BACKGROUND:Stopping or reducing risky or unneeded medications ("deprescribing") could improve older adults' health. Electronic health data can support observational and intervention studies of deprescribing, but there are no standardized measures for key variables, and healthcare systems have differing data types and availability. We developed definitions for chronic medication use and discontinuation based on electronic health data and applied them in a case study of benzodiazepines and Z-drugs in five diverse US healthcare systems. METHODS:We conducted a retrospective cohort study of adults age 65+ from 2017 to 2019 with chronic benzodiazepine or Z-drug use. We determined whether sites had access to medication orders and/or dispensings. We developed definitions for chronic use and discontinuation using both data types. Discontinuation definitions were based on (1) gaps in medication availability during follow-up or (2) not having medication available at a fixed time point. We examined the impact of varying the gap length and requiring a 30-day period without orders/dispensings ("halo") around the fixed time point. We compared results derived from orders versus dispensings at one site. RESULTS:Approximately 1.6%-2.6% of older adults had chronic benzodiazepine/Z-drug use (total N = 6775, ranging from 431 to 2122 across sites). Depending on the definition and site, the proportion discontinuing use during 12 months ranged from 6% to 49%. Requiring a longer gap or a 30-day "halo" resulted in lower estimates. At one site, only 56% of those with chronic use defined from orders also qualified based on dispensings, and the discontinuation rate at 180 days was 20% from orders versus 32% from dispensings. CONCLUSIONS:Requiring a gap of ≥90 days or a "halo" around a time point may more accurately capture discontinuation than using a shorter gap or no halo. Orders data underestimate discontinuation compared to dispensings. Work is needed to adapt these definitions for other drug classes and settings.

Background Efficiently identifying patients with human immunodeficiency virus (HIV) using administrative health care data (e.g., claims) can facilitate research on their quality of care and health outcomes. No prior study has validated the use of only ICD-10-CM HIV diagnosis codes to identify patients with HIV. Methods We validated HIV diagnosis codes among women enrolled in a large U.S. integrated health care system during 2010–2020. We examined HIV diagnosis code-based algorithms that varied by type, frequency, and timing of the codes in patients’ claims data. We calculated the positive predictive values (PPVs) and 95% confidence intervals (CIs) of the algorithms using a medical record-confirmed diagnosis of HIV as the gold standard. Results A total of 272 women with ≥ 1 HIV diagnosis code in the administrative claims data were identified and medical records were reviewed for all 272 women. The PPV of an algorithm classifying women as having HIV as of the first HIV diagnosis code during the observation period was 80.5% (95% CI: 75.4–84.8%), and it was 93.9% (95% CI: 90.0-96.3%) as of the second. Little additional increase in PPV was observed when a third code was required. The PPV of an algorithm based on ICD-10-CM-era codes was similar to one based on ICD-9-CM-era codes. Conclusion If the accuracy measure of greatest interest is PPV, our findings suggest that use of ≥ 2 HIV diagnosis codes to identify patients with HIV may perform well. However, health care coding practices may vary across settings, which may impact generalizability of our results.

Benzodiazepines and z-drugs increase the fall risk in older adults. There is a lack of real-world data examining the association between falls and deprescribing medications. In a retrospective cohort study of older adults with chronic benzodiazepine or z-drug use receiving care at an academic health system from January 2017 to December 2020, we explored the association between medication discontinuation and falls. Chronic use was defined as ≥ 3 medication dispensings and cumulative days’ supply ≥ 45 days within 100 days in 2018. Discontinuation was defined as a dispensing gap of ≥ 180 days within 1 year of chronic use eligibility, with a secondary definition requiring a gap of ≥ 90 days. Non-discontinuers (n = 524) were matched 4:1 to discontinuers (n = 131) if they had a fill in the same month as the matched discontinuation index date. The association between discontinuation and a fall during 2.25-year follow-up was assessed using Cox proportional hazards models. The analysis was repeated using a gap of ≥ 90 days (n = 279 discontinuers; 1116 matched non-discontinuers). The cumulative incidence of falls-related acute visits was 6.9

Evidence suggests fetal risks are associated with cannabis use during pregnancy. Yet, insights into women's decision-making and cannabis use during pregnancy are limited. This study explored these concepts with postpartum women who used cannabis during and after pregnancy. We conducted interviews with 15 women (4 self-identifying a race other than White and 4 self-identifying Hispanic ethnicity) who: 1) lived in the Puget Sound region of Washington State, 2) reported past-year cannabis use on a routine screen, and 3) had documented pregnancy and delivery March 2015-May 2017. Semi-structured interviews asked about decision-making and cannabis use during pregnancy and postpartum. We used template analysis for coding and analysis. The key findings included that women: 1) gathered information about cannabis use during pregnancy primarily through internet searches and discussions with peers; 2) were reluctant to talk with health care providers about cannabis; 3) used cannabis while pregnant to treat health issues, including morning sickness, pain, and mental health conditions; 4) were comfortable with their decision to use cannabis while pregnant, but had questions about long-term effects; and 5) tried to mitigate transmission through breastmilk. Women decided about cannabis during pregnancy based on their experience, health symptoms, and information gathered from the internet and peers, often without guidance from their health care provider. Results point to opportunities for providers to become informed about and engage in discussion with patients about cannabis use during preconception, pregnancy, and postpartum.

Little is known about whether diabetes increases the risk of COVID-19 infection and whether measures of diabetes severity are related to COVID-19 outcomes. Investigate diabetes severity measures as potential risk factors for COVID-19 infection and COVID-19 outcomes. In integrated healthcare systems in Colorado, Oregon, and Washington, we identified a cohort of adults on February 29, 2020 (n = 1,086,918) and conducted follow-up through February 28, 2021. Electronic health data and death certificates were used to identify markers of diabetes severity, covariates, and outcomes. Outcomes were COVID-19 infection (positive nucleic acid antigen test, COVID-19 hospitalization, or COVID-19 death) and severe COVID-19 (invasive mechanical ventilation or COVID-19 death). Individuals with diabetes (n = 142,340) and categories of diabetes severity measures were compared with a referent group with no diabetes (n = 944,578), adjusting for demographic variables, neighborhood deprivation index, body mass index, and comorbidities. Of 30,935 patients with COVID-19 infection, 996 met the criteria for severe COVID-19. Type 1 (odds ratio [OR] 1.41, 95% CI 1.27–1.57) and type 2 diabetes (OR 1.27, 95% CI 1.23–1.31) were associated with increased risk of COVID-19 infection. Insulin treatment was associated with greater COVID-19 infection risk (OR 1.43, 95% CI 1.34–1.52) than treatment with non-insulin drugs (OR 1.26, 95% 1.20–1.33) or no treatment (OR 1.24; 1.18–1.29). The relationship between glycemic control and COVID-19 infection risk was dose-dependent: from an OR of 1.21 (95% CI 1.15–1.26) for hemoglobin A1c (HbA1c) < 7% to an OR of 1.62 (95% CI 1.51–1.75) for HbA1c ≥ 9%. Risk factors for severe COVID-19 were type 1 diabetes (OR 2.87; 95% CI 1.99–4.15), type 2 diabetes (OR 1.80; 95% CI 1.55–2.09), insulin treatment (OR 2.65; 95% CI 2.13–3.28), and HbA1c ≥ 9% (OR 2.61; 95% CI 1.94–3.52). Diabetes and greater diabetes severity were associated with increased risks of COVID-19 infection and worse COVID-19 outcomes.

COVID-19 inequities have been well-documented. We evaluated whether higher rates of severe COVID-19 in racial and ethnic minority groups were driven by higher infection rates by evaluating if disparities remained when analyses were restricted to people with infection. We conducted a retrospective cohort study of adults insured through Kaiser Permanente (Colorado, Northwest, Washington), follow-up in March-September 2020. Laboratory results and hospitalization diagnosis codes identified individuals with COVID-19. Severe COVID-19 was defined as invasive mechanical ventilation or mortality. Self-reported race and ethnicity, demographics, and medical comorbidities were extracted from health records. Modified Poisson regression estimated adjusted relative risks (aRRs) of severe COVID-19 in full cohort and among individuals with infection. Our cohort included 1,052,774 individuals, representing diverse racial and ethnic minority groups (e.g., 68,887 Asian, 41,243 Black/African American, 93,580 Hispanic or Latino/a individuals). Among 7,399 infections, 442 individuals experienced severe COVID-19. In the full cohort, severe COVID-19 aRRs for Asian, Black/African American, and Hispanic individuals were 2.09 (95% CI: 1.36, 3.21), 2.02 (1.39, 2.93), and 2.09 (1.57, 2.78), respectively, compared to non-Hispanic Whites. In analyses restricted to individuals with COVID-19, all aRRs were near 1, except among Asian Americans (aRR 1.82 [1.23, 2.68]). These results indicate increased incidence of severe COVID-19 among Black/African American and Hispanic individuals is due to higher infection rates, not increased susceptibility to progression. COVID-19 disparities most likely result from social, not biological, factors. Future work should explore reasons for increased severe COVID-19 risk among Asian Americans. Our findings highlight the importance of equity in vaccine distribution.

Purpose To estimate prevalence of prescription opioid use during pregnancy in eight US health plans during 2001-2014. Methods We conducted a cohort study of singleton live birth deliveries. Maternal characteristics were ascertained from health plan and/or birth certificate data and opioids dispensed during pregnancy from health plan pharmacy records. Prevalence of prescription opioid use during pregnancy was calculated for any use, cumulative days of use, and number of dispensings. Results We examined prevalence of prescription opioid use during pregnancy in each health plan. Tennessee Medicaid had appreciably greater prevalence of use compared to the seven other health plans. Thus, results for the two groups were reported separately. In the seven health plans (n = 587 093 deliveries), prevalence of use during pregnancy was relatively stable at 9%-11% throughout 2001-2014. In Tennessee Medicaid (n = 256 724 deliveries), prevalence increased from 29% in 2001 to a peak of 36%-37% in 2004-2010, and then declined to 28% in 2014. Use for >= 30 days during pregnancy was stable at 1% in the seven health plans and increased from 2% to 7% in Tennessee Medicaid during 2001-2014. Receipt of >= 5 opioid dispensings during pregnancy increased in the seven health plans (0.3%-0.6%) and Tennessee Medicaid (3%-5%) during 2001-2014. Conclusion During 2001-2014, prescription opioid use during pregnancy was more common in Tennessee Medicaid (peak prevalence in late 2000s) compared to the seven health plans (relatively stable prevalence). Although a small percentage of women had opioid use during pregnancy for >= 30 days or >= 5 dispensings, they represent thousands of women during 2001-2014.

Objective: Opioid surveillance in response to the opioid epidemic will benefit from scalable, automated algorithms for identifying patients with clinically documented signs of problem prescription opioid use. Existing algorithms lack accuracy. We sought to develop a high-sensitivity, high-specificity classification algorithm based on widely available structured health data to identify patients receiving chronic extended-release/long-acting (ER/LA) therapy with evidence of problem use to support subsequent epidemiologic investigations. Methods: Outpatient medical records of a probability sample of 2,000 Kaiser Permanente Washington patients receiving >= 60 days' supply of ER/LA opioids in a 90-day period from 1 January 2006 to 30 June 2015 were manually reviewed to determine the presence of clinically documented signs of problem use and used as a reference standard for algorithm development. Using 1,400 patients as training data, we constructed candidate predictors from demographic, enrollment, encounter, diagnosis, procedure, and medication data extracted from medical claims records or the equivalent from electronic health record (EHR) systems, and we used adaptive least absolute shrinkage and selection operator (LASSO) regression to develop a model. We evaluated this model in a comparable 600-patient validation set. We compared this model to ICD-9 diagnostic codes for opioid abuse, dependence, and poisoning. This study was registered with ClinicalTrials.gov as study NCT02667262 on 28 January 2016. Results: We operationalized 1,126 potential predictors characterizing patient demographics, procedures, diagnoses, timing, dose, and location of medication dispensing. The final model incorporating 53 predictors had a sensitivity of 0.582 at positive predictive value (PPV) of 0.572. ICD-9 codes for opioid abuse, dependence, and poisoning had a sensitivity of 0.390 at PPV of 0.599 in the same cohort. Conclusions: Scalable methods using widely available structured EHR/claims data to accurately identify problem opioid use among patients receiving long-term ER/LA therapy were unsuccessful. This approach may be useful for identifying patients needing clinical evaluation.

Primary care providers prescribe most long-term opioid therapy and are increasingly asked to taper the opioid doses of these patients to safer levels. A recent systematic review suggests that multiple interventions may facilitate opioid taper, but many of these are not feasible within the usual primary care practice. To determine if opioid taper plans documented by primary care providers in the electronic health record are associated with significant and sustained opioid dose reductions among patients on long-term opioid therapy. A nested case-control design was used to compare cases (patients with a sustained opioid taper defined as average daily opioid dose of ≤ 30 mg morphine equivalent (MME) or a 50% reduction in MME) to controls (patients matched to cases on year and quarter of cohort entry, sex, and age group, who had not achieved a sustained taper). Each case was matched with four controls. Two thousand four hundred nine patients receiving a ≥ 60-day supply of opioids with an average daily dose of ≥ 50 MME during 2011–2015. Opioid taper plans documented in prescription instructions or clinical notes within the electronic health record identified through natural language processing; opioid dosing, patient characteristics, and taper plan components also abstracted from the electronic health record. Primary care taper plans were associated with an increased likelihood of sustained opioid taper after adjusting for all patient covariates and near peak dose (OR = 3.63 [95% CI 2.96–4.46], p < 0.0001). Both taper plans in prescription instructions (OR = 4.03 [95% CI 3.19–5.09], p < 0.0001) and in clinical notes (OR = 2.82 [95% CI 2.00–3.99], p < 0.0001) were associated with sustained taper. These results suggest that planning for opioid taper during primary care visits may facilitate significant and sustained opioid dose reduction.

PURPOSE:The use of validated criteria to identify birth defects in electronic healthcare databases can avoid the cost and time-intensive efforts required to conduct chart reviews to confirm outcomes. This study evaluated the validity of various case-finding methodologies to identify neural tube defects (NTDs) in infants using an electronic healthcare database. METHODS:This analysis used data generated from a study whose primary aim was to evaluate the association between first-trimester maternal prescription opioid use and NTDs. The study was conducted within the Medication Exposure in Pregnancy Risk Evaluation Program. A broad approach was used to identify potential NTDs including diagnosis and procedure codes from inpatient and outpatient settings, death certificates and birth defect flags in birth certificates. Potential NTD cases were chart abstracted and confirmed by clinical experts. Positive predictive values (PPVs) and 95% confidence intervals (95% CI) are reported. RESULTS:The cohort included 113 168 singleton live-born infants: 55 960 infants with opioid exposure in pregnancy and 57 208 infants unexposed in pregnancy. Seventy-three potential NTD cases were available for the validation analysis. The overall PPV was 41% using all diagnosis and procedure codes plus birth certificates. Restricting approaches to codes recorded in the infants' medical record or to birth certificate flags increased the PPVs (72% and 80%, respectively) but missed a substantial proportion of confirmed NTDs. CONCLUSIONS:Codes in electronic healthcare data did not accurately identify confirmed NTDs. These results indicate that chart review with adjudication of outcomes is important when conducting observational studies of NTDs using electronic healthcare data.

There are plausible mechanisms by which angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of COVID-19 infection or affect disease severity. To examine the association between these medications and COVID-19 infection or hospitalization, we conducted a retrospective cohort study within a US integrated healthcare system. Among people aged ≥18 years enrolled in the health plan for at least 4 months as of 2/29/2020, current ACEI and ARB use was identified from pharmacy data, and the estimated daily dose was calculated and standardized across medications. COVID-19 infections were identified through 6/14/2020 from laboratory and hospitalization data. We used logistic regression to estimate adjusted odds ratios (ORs) and 95% confidence intervals. Among 322,044 individuals, 720 developed COVID-19 infection. Among people using ACEI/ARBs, 183/56,105 developed COVID-19 (3.3 per 1000 individuals) compared with 537/265,939 without ACEI/ARB use (2.0 per 1000), yielding an adjusted OR of 0.94 (95% CI 0.75-1.16). For use of < 1 defined daily dose vs. nonuse, the adjusted OR for infection was 0.89 (95% CI 0.62-1.26); for 1 to < 2 defined daily doses, 0.97 (95% CI 0.71-1.31); and for ≥2 defined daily doses, 0.94 (95% CI 0.72-1.23). The OR was similar for ACEIs and ARBs and in subgroups by age and sex. 29% of people with COVID-19 infection were hospitalized; the adjusted OR for hospitalization in relation to ACEI/ARB use was 0.92 (95% CI 0.54-1.57), and there was no association with dose. These findings support current recommendations that individuals on these medications continue their use.

Abstract Background Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of coronavirus disease 2019 (COVID-19) infection or affect disease severity. Prior studies have not examined risks by medication dose. Methods This retrospective cohort study included people aged ≥18 years enrolled in a US integrated healthcare system for at least 4 months as of 2/29/2020. Current ACEI and ARB use was identified from pharmacy data, and the estimated daily dose was calculated and standardized across medications. COVID-19 infections and hospitalizations were identified through 6/14/2020 from laboratory and hospitalization data. We used logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for race/ethnicity, obesity, and other covariates. Results Among 322,044 individuals, 826 developed COVID-19 infection. Among people using ACEI/ARBs, 204/56,105 developed COVID-19 (3.6 per 1,000 individuals) compared with 622/265,939 without ACEI/ARB use (2.3 per 1,000), yielding an adjusted OR of 0.91 (95% CI 0.74–1.12). For use of <1 defined daily dose (DDD) vs. nonuse, the adjusted OR for infection was 0.92 (95% CI 0.66–1.28); for 1 to <2 DDDs, 0.89 (95% CI 0.66–1.19); and for ≥2 DDDs, 0.92 (95% CI 0.72–1.18). The OR was similar for ACEIs and ARBs and in subgroups by age and sex. 26% of people with COVID-19 infection were hospitalized; the adjusted OR for hospitalization in relation to ACEI/ARB use was 0.98 (95% CI 0.63–1.54), and there was no association with dose. Conclusions These findings support current recommendations that individuals on these medications continue their use.

Background The eMERGE III Network was tasked with harmonizing genetic testing protocols linking multiple sites and investigators.Methods DNA capture panels targeting 109 genes and 1551 variants were constructed by two clinical sequencing centers for analysis of 25,000 participant DNA samples collected at 11 sites where samples were linked to patients with electronic health records. Each step from sample collection, data generation, interpretation, reporting, delivery and storage, were developed and validated in CAP/CLIA settings and harmonized across sequencing centers.Results A compliant and secure network was built and enabled ongoing review and reconciliation of clinical interpretations while maintaining communication and data sharing between investigators. Mechanisms for sustained propagation and growth of the network were established. An interim data freeze representing 15,574 sequenced subjects, informed the assay performance for a range of variant types, the rate of return of results for different phenotypes and the frequency of secondary findings. Practical obstacles for implementation and scaling of clinical and research findings were identified and addressed. The eMERGE protocols and tools established are now available for widespread dissemination.Conclusions This study established processes for different sequencing sites to harmonize the technical and interpretive aspects of sequencing tests, a critical achievement towards global standardization of genomic testing. The network established experience in the return of results and the rate of secondary findings across diverse biobank populations. Furthermore, the eMERGE network has accomplished integration of structured genomic results into multiple electronic health record systems, setting the stage for clinical decision support to enable genomic medicine.