Assessing Racial Residential Segregation as a Risk Factor for Severe Maternal Morbidity.

2023

05/2023

1873-2585

PURPOSE: To measure associations of area-level racial and economic residential segregation with severe maternal morbidity (SMM).

METHODS: We conducted a retrospective cohort study of births at two Philadelphia hospitals between 2018-2020 to analyze associations of segregation, quantified using the Index of Concentration at the Extremes (ICE), with SMM. We used stratified multivariable, multilevel, logistic regression models to determine whether associations of ICE with SMM varied by self-identified race or hospital catchment.

RESULTS: Of the 25,979 patients (44.1% Black, 35.8% White), 1,381 (5.3%) had SMM (Black [6.1%], White [4.4%]). SMM was higher among patients residing outside (6.3%), then inside, (5.0%) Philadelphia (P<0.001). Overall, ICE was not associated with SMM. However, ICE (higher proportion of White vs. Black households) was associated with lower odds of SMM among patients residing inside Philadelphia (aOR 0.87, 95% CI: 0.80-0.94) and higher odds outside Philadelphia (aOR 1.12, 95% CI: 0.95-1.31). Moran's I indicated spatial autocorrelation of SMM overall (P<0.001); when stratified, autocorrelation was only evident outside Philadelphia.

CONCLUSIONS: Overall, ICE was not associated with SMM. However, higher ICE was associated with lower odds of SMM among Philadelphia residents. Findings highlight the importance of hospital catchment area and referral patterns in spatial analyses of hospital datasets.

10.1016/j.annepidem.2023.04.018

Ann Epidemiol

37146923

Neighborhood deprivation increases the risk of Post-induction cesarean delivery.

2021

2021 Dec 17

1527-974X

OBJECTIVE: The purpose of this study was to measure the association between neighborhood deprivation and cesarean delivery following labor induction among people delivering at term (≥37 weeks of gestation).

MATERIALS AND METHODS: We conducted a retrospective cohort study of people ≥37 weeks of gestation, with a live, singleton gestation, who underwent labor induction from 2010 to 2017 at Penn Medicine. We excluded people with a prior cesarean delivery and those with missing geocoding information. Our primary exposure was a nationally validated Area Deprivation Index with scores ranging from 1 to 100 (least to most deprived). We used a generalized linear mixed model to calculate the odds of postinduction cesarean delivery among people in 4 equally-spaced levels of neighborhood deprivation. We also conducted a sensitivity analysis with residential mobility.

RESULTS: Our cohort contained 8672 people receiving an induction at Penn Medicine. After adjustment for confounders, we found that people living in the most deprived neighborhoods were at a 29% increased risk of post-induction cesarean delivery (adjusted odds ratio = 1.29, 95% confidence interval, 1.05-1.57) compared to the least deprived. In a sensitivity analysis, including residential mobility seemed to magnify the effect sizes of the association between neighborhood deprivation and postinduction cesarean delivery, but this information was only available for a subset of people.

CONCLUSIONS: People living in neighborhoods with higher deprivation had higher odds of postinduction cesarean delivery compared to people living in less deprived neighborhoods. This work represents an important first step in understanding the impact of disadvantaged neighborhoods on adverse delivery outcomes.

10.1093/jamia/ocab258

J Am Med Inform Assoc

34921313

Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

2021

2021 Jul 19

2511-705X

<p><strong>BACKGROUND: </strong> The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES:  Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems.</p>

<p><strong>METHODS: </strong> This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time.</p>

<p><strong>RESULTS: </strong> Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research.</p>

<p><strong>CONCLUSION: </strong> We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users.</p>

10.1055/s-0041-1731784

Methods Inf Med

34282602

Development and evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic health records.

2020

104339

2020 Nov 06

1872-8243

<p><strong>OBJECTIVE: </strong>To develop an algorithm that infers patient delivery dates (PDDs) and delivery-specific details from Electronic Health Records (EHRs) with high accuracy; enabling pregnancy-level outcome studies in women's health.</p>

<p><strong>MATERIALS AND METHODS: </strong>We obtained EHR data from 1,060,100 female patients treated at Penn Medicine hospitals or outpatient clinics between 2010-2017. We developed an algorithm called MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records that infers a PDD for distinct deliveries based on EHR encounter dates assigned a delivery code, the frequency of code usage, and the time differential between code assignments. We validated MADDIE's PDDs against a birth log independently maintained by the Department of Obstetrics and Gynecology.</p>

<p><strong>RESULTS: </strong>MADDIE identified 50,560 patients having 63,334 distinct deliveries. MADDIE was 98.6 % accurate (F-score 92.1 %) when compared to the birth log. The PDD was on average 0.68 days earlier than the true delivery date for patients with only one delivery (± 1.43 days) and 0.52 days earlier for patients with more than one delivery episode (± 1.11 days).</p>

<p><strong>DISCUSSION: </strong>MADDIE is the first algorithm to successfully infer PDD information using only structured delivery codes and identify multiple deliveries per patient. MADDIE is also the first to validate the accuracy of the PDD using an external gold standard of known delivery dates as opposed to manual chart review of a sample.</p>

<p><strong>CONCLUSION: </strong>MADDIE augments the EHR with delivery-specific details extracted with high accuracy and relies only on structured EHR elements while harnessing temporal information and the frequency of code usage to identify accurate PDDs.</p>

10.1016/j.ijmedinf.2020.104339

Int J Med Inform

33232918