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Systemically identifying caregivers in the electronic health record (EHR) is a critical step for delivering patient-centered care, enhancing care coordination, and advancing research and population health efforts in caregiving. Despite EHRs being effective in identifying patients through standardized data fields like demographics, laboratory results, medications, and diagnoses, identifying caregivers through the EHR is challenging in the absence of specific caregiver fields. Recognizing the complexity of identifying caregiving networks of people living with dementia, this study aims to systematically capture caregiver information by combining EHR structured fields, unstructured notes, and free text. Among a cohort of people living with dementia aged 60 years and older from Kaiser Permanente Colorado, caregiver names were identified by combining structured patient contact fields, that is, known contacts, with name-matching and natural language processing techniques of unstructured notes and patient portal messages containing caregiver terms. Among the cohort of 789 people living with dementia, 95% (n=749) had at least 1 caregiver name listed in structured fields (mean 2.1 SD 1.1). Over 95% of the cohort had caregiver terms mentioned near a known contact name in unstructured encounter notes, with 35% having a full name match in unstructured patient portal messages. The natural language processing model identified 7556 \"new\" names in the unstructured EHR text containing caregiver terms among 99% of the cohort with high accuracy and reliability (F -score=.85; precision=.89; recall=.82). Overall, 87% of the cohort had a new name identified ≥2 near a caregiver term in their notes and portal messages. Patterns in caregiver-related information were distributed across structured and unstructured EHR fields, emphasizing the importance of integrating both data sources for a comprehensive understanding of caregiving networks. A framework was developed to systematically identify potential caregivers across caregiving networks using structured and unstructured EHR data. This approach has the potential to improve health services for people living with dementia and their caregivers.

Retention in hypertension care, medication adherence, and blood pressure (BP) may have been affected by the COVID‐19 pandemic. In a retrospective cohort study of 64 766 individuals with treated hypertension from an integrated health care system, we compared hypertension care during the year pre‐COVID‐19 (March 2019–February 2020) and the first year of COVID‐19 (March 2020–February 2021). Retention in hypertension care was defined as receiving clinical BP measurements during COVID‐19. Medication adherence was measured using prescription refills. Clinical care was assessed by in‐person and virtual visits and changes in systolic and diastolic BP. The cohort had a mean age of 67.8 (12.2) years, 51.2% were women, and 73.5% were White. In 60 757 individuals with BP measurements pre‐COVID‐19, 16618 (27.4%) had no BP measurements during COVID‐19. Medication adherence declined from 86.0% to 80.8% (p < .001). In‐person primary care visits decreased from 2.7 (2.7) to 1.4 (1.9) per year, while virtual contacts increased from 9.5 (12.2) to 11.2 (14.2) per year (both p < .001). Among individuals with BP measurements, mean (SD) systolic BP was 126.5 mm Hg (11.8) pre‐COVID‐19 and 127.3 mm Hg (12.6) during COVID‐19 (p = .14). Mean diastolic BP was 73.5 mm Hg (8.5) pre‐COVID‐19 and 73.5 mm Hg (8.7) during COVID‐19 (p = .77). Even in this integrated health care system, many individuals did not receive clinical BP monitoring during COVID‐19. Most individuals who remained in care maintained pre‐COVID BP. Targeted outreach may be necessary to restore care continuity and hypertension control at the population level.

BackgroundBoth hyperkalemia and hypokalemia can lead to cardiac arrhythmias and are associated with increased mortality. Information on the predictors of potassium in individuals with diabetes in routine clinical practice is lacking.ObjectiveTo identify predictors of hyperkalemia and hypokalemia in adults with diabetes.DesignRetrospective cohort study, with classification and regression tree (CART) analysis.Participants321,856 individuals with diabetes enrolled in four large integrated health care systems from 2012 to 2013.Main MeasuresWe used a single serum potassium result collected in 2012 or 2013. Hyperkalemia was defined as a serum potassium ≥ 5.5 mEq/L and hypokalemia as < 3.5 mEq/L. Predictors included demographic factors, laboratory measurements, comorbidities, medication use, and health care utilization.Key ResultsThere were 2556 hypokalemia events (0.8%) and 1517 hyperkalemia events (0.5%). In univariate analyses, we identified concordant predictors (associated with increased probability of both hyperkalemia and hypokalemia), discordant predictors, and predictors of only hyperkalemia or hypokalemia. In CART models, the hyperkalemia “tree” had 5 nodes and a c-statistic of 0.76. The nodes were defined by prior potassium results and eGFRs, and the 5 terminal “leaves” had hyperkalemia probabilities of 0.2 to 7.2%. The hypokalemia tree had 4 nodes and a c-statistic of 0.76. The hypokalemia tree included nodes defined by prior potassium results, and the 4 terminal leaves had hypokalemia probabilities of 0.3 to 17.6%. Individuals with a recent potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no hypokalemia in the previous year had a < 1% rate of either hypokalemia or hyperkalemia.ConclusionsThe yield of routine serum potassium testing may be low in individuals with a recent serum potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no recent history of hypokalemia. We did not examine the effect of recent changes in clinical condition or medications on acute potassium changes.

