Factors associated with COVID-19-related death using OpenSAFELY.

Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY-a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53-1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29-1.69) and 1.45 (1.32-1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly.

Contribution of Potentially Inappropriate Medications to Polypharmacy-Associated Risk of Mortality in Middle-Aged Patients: A National Cohort Study.

BACKGROUND: The role of potentially inappropriate medications (PIMs) in mortality has been studied among those 65 years or older. While middle-aged individuals are believed to be less susceptible to the harms of polypharmacy, PIMs have not been as carefully studied in this group. OBJECTIVE: To estimate PIM-associated risk of mortality and evaluate the extent PIMs explain associations between polypharmacy and mortality in middle-aged patients, overall and by sex and race/ethnicity. DESIGN: Observational cohort study. SETTING: Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. PARTICIPANTS: Patients aged 41 to 64 who received a chronic medication (continuous use of ≥ 90 days) between October 1, 2008, and September 30, 2017. MEASUREMENT: Patients were followed for 5 years until death or end of study period (September 30, 2019). Time-updated polypharmacy and hyperpolypharmacy were defined as 5-9 and ≥ 10 chronic medications, respectively. PIMs were identified using the Beers criteria (2015) and were time-updated. Cox models were adjusted for demographic, behavioral, and clinical characteristics. RESULTS: Of 733,728 patients, 676,935 (92.3%) were men, 479,377 (65.3%) were White, and 156,092 (21.3%) were Black. By the end of follow-up, 104,361 (14.2%) patients had polypharmacy, 15,485 (2.1%) had hyperpolypharmacy, and 129,992 (17.7%) were dispensed ≥ 1 PIM. PIMs were independently associated with mortality (HR 1.11, 95% CI 1.04-1.18). PIMs also modestly attenuated risk of mortality associated with polypharmacy (HR 1.07, 95% CI 1.03-1.11 before versus HR 1.05, 95% CI 1.01-1.09 after) and hyperpolypharmacy (HR 1.18, 95% CI 1.09-1.28 before versus HR 1.12, 95% CI 1.03-1.22 after). Patterns varied when stratified by sex and race/ethnicity. LIMITATIONS: The predominantly male VA patient population may not represent the general population. CONCLUSION: PIMs were independently associated with increased mortality, and partially explained polypharmacy-associated mortality in middle-aged people. Other mechanisms of injury from polypharmacy should also be studied.

Herpes Zoster and Risk of Incident Parkinson's Disease in US Veterans: A Matched Cohort Study.

BACKGROUND: Although some systemic infections are associated with Parkinson's disease (PD), the relationship between herpes zoster (HZ) and PD is unclear. OBJECTIVE: The objective is to investigate whether HZ is associated with incident PD risk in a matched cohort study using data from the US Department of Veterans Affairs. METHODS: We compared the risk of PD between individuals with incident HZ matched to up to five individuals without a history of HZ using Cox proportional hazards regression. In sensitivity analyses, we excluded early outcomes. RESULTS: Among 198,099 individuals with HZ and 976,660 matched individuals without HZ (median age 67.0 years (interquartile range [IQR 61.4-75.7]); 94% male; median follow-up 4.2 years [IQR 1.9-6.6]), HZ was not associated with an increased risk of incident PD overall (adjusted HR 0.95, 95% CI 0.90-1.01) or in any sensitivity analyses. CONCLUSION: We found no evidence that HZ was associated with increased risk of incident PD in this cohort. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

OpenSAFELY: A platform for analysing electronic health records designed for reproducible research.

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY's reproducibility-by-design approach in detail.

Challenges in Estimating the Effectiveness of COVID-19 Vaccination Using Observational Data.

The COVID-19 vaccines were developed and rigorously evaluated in randomized trials during 2020. However, important questions, such as the magnitude and duration of protection, their effectiveness against new virus variants, and the effectiveness of booster vaccination, could not be answered by randomized trials and have therefore been addressed in observational studies. Analyses of observational data can be biased because of confounding and because of inadequate design that does not consider the evolution of the pandemic over time and the rapid uptake of vaccination. Emulating a hypothetical "target trial" using observational data assembled during vaccine rollouts can help manage such potential sources of bias. This article describes 2 approaches to target trial emulation. In the sequential approach, on each day, eligible persons who have not yet been vaccinated are matched to a vaccinated person. The single-trial approach sets a single baseline at the start of the rollout and considers vaccination as a time-varying variable. The nature of the confounding depends on the analysis strategy: Estimating "per-protocol" effects (accounting for vaccination of initially unvaccinated persons after baseline) may require adjustment for both baseline and "time-varying" confounders. These issues are illustrated by using observational data from 2 780 931 persons in the United Kingdom aged 70 years or older to estimate the effect of a first dose of a COVID-19 vaccine. Addressing the issues discussed in this article should help authors of observational studies provide robust evidence to guide clinical and policy decisions.

Contribution of alcohol use in HIV/hepatitis C virus co-infection to all-cause and cause-specific mortality: A collaboration of cohort studies.

Among persons with HIV (PWH), higher alcohol use and having hepatitis C virus (HCV) are separately associated with increased morbidity and mortality. We investigated whether the association between alcohol use and mortality among PWH is modified by HCV. Data were combined from European and North American cohorts of adult PWH who started antiretroviral therapy (ART). Self-reported alcohol use data, collected in diverse ways between cohorts, were converted to grams/day. Eligible PWH started ART during 2001-2017 and were followed from ART initiation for mortality. Interactions between the associations of baseline alcohol use (0, 0.1-20.0, >20.0 g/day) and HCV status were assessed using multivariable Cox models. Of 58,769 PWH, 29,711 (51%), 23,974 (41%) and 5084 (9%) self-reported alcohol use of 0 g/day, 0.1-20.0 g/day, and > 20.0 g/day, respectively, and 4799 (8%) had HCV at baseline. There were 844 deaths in 37,729 person-years and 2755 deaths in 443,121 person-years among those with and without HCV, respectively. Among PWH without HCV, adjusted hazard ratios (aHRs) for mortality were 1.18 (95% CI: 1.08-1.29) for 0.0 g/day and 1.84 (1.62-2.09) for >20.0 g/day compared with 0.1-20.0 g/day. This J-shaped pattern was absent among those with HCV: aHRs were 1.00 (0.86-1.17) for 0.0 g/day and 1.64 (1.33-2.02) for >20.0 g/day compared with 0.1-20.0 g/day (interaction p < .001). Among PWH without HCV, mortality was higher in both non-drinkers and heavy drinkers compared with moderate alcohol drinkers. Among those with HCV, mortality was higher in heavy drinkers but not non-drinkers, potentially due to differing reasons for not drinking (e.g. illness) between those with and without HCV.

Enhanced Identification of Hispanic Ethnicity Using Clinical Data: A Study in the Largest Integrated United States Health Care System.

BACKGROUND: Collection of accurate Hispanic ethnicity data is critical to evaluate disparities in health and health care. However, this information is often inconsistently recorded in electronic health record (EHR) data. OBJECTIVE: To enhance capture of Hispanic ethnicity in the Veterans Affairs EHR and compare relative disparities in health and health care. METHODS: We first developed an algorithm based on surname and country of birth. We then determined sensitivity and specificity using self-reported ethnicity from the 2012 Veterans Aging Cohort Study survey as the reference standard and compared this to the research triangle institute race variable from the Medicare administrative data. Finally, we compared demographic characteristics and age-adjusted and sex-adjusted prevalence of conditions in Hispanic patients among different identification methods in the Veterans Affairs EHR 2018-2019. RESULTS: Our algorithm yielded higher sensitivity than either EHR-recorded ethnicity or the research triangle institute race variable. In 2018-2019, Hispanic patients identified by the algorithm were more likely to be older, had a race other than White, and foreign born. The prevalence of conditions was similar between EHR and algorithm ethnicity. Hispanic patients had higher prevalence of diabetes, gastric cancer, chronic liver disease, hepatocellular carcinoma, and human immunodeficiency virus than non-Hispanic White patients. Our approach evidenced significant differences in burden of disease among Hispanic subgroups by nativity status and country of birth. CONCLUSIONS: We developed and validated an algorithm to supplement Hispanic ethnicity information using clinical data in the largest integrated US health care system. Our approach enabled clearer understanding of demographic characteristics and burden of disease in the Hispanic Veteran population.

Spironolactone as a potential new pharmacotherapy for alcohol use disorder: convergent evidence from rodent and human studies.

Evidence suggests that spironolactone, a nonselective mineralocorticoid receptor (MR) antagonist, modulates alcohol seeking and consumption. Therefore, spironolactone may represent a novel pharmacotherapy for alcohol use disorder (AUD). In this study, we tested the effects of spironolactone in a mouse model of alcohol drinking (drinking-in-the-dark) and in a rat model of alcohol dependence (vapor exposure). We also investigated the association between spironolactone receipt for at least 60 continuous days and change in self-reported alcohol consumption, using the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C), in a pharmacoepidemiologic cohort study in the largest integrated healthcare system in the US. Spironolactone dose-dependently reduced the intake of sweetened or unsweetened alcohol solutions in male and female mice. No effects of spironolactone were observed on drinking of a sweet solution without alcohol, food or water intake, motor coordination, alcohol-induced ataxia, or blood alcohol levels. Spironolactone dose-dependently reduced operant alcohol self-administration in dependent and nondependent male and female rats. In humans, a greater reduction in alcohol consumption was observed among those who received spironolactone, compared to propensity score-matched individuals who did not receive spironolactone. The largest effects were among those who reported hazardous/heavy episodic alcohol consumption at baseline (AUDIT-C ≥ 8) and those exposed to ≥ 50 mg/day of spironolactone. These convergent findings across rodent and human studies demonstrate that spironolactone reduces alcohol use and support the hypothesis that this medication may be further studied as a novel pharmacotherapy for AUD.

Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV.

PURPOSE: Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to evaluate liver fat within digitized images have not been developed. We determined the accuracy of a deep learning algorithm (automatic liver attenuation region-of-interest-based measurement [ALARM]) to identify steatosis within clinically obtained noncontrast abdominal CT images compared to manual radiologist review and evaluated its performance by HIV status. METHODS: We performed a cross-sectional study to evaluate the performance of ALARM within noncontrast abdominal CT images from a sample of patients with and without HIV in the US Veterans Health Administration. We evaluated the ability of ALARM to identify moderate-to-severe hepatic steatosis, defined by mean absolute liver attenuation <40 Hounsfield units (HU), compared to manual radiologist assessment. RESULTS: Among 120 patients (51 PWH) who underwent noncontrast abdominal CT, moderate-to-severe hepatic steatosis was identified in 15 (12.5%) persons via ALARM and 12 (10%) by radiologist assessment. Percent agreement between ALARM and radiologist assessment of absolute liver attenuation <40 HU was 95.8%. Sensitivity, specificity, positive predictive value, and negative predictive value of ALARM were 91.7% (95%CI, 51.5%-99.8%), 96.3% (95%CI, 90.8%-99.0%), 73.3% (95%CI, 44.9%-92.2%), and 99.0% (95%CI, 94.8%-100%), respectively. No differences in performance were observed by HIV status. CONCLUSIONS: ALARM demonstrated excellent accuracy for moderate-to-severe hepatic steatosis regardless of HIV status. Application of ALARM to radiographic repositories could facilitate real-world studies to evaluate medications associated with steatosis and assess differences by HIV status.

Severity of Severe Acute Respiratory System Coronavirus 2 (SARS-CoV-2) Alpha Variant (B.1.1.7) in England.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) alpha variant (B.1.1.7) is associated with higher transmissibility than wild-type virus, becoming the dominant variant in England by January 2021. We aimed to describe the severity of the alpha variant in terms of the pathway of disease from testing positive to hospital admission and death. METHODS: With the approval of NHS England, we linked individual-level data from primary care with SARS-CoV-2 community testing, hospital admission, and Office for National Statistics all-cause death data. We used testing data with S-gene target failure as a proxy for distinguishing alpha and wild-type cases, and stratified Cox proportional hazards regression to compare the relative severity of alpha cases with wild-type diagnosed from 16 November 2020 to 11 January 2021. RESULTS: Using data from 185 234 people who tested positive for SARS-CoV-2 in the community (alpha = 93 153; wild-type = 92 081), in fully adjusted analysis accounting for individual-level demographics and comorbidities as well as regional variation in infection incidence, we found alpha associated with 73% higher hazards of all-cause death (adjusted hazard ratio [aHR]: 1.73; 95% confidence interval [CI]: 1.41-2.13; P < .0001) and 62% higher hazards of hospital admission (1.62; 1.48-1.78; P < .0001) compared with wild-type virus. Among patients already admitted to the intensive care unit, the association between alpha and increased all-cause mortality was smaller and the CI included the null (aHR: 1.20; 95% CI: .74-1.95; P = .45). CONCLUSIONS: The SARS-CoV-2 alpha variant is associated with an increased risk of both hospitalization and mortality than wild-type virus.

Overall and cause-specific hospitalisation and death after COVID-19 hospitalisation in England: A cohort study using linked primary care, secondary care, and death registration data in the OpenSAFELY platform.

BACKGROUND: There is concern about medium to long-term adverse outcomes following acute Coronavirus Disease 2019 (COVID-19), but little relevant evidence exists. We aimed to investigate whether risks of hospital admission and death, overall and by specific cause, are raised following discharge from a COVID-19 hospitalisation. METHODS AND FINDINGS: With the approval of NHS-England, we conducted a cohort study, using linked primary care and hospital data in OpenSAFELY to compare risks of hospital admission and death, overall and by specific cause, between people discharged from COVID-19 hospitalisation (February to December 2020) and surviving at least 1 week, and (i) demographically matched controls from the 2019 general population; and (ii) people discharged from influenza hospitalisation in 2017 to 2019. We used Cox regression adjusted for age, sex, ethnicity, obesity, smoking status, deprivation, and comorbidities considered potential risk factors for severe COVID-19 outcomes. We included 24,673 postdischarge COVID-19 patients, 123,362 general population controls, and 16,058 influenza controls, followed for ≤315 days. COVID-19 patients had median age of 66 years, 13,733 (56%) were male, and 19,061 (77%) were of white ethnicity. Overall risk of hospitalisation or death (30,968 events) was higher in the COVID-19 group than general population controls (fully adjusted hazard ratio [aHR] 2.22, 2.14 to 2.30, p < 0.001) but slightly lower than the influenza group (aHR 0.95, 0.91 to 0.98, p = 0.004). All-cause mortality (7,439 events) was highest in the COVID-19 group (aHR 4.82, 4.48 to 5.19 versus general population controls [p < 0.001] and 1.74, 1.61 to 1.88 versus influenza controls [p < 0.001]). Risks for cause-specific outcomes were higher in COVID-19 survivors than in general population controls and largely similar or lower in COVID-19 compared with influenza patients. However, COVID-19 patients were more likely than influenza patients to be readmitted or die due to their initial infection or other lower respiratory tract infection (aHR 1.37, 1.22 to 1.54, p < 0.001) and to experience mental health or cognitive-related admission or death (aHR 1.37, 1.02 to 1.84, p = 0.039); in particular, COVID-19 survivors with preexisting dementia had higher risk of dementia hospitalisation or death (age- and sex-adjusted HR 2.47, 1.37 to 4.44, p = 0.002). Limitations of our study were that reasons for hospitalisation or death may have been misclassified in some cases due to inconsistent use of codes, and we did not have data to distinguish COVID-19 variants. CONCLUSIONS: In this study, we observed that people discharged from a COVID-19 hospital admission had markedly higher risks for rehospitalisation and death than the general population, suggesting a substantial extra burden on healthcare. Most risks were similar to those observed after influenza hospitalisations, but COVID-19 patients had higher risks of all-cause mortality, readmission or death due to the initial infection, and dementia death, highlighting the importance of postdischarge monitoring.

Ethnic differences in SARS-CoV-2 infection and COVID-19-related hospitalisation, intensive care unit admission, and death in 17 million adults in England: an observational cohort study using the OpenSAFELY platform.

BACKGROUND: COVID-19 has disproportionately affected minority ethnic populations in the UK. Our aim was to quantify ethnic differences in SARS-CoV-2 infection and COVID-19 outcomes during the first and second waves of the COVID-19 pandemic in England. METHODS: We conducted an observational cohort study of adults (aged ≥18 years) registered with primary care practices in England for whom electronic health records were available through the OpenSAFELY platform, and who had at least 1 year of continuous registration at the start of each study period (Feb 1 to Aug 3, 2020 [wave 1], and Sept 1 to Dec 31, 2020 [wave 2]). Individual-level primary care data were linked to data from other sources on the outcomes of interest: SARS-CoV-2 testing and positive test results and COVID-19-related hospital admissions, intensive care unit (ICU) admissions, and death. The exposure was self-reported ethnicity as captured on the primary care record, grouped into five high-level census categories (White, South Asian, Black, other, and mixed) and 16 subcategories across these five categories, as well as an unknown ethnicity category. We used multivariable Cox regression to examine ethnic differences in the outcomes of interest. Models were adjusted for age, sex, deprivation, clinical factors and comorbidities, and household size, with stratification by geographical region. FINDINGS: Of 17 288 532 adults included in the study (excluding care home residents), 10 877 978 (62·9%) were White, 1 025 319 (5·9%) were South Asian, 340 912 (2·0%) were Black, 170 484 (1·0%) were of mixed ethnicity, 320 788 (1·9%) were of other ethnicity, and 4 553 051 (26·3%) were of unknown ethnicity. In wave 1, the likelihood of being tested for SARS-CoV-2 infection was slightly higher in the South Asian group (adjusted hazard ratio 1·08 [95% CI 1·07-1·09]), Black group (1·08 [1·06-1·09]), and mixed ethnicity group (1·04 [1·02-1·05]) and was decreased in the other ethnicity group (0·77 [0·76-0·78]) relative to the White group. The risk of testing positive for SARS-CoV-2 infection was higher in the South Asian group (1·99 [1·94-2·04]), Black group (1·69 [1·62-1·77]), mixed ethnicity group (1·49 [1·39-1·59]), and other ethnicity group (1·20 [1·14-1·28]). Compared with the White group, the four remaining high-level ethnic groups had an increased risk of COVID-19-related hospitalisation (South Asian group 1·48 [1·41-1·55], Black group 1·78 [1·67-1·90], mixed ethnicity group 1·63 [1·45-1·83], other ethnicity group 1·54 [1·41-1·69]), COVID-19-related ICU admission (2·18 [1·92-2·48], 3·12 [2·65-3·67], 2·96 [2·26-3·87], 3·18 [2·58-3·93]), and death (1·26 [1·15-1·37], 1·51 [1·31-1·71], 1·41 [1·11-1·81], 1·22 [1·00-1·48]). In wave 2, the risks of hospitalisation, ICU admission, and death relative to the White group were increased in the South Asian group but attenuated for the Black group compared with these risks in wave 1. Disaggregation into 16 ethnicity groups showed important heterogeneity within the five broader categories. INTERPRETATION: Some minority ethnic populations in England have excess risks of testing positive for SARS-CoV-2 and of adverse COVID-19 outcomes compared with the White population, even after accounting for differences in sociodemographic, clinical, and household characteristics. Causes are likely to be multifactorial, and delineating the exact mechanisms is crucial. Tackling ethnic inequalities will require action across many fronts, including reducing structural inequalities, addressing barriers to equitable care, and improving uptake of testing and vaccination. FUNDING: Medical Research Council.

Dexamethasone in hospitalised COVID-19 patients not on intensive respiratory support.

BACKGROUND: Dexamethasone decreases mortality in coronavirus disease 2019 (COVID-19) patients on intensive respiratory support (IRS) but is of uncertain benefit if less severely ill. We determined whether early (within 48 h) dexamethasone was associated with mortality in patients hospitalised with COVID-19 not on IRS. METHODS: We included patients admitted to US Veterans Affairs hospitals between 7 June 2020 and 31 May 2021 within 14 days after a positive test for severe acute respiratory syndrome coronavirus 2. Exclusions included recent prior corticosteroids and IRS within 48 h. We used inverse probability of treatment weighting (IPTW) to balance exposed and unexposed groups, and Cox proportional hazards models to determine 90-day all-cause mortality. RESULTS: Of 19 973 total patients (95% men, median age 71 years, 27% black), 15 404 (77%) were without IRS within 48 h. Of these, 3514 out of 9450 (34%) patients on no oxygen received dexamethasone and 1042 (11%) died; 4472 out of 5954 (75%) patients on low-flow nasal cannula (NC) only received dexamethasone and 857 (14%) died. In IPTW stratified models, patients on no oxygen who received dexamethasone experienced 76% increased risk for 90-day mortality (hazard ratio (HR) 1.76, 95% CI 1.47-2.12); there was no association with mortality among patients on NC only (HR 1.08, 95% CI 0.86-1.36). CONCLUSIONS: In patients hospitalised with COVID-19, early initiation of dexamethasone was common and was associated with no mortality benefit among those on no oxygen or NC only in the first 48 h; instead, we found evidence of potential harm. These real-world findings do not support the use of early dexamethasone in hospitalised COVID-19 patients without IRS.

Sociodemographic and clinical correlates of gabapentin receipt with and without opioids among a national cohort of patients with HIV.

Gabapentin is commonly prescribed for chronic pain, including to patients with HIV (PWH). There is growing concern regarding gabapentin's potential for harm, particularly in combination with opioids. Among PWH, we examined factors associated with higher doses of gabapentin receipt and determined if receipt varied by opioid use. We examined data from the Veterans Aging Cohort Study, a national prospective cohort including PWH, from 2002 through 2017. Covariates included prescribed opioid dose, self-reported past year opioid use, and other sociodemographic and clinical variables. We used multinomial logistic regression to determine independent predictors of gabapentin receipt. Among 3,702 PWH, 902 (24%) received any gabapentin during the study period at a mean daily dose of 1,469 mg. In the multinomial model, high-dose gabapentin receipt was associated with high-dose benzodiazepine receipt (adjusted odds ratio [aOR], 95% confidence interval [CI]= 1.53, [1.03-2.27]), pain interference (1.65 [1.39-1.95]), and hand or foot pain (1.81, [1.45-2.26]). High-dose gabapentin receipt was associated with prescribed high-dose opioids receipt (2.66 [1.95-3.62]) but not self-reported opioid use (1.03 [0.89-1.21]). PWH prescribed gabapentin at higher doses are more likely to receive high-dose opioids and high-dose benzodiazepines, raising safety concerns.

Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV.

The accuracy of a prediction algorithm depends on contextual factors that may vary across deployment settings. To address this inherent limitation of prediction, we propose an approach to counterfactual prediction based on the g-formula to predict risk across populations that differ in their distribution of treatment strategies. We apply this to predict 5-year risk of mortality among persons receiving care for HIV in the U.S. Veterans Health Administration under different hypothetical treatment strategies. First, we implement a conventional approach to develop a prediction algorithm in the observed data and show how the algorithm may fail when transported to new populations with different treatment strategies. Second, we generate counterfactual data under different treatment strategies and use it to assess the robustness of the original algorithm's performance to these differences and to develop counterfactual prediction algorithms. We discuss how estimating counterfactual risks under a particular treatment strategy is more challenging than conventional prediction as it requires the same data, methods, and unverifiable assumptions as causal inference. However, this may be required when the alternative assumption of constant treatment patterns across deployment settings is unlikely to hold and new data is not yet available to retrain the algorithm.

Potentially inappropriate medication use by level of polypharmacy among US Veterans 49-64 and 65-70 years old.

BACKGROUND: Potentially inappