregation ◽  
Census Tract ◽  
Hypertensive Disorder ◽  
Electronic Health Record Data ◽  
Racial Residential Segregation ◽  
Maternal Characteristics ◽  
Neighborhood Poverty ◽  
Hypertensive Disorder Of Pregnancy ◽  
Electronic Health

Abstract BACKGROUND Racial residential segregation is associated with higher rates of chronic hypertension, as well as greater risk of preterm birth and low birthweight. However, few studies have examined associations between segregation and hypertensive disorder of pregnancy (HDP). METHODS Electronic health records from 4,748 singleton births among non-Hispanic black women at Prentice Women’s Hospital in Chicago, IL (2009–2013) were geocoded to the census tract level. Residential segregation was measured using the Gi* statistic, a z-score measuring the extent to which each individual’s neighborhood composition deviates from the composition of the larger surrounding area. Segregation was categorized as low (z < 0), medium (z = 0–1.96) or high (z > 1.96). We estimated cross-sectional associations of segregation with HDP using multilevel logistic regression models with census tract random intercepts. Models adjusted for neighborhood poverty and maternal characteristics. We also examined effect modification by neighborhood poverty. RESULTS Overall, 27.2% of women lived in high segregation, high-poverty neighborhoods. Racial residential segregation was not associated as a main effect with HDP in models adjusting for neighborhood poverty and maternal characteristics. However, at higher levels of neighborhood poverty (>20%), women living in high- and medium-segregated neighborhoods had greater odds of HDP relative to those in low-segregation neighborhoods (P interaction: 0.002). CONCLUSIONS In this sample of non-Hispanic black women in Chicago, racial residential segregation was associated with greater prevalence of HDP among those living in higher poverty neighborhoods. Understanding sources of heterogeneity in the relationship between segregation and health will help refine targeted intervention efforts to reduce disparities.

Abstract P208: The Differential Impact Of Residential Segregation On Gestational Hypertension Development Among Minority Women

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Mary D Schiff ◽  
Anthony Fabio ◽  
Tiffany Gary-Webb ◽  
Dara Mendez
Keyword(s):  
Black Women ◽  
Residential Segregation ◽  
Census Tract ◽  
Gestational Hypertension ◽  
Cardiometabolic Health ◽  
Economic Segregation ◽  
Birth Record ◽  
Child Bearing ◽  
Record Data ◽  
Us Census

Introduction: Higher levels of residential segregation have been associated with poorer cardiometabolic health profiles among women. Still, it remains unclear whether segregation may differentially impact the development of gestational hypertension (gHTN) among an ethnically-diverse cohort of pregnant women. We used birth record data from 2003-2009 and data from the 2000 US Census to determine whether racial and economic segregation are associated with gHTN among a diverse cohort of child-bearing women in the greater Philadelphia area. Methods: We quantified racial and economic segregation using sociodemographic data from the US Census and the local Getis-Ord (Gi*) spatial statistic. The Gi* produces a spatially-weighted z-score for each census tract reflecting the degree of clustering of racially-similar neighborhoods in an area relative to the surrounding Philadelphia region. We categorized each type of segregation as low (Gi*<0), moderate (Gi*0-1.96), or high (Gi*>1.96), and assigned these to each woman by her census tract of residence. Gestational hypertension was defined in the birth record data as the development of pregnancy-induced hypertension or preeclampsia. We used hierarchical generalized linear mixed effect models to obtain risk ratios and differences (per 1000 women) for the relationships between each form of residential segregation and gHTN. All models were stratified by maternal race/ethnicity, and sequentially adjusted for maternal sociodemographics, health behaviors, medical histories, and neighborhood-level characteristics. Results: Our sample consisted of 220,897 Non-Hispanic (NH) Black (26%), NH White (64%), and Hispanic (10%) women, of whom 4% developed gHTN. However, a much greater proportion of NH Black women both developed gHTN and lived in high segregation neighborhoods compared to NH Whites and Hispanics. After adjustment, NH Black women in moderate and high economic segregation areas had 16% higher risk (RR=1.16, 95% CI: 1.03-1.31) and 23% higher risk (RR=1.23, 95% CI: 1.08-1.39) of gHTN, respectively, compared to NH Black women living in low segregation areas. NH Black women in highly racially segregated neighborhoods saw an additional 9 cases of gHTN (per 1000 women) compared to NH Black women living in more racially integrated neighborhoods (RD=8.47, 95% CI: 3.14-13.80). Among NH White and Hispanic women, economic segregation was not associated with gHTN, and only marginally significant findings were observed for racial segregation. Conclusions: In our diverse sample of child-bearing women from the greater Philadelphia area, higher levels of racial and economic segregation were associated with greater risk of gHTN among NH Black women. Future work should seek to delineate the specific pathways by which neighborhoods differentially impact individual level cardiovascular health based upon race.

scholarly journals Do measures matter? Comparing surface-density-derived and census-tract-derived measures of racial residential segregation

2010 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Michael R Kramer ◽  
Hannah L Cooper ◽  
Carolyn D Drews-Botsch ◽  
Lance A Waller ◽  
Carol R Hogue
Keyword(s):  
Residential Segregation ◽  
Census Tract ◽  
Surface Density ◽  
Racial Residential Segregation

Abstract 15788: Hypertensive Disorders of Pregnancy in Urban Black Women: The Biosocial Impact on Black Births (bibb) Study

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Rhonda Dailey ◽  
Ashleigh Peoples ◽  
Brooke Rengers ◽  
Ana C Wong ◽  
Kristen Daughters ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Black Women ◽  
Gestational Age ◽  
Chronic Hypertension ◽  
Hypertensive Disorder ◽  
Hypertensive Disorders Of Pregnancy ◽  
Hypertensive Disorders ◽  
Hypertensive Disorder Of Pregnancy ◽  
The Mean ◽  
Prenatal Visits

Introduction: Black women experience significant maternal mortality (3.3 times higher) compared to White women, and experience higher adverse outcomes. In the United States, cardiovascular disease is the second leading cause of maternal mortality for Black women. Hypertensive disorders of pregnancy (HDOP) falls under the cardiovascular disease spectrum. Objective: To explore differences in women diagnosed with a HDOP compared to those that do not have HDOP. Methods: A total of 226 African American women from Metro-Detroit and Columbus, Ohio enrolled in a cross-sectional study who had recently gave birth. Women enrolled in a mixed methods study on social and biological stressors to preterm birth with a completed medical record abstraction were identified as having chronic hypertension or a hypertensive disorder of pregnancy (HDOP) prior to the current pregnancy. HDOP is defined as chronic hypertension, chronic hypertension with superimposed preeclampsia, gestational hypertension, preeclampsia or eclampsia. Perinatal complications and birth outcomes were explored. Sociodemographic was derived from completed prenatal questionnaires. Chi square was used for categorical variable and T-test was used for continuous variables. Significance is defined as p ≤ 0.05. Results: The mean age was 26.8±5.9 years. Approximately 70.4% (n=159) were from Detroit, MI and 29.6% were from Columbus, OH. The mean previous live births were 1.9±1.8 (range 0-8). The average number of prenatal visits with a physician were 9.2±2.9 (range 2-19) and the total number of any prenatal visits were 16.0±6.9 (range 1-44). Average baby gestational age is 37.9±2.2 weeks (range 15-26 weeks), and weight is 2998±703.4 grams. Approximately 60 women (26.5%) were identified with a hypertensive disorder of pregnancy. Compared to women not diagnosed with a HDOP, women with a HDOP had an older mean age (28.3±6.4 vs 26.3±5.6), p=0.023; had more prenatal visits (18.2±7.6 vs 15.6±6.5) p=0.007; had babies at a younger gestational age (37.2±2.1 vs 38.2±2.1), p=0.002. Conclusion: These findings will aid in determining factors associated with HDOP in our population, and aid in determining next steps to reduce historic mortality in this group.

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Improving pragmatic clinical trial design using real-world data

2019 ◽  
Vol 16 (3) ◽  
pp. 273-282 ◽  
Author(s):  
Susan M Shortreed ◽  
Carolyn M Rutter ◽  
Andrea J Cook ◽  
Gregory E Simon
Keyword(s):  
Clinical Trials ◽  
Trial Design ◽  
Suicide Attempts ◽  
Real World Data ◽  
Electronic Health Record Data ◽  
Pragmatic Clinical Trial ◽  
Pragmatic Clinical Trials ◽  
Sample Size Calculations ◽  
Record Data ◽  
Electronic Health

Background Pragmatic clinical trials often use automated data sources such as electronic health records, claims, or registries to identify eligible individuals and collect outcome information. A specific advantage that this automated data collection often yields is having data on potential participants when design decisions are being made. We outline how this data can be used to inform trial design. Methods Our work is motivated by a pragmatic clinical trial evaluating the impact of suicide-prevention outreach interventions on fatal and non-fatal suicide attempts in the 18 months after randomization. We illustrate our recommended approaches for designing pragmatic clinical trials using historical data from the health systems participating in this study. Specifically, we illustrate how electronic health record data can be used to inform the selection of trial eligibility requirements, to estimate the distribution of participant characteristics over the course of the trial, and to conduct power and sample size calculations. Results Data from 122,873 people with patient health questionnaire (PHQ) responses, recorded in their electronic health records between 1 July 2010 and 31 March 2012, were used to show that the suicide attempt rate in the 18 months following completion of the questionnaire varies by response to item nine of the PHQ. We estimated that the proportion of individuals with a prior recorded elevated PHQ (i.e. history of suicidal ideation) would decrease from approximately 50% at the beginning of a trial to about 5%, 50 weeks later. Using electronic health record data, we conducted simulations to estimate the power to detect a 25% reduction in suicide attempts. Simulation-based power calculations estimated that randomizing 8000 participants per randomization arm would allow 90% power to detect a 25% reduction in the suicide attempt rate in the intervention arm compared to usual care at an alpha rate of 0.05. Conclusions Historical data can be used to inform the design of pragmatic clinical trials, a strength of trials that use automated data collection for randomizing participants and assessing outcomes. In particular, realistic sample size calculations can be conducted using real-world data from the health systems in which the trial will be conducted. Data-informed trial design should yield more realistic estimates of statistical power and maximize efficiency of trial recruitment.

Leveraging electronic health record data to inform hospital resource management

2021 ◽  
Author(s):  
José Carlos Ferrão ◽  
Mónica Duarte Oliveira ◽  
Daniel Gartner ◽  
Filipe Janela ◽  
Henrique M. G. Martins
Keyword(s):  
Resource Management ◽  
Electronic Health Record ◽  
Health Record ◽  
Hospital Resource ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

scholarly journals Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

2021 ◽  
Author(s):  
Jeffrey G Klann ◽  
Griffin M Weber ◽  
Hossein Estiri ◽  
Bertrand Moal ◽  
Paul Avillach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Chart Review ◽  
Learning Approach ◽  
Health Record ◽  
Learning Approaches ◽  
Electronic Health Record Data ◽  
Icu Admission ◽  
Machine Learning Approach ◽  
Electronic Health

Abstract Introduction The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective We sought to develop and validate a computable phenotype for COVID-19 severity. Methods Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. Conclusion We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

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