Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response.

Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response.

Cross-Trait Mendelian Randomization Study to Investigate Whether Migraine Is a Risk Factor for Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Migraine is common among people with multiple sclerosis (MS), but the reasons for this are unknown. We tested 3 hypothesized mechanisms for this observed comorbidity, including migraine is a risk factor of MS, genetic variants are shared between the conditions, and migraine is because of MS. METHODS: Data were from 2 sources: publicly available summary statistics from genome-wide association studies of MS (N = 115,748) and migraine (N = 375,752 and N = 361,141) and a case-control study of MS recruited from the Kaiser Permanente Northern California Health Plan (N = 1,991). For the latter participants, migraine status was ascertained using a validated electronic health record migraine probability algorithm or self-report. Using the public summary statistics, we used 2-sample Mendelian randomization to test whether a migraine genetic instrumental variable was associated with MS. We used linkage disequilibrium score regression and LOGODetect to ascertain whether MS and migraine shared genetic variants across the genome and regionally. Using the Northern California MS cohort, we used logistic regression to identify whether people with both MS and migraine had different odds of clinical characteristics (e.g., age at MS onset, Perceived Deficits Questionnaire, and depression) or MS-specific risk factors (e.g., body mass index, smoking status, and infectious mononucleosis status) compared with people with MS without migraine. RESULTS: We did not find evidence supporting migraine as a causal risk factor of MS (p = 0.29). We did, however, identify 4 major histocompatibility complex (MHC) loci shared between MS and migraine. Among the Northern California MS cohort, 774 (39%) experienced migraine. People with both MS and migraine from this cohort were more likely to ever smoke (odds ratio [OR] = 1.30, 95% CI: 1.08-1.57), have worse self-reported cognitive deficits (OR = 1.04, 95% CI: 1.02-1.06), and ever experience depression (OR = 1.48, 95% CI: 1.22-1.80). DISCUSSION: Our findings do not support migraine as a causal risk factor of MS. Several genetic variants, particularly in the MHC, may account for some of the overlap. It seems likely that migraine within the context of MS is because of MS. Identifying what increases the risk of migraine within MS might lead to an improved treatment and quality of life.

Multidrug Resistance of Escherichia coli From Outpatient Uncomplicated Urinary Tract Infections in a Large United States Integrated Healthcare Organization.

Background: Urinary tract infections (UTIs) cause significant disease and economic burden. Uncomplicated UTIs (uUTIs) occur in otherwise healthy individuals without underlying structural abnormalities, with uropathogenic Escherichia coli (UPEC) accounting for 80% of cases. With recent transitions in healthcare toward virtual visits, data on multidrug resistance (MDR) (resistant to ≥3 antibiotic classes) by care setting are needed to inform empiric treatment decision making. Methods: We evaluated UPEC resistance over time by care setting (in-person vs virtual), in adults who received outpatient care for uUTI at Kaiser Permanente Southern California between January 2016 and December 2021. Results: We included 174 185 individuals who had ≥1 UPEC uUTI (233 974 isolates) (92% female, 46% Hispanic, mean age 52 years [standard deviation 20]). Overall, prevalence of UPEC MDR decreased during the study period (13% to 12%) both in virtual and in-person settings (P for trend <.001). Resistance to penicillins overall (29%), coresistance to penicillins and trimethoprim-sulfamethoxazole (TMP-SMX) (12%), and MDR involving the 2 plus ≥1 antibiotic class were common (10%). Resistance to 1, 2, 3, and 4 antibiotic classes was found in 19%, 18%, 8%, and 4% of isolates, respectively; 1% were resistant to ≥5 antibiotic classes, and 50% were resistant to none. Similar resistance patterns were observed over time and by care setting. Conclusions: We observed a slight decrease in both class-specific antimicrobial resistance and MDR of UPEC overall, most commonly involving penicillins and TMP-SMX. Resistance patterns were consistent over time and similar in both in-person and virtual settings. Virtual healthcare may expand access to UTI care.

Ambient temperature and risk of urinary tract infection in California: A time-stratified case-crossover study using electronic health records.

BACKGROUND: In the United States (US), urinary tract infections (UTI) lead to more than 10 million office visits each year. Temperature and season are potentially important risk factors for UTI, particularly in the context of climate change. METHODS: We examined the relationship between ambient temperature and outpatient UTI diagnoses among patients followed from 2015 to 2017 in two California healthcare systems: Kaiser Permanente Southern California (KPSC) and Sutter Health in Northern California. We identified UTI diagnoses in adult patients using diagnostic codes and laboratory records from electronic health records. We abstracted patient age, sex, season of diagnosis, and linked community-level Index of Concentration at the Extremes (ICE-I, a measure of wealth and poverty concentration) based on residential address. Daily county-level average ambient temperature was assembled from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). We implemented distributed lag nonlinear models (DLNM) to assess the association between UTI and lagged daily temperatures. Main analyses were confined to women. In secondary analyses, we stratified by season, healthcare system, and community-level ICE-I. RESULTS: We observed 787,186 UTI cases (89% among women). We observed a threshold association between ambient temperature and UTI among women: an increase in daily temperature from the 5th percentile (6.0 ˚C) to the mean (16.2 ˚C) was associated with a 3.2% (95% CI: 2.4, 3.9%) increase in same-day UTI diagnosis rate, whereas an increase from the mean to 95th percentile was associated with no change in UTI risk (0.0%, 95% CI: -0.7, 0.6%). In secondary analyses, we observed the clearest monotonic increase in the rate of UTI diagnosis with higher temperatures in the fall. Associations did not differ meaningfully by healthcare system or community-level ICE-I. Results were robust to alternate model specifications. DISCUSSION: Increasing temperature was related to higher rate of outpatient UTI, particularly in the shoulder seasons (spring, autumn).

Sociodemographic Inequalities in Urinary Tract Infection in 2 Large California Health Systems.

BACKGROUND: Urinary tract infection (UTI) accounts for a substantial portion of outpatient visits and antibiotic prescriptions in the United States. Few studies have considered sociodemographic factors including low socioeconomic status (SES)-which may increase residential crowding, inappropriate antibiotic prescribing, or comorbidities-as UTI or multidrug-resistant (MDR) UTI risk factors. METHODS: We used 2015-2017 electronic health record data from 2 California health care systems to assess whether 3 sociodemographic factors-use of Medicaid, use of an interpreter, and census tract-level deprivation-were associated with overall UTI or MDR UTI. UTIs resistant to ≥3 antibiotic classes were considered MDR. RESULTS: Analyses included 601 352 UTI cases, 1 303 455 controls, and 424 977 urinary Escherichia coli isolates from Kaiser Permanente Southern California (KPSC) and Sutter Health in Northern California. The MDR prevalence was 10.4% at KPSC and 12.8% at Sutter Health. All 3 sociodemographic factors (ie, use of Medicaid, using an interpreter, and community deprivation) were associated increased risk of MDR UTI. For example, using an interpreter was associated with a 36% (relative risk [RR], 1.36; 95% CI, 1.31 to 1.40) and 28% (RR, 1.28; 95% CI, 1.22 to 1.34) increased risk of MDR UTI at KPSC and Sutter Health, respectively, adjusted for SES and other potential confounding variables. The 3 sociodemographic factors were only weakly associated with UTI overall. CONCLUSIONS: We found low SES and use of an interpreter to be novel risk factors for MDR UTI in the United States.