Background As more states legalize cannabis for medical and recreational use, people increasingly use cannabis to treat medical conditions and associated symptoms. The prevalence and utility of cannabis for cancer-related symptoms may be clarified by examining cannabis use among patients with a common cancer diagnosis. We aimed to determine the prevalence of cannabis use among colorectal cancer (CRC) survivors and its associations with quality of life (QoL) and cancer-related symptomatology. Methods A cross-sectional survey of patient-reported QoL outcomes and behaviors, including cannabis use, was conducted within the Patient Outcomes To Advance Learning network’s (PORTAL) CRC Cohort. The cohort included a population-based sample of healthcare system members ≥18 years old diagnosed with adenocarcinoma of the colon or rectum from 2010 through 2016. We assessed the association between cannabis use and QoL using the European Organization for Research and Treatment of Cancer QLQ-C30 summary score. Results Of the 1784 respondents, 293 (16.4%) reported cannabis use following CRC diagnosis. Current tobacco smokers were more likely to use cannabis compared to former or never tobacco smokers (adjusted odds ratio [aOR] 2.71, 95% confidence interval [CI] 1.56 to 4.70). Greater alcohol use (> 4 drinks per month versus ≤4 drinks per month) was associated with cannabis use (aOR 2.17, 95% CI 1.65 to 2.85). There was an association between cannabis use and cancer stage at diagnosis, with stage 3 or 4 CRC patients more likely to use cannabis than stage 1 or 2 CRC patients (aOR 1.68, 95% CI 1.25 to 2.25). After adjusting for demographics, medical comorbidities, stage and site of CRC diagnosis, and prescription opioid use, people who used cannabis had significantly lower QoL than people who did not use cannabis (difference of − 6.14, 95% CI − 8.07 to − 4.20). Conclusion Among CRC survivors, cannabis use was relatively common, associated with more advanced stages of disease, associated with tobacco and alcohol use, and not associated with better QoL. Clinicians should inquire about cannabis use among their patients and provide evidence-based recommendations for cancer-related symptoms.

Purpose Our objective was to describe differences in treatment patterns and survival between early-onset (< 50 years old) and late-onset colorectal cancer (CRC) patients in community-based health systems. Methods We used tumor registry and electronic health record data to identify and characterize patients diagnosed with adenocarcinoma of the colon or rectum from 2010 to 2014 at six US health systems in the patient outcomes to advance learning (PORTAL) network. We used logistic regression to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing the distribution of tumor characteristics and treatment patterns in early-onset versus late-onset CRC. Cox regression models were used to estimate adjusted hazard ratios (HRs) and CIs comparing survival between early- and late-onset CRC patients. Results There were 1,424 early-onset and 10,810 late-onset CRC cases in our analyses. Compared to late-onset CRC, early-onset CRC was significantly associated with advanced-stage disease, high-grade histology, signet ring histology, and rectal or left colon location. After adjusting for differences in tumor and patient characteristics, early-onset patients were more likely than late-onset patients to have > 12 lymph nodes examined (OR 1.60, CI 1.37–1.87), to receive systemic therapy (chemotherapy or immunotherapy) within 6 months of diagnosis (OR 2.84, CI 2.40–3.37), and to have a reduced risk of CRC-specific death (HR 0.66, CI 0.56–0.79). Conclusions Early-onset CRC is associated with aggressive tumor characteristics, distal location, and systemic therapy use. Despite some adverse risk factors, these patients tend to have better survival than older onset patients.

Purpose The goal of this study was to determine response rates and associated costs of different survey methods among colorectal cancer (CRC) survivors. Methods We assembled a cohort of 16,212 individuals diagnosed with CRC (2010–2014) from six health plans, and randomly selected 4000 survivors to test survey response rates across four mixed-mode survey administration protocols (in English and Spanish): arm 1, mailed survey with phone follow-up; arm 2, interactive voice response (IVR) followed by mail; arm 3; email linked to web-based survey with mail follow-up; and arm 4, email linked to web-based survey followed by IVR. Results Our overall response rate was 50.2%. Arm 1 had the highest response rate (59.9%), followed by arm 3 (51.9%), arm 2 (51.2%), and arm 4 (37.9%). Response rates were higher among non-Hispanic whites in all arms than other racial/ethnic groups ( p  < 0.001), among English (51.5%) than Spanish speakers (36.4%) ( p  < 0.001), and among higher (53.7%) than lower (41.4%) socioeconomic status ( p  < 0.001). Survey arms were roughly comparable in cost, with a difference of only 8% of total costs between the most (arm 2) and least (arm 3) expensive arms. Conclusions Mailed surveys followed by phone calls achieved the highest response rate; email invitations and online surveys cost less per response. Electronic methods, even among those with email availability, may miss important populations including Hispanics, non-English speakers, and those of lower socioeconomic status. Implications for cancer survivors Our results demonstrate effective methods for capturing patient-reported outcomes, inform the relative benefits/disadvantages of the different methods, and identify future research directions.

Diabetes is a leading cause of chronic kidney disease (CKD). Different methods of CKD ascertainment may impact prevalence estimates. We used data from 11 integrated health systems in the United States to estimate CKD prevalence in adults with diabetes (2005–2011), and compare the effect of different ascertainment methods on prevalence estimates. We used the SUPREME-DM DataLink (n=879,312) to estimate annual CKD prevalence. Methods of CKD ascertainment included: diagnosis codes alone, impaired estimated glomerular filtration rate (eGFR) alone (eGFR<60mL/min/1.73m2), albuminuria alone (spot urine albumin creatinine ratio>30mg/g or equivalent), and combinations of these approaches. CKD prevalence was 20.0% using diagnosis codes, 17.7% using impaired eGFR, 11.9% using albuminuria, and 32.7% when one or more method suggested CKD. The criteria had poor concordance. After age- and sex-standardization to the 2010 U.S. Census population, prevalence using diagnosis codes increased from 10.7% in 2005 to 14.3% in 2011 (P<0.001). The prevalence using eGFR decreased from 9.7% in 2005 to 8.6% in 2011 (P<0.001). Our data indicate that CKD prevalence and prevalence trends differ according to the CKD ascertainment method, highlighting the necessity for multiple sources of data to accurately estimate and track CKD prevalence.

BACKGROUND Although combustible cigarette use is an established risk factor for severe COVID-19 disease, there is conflicting evidence for the association of electronic cigarette use with SARS-CoV-2 infection and COVID-19 disease severity. METHODS Study participants were from the Kaiser Permanente Research Bank (KPRB), a biorepository that includes adult Kaiser Permanente members from across the United States. Starting in April 2020, electronic surveys were sent to KPRB members to assess the impact of the COVID-19 pandemic. These surveys collected information on self-report of SARS-CoV-2 infection and COVID-related risk factors, including electronic cigarette and combustible cigarette smoking history. We also used electronic health records data to assess COVID-19 diagnoses, positive PCR lab tests, hospitalizations, and death. We used multivariable Cox proportional hazards regression to calculate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) comparing the risk of SARS-CoV-2 infection between individuals by e-cigarette use categories (never, former, and current). Among those with SARS-CoV-2 infection, we used multivariable logistic regression to estimate adjusted odds ratios (ORs) and 95% CIs comparing the odds of hospitalization or death within 30 days of infection between individuals by e-cigarette use categories. RESULTS There were 126,475 individuals who responded to the survey and completed questions on e-cigarette and combustible cigarette use (48% response rate). Among survey respondents, 819 (1%) currently used e-cigarettes, 3,691 (3%) formerly used e-cigarettes, and 121,965 (96%) had never used e-cigarettes. After adjustment for demographic, behavioral, and clinical factors, there was no association with SARS-CoV-2 infection and former e-cigarette use (hazard ratio (HR) = 0.99; CI: 0.83–1.18) or current e-cigarette use (HR = 1.08; CI: 0.76–1.52). Among those with SARS-CoV-2 infection, there was no association with hospitalization or death within 30 days of infection and former e-cigarette use (odds ratio (OR) = 1.19; CI: 0.59–2.43) or current e-cigarette use (OR = 1.02; CI: 0.22–4.74). CONCLUSIONS Our results suggest that e-cigarette use is not associated with an increased risk of SARS-CoV-2 infection or severe COVID-19 illness.

1. Overview The 10th Antarctic Meteorological Observation, Modeling, and Forecasting Workshop （hereinafter AMOMFW） took place June 17-19, 2015 in the historic city of Cambridge, United Kingdom. The meeting followed its purpose of connecting Antarctic atmospheric science to weatherrelated operational issues and advances in observing, modeling, forecasting, and understanding the Antarctic environment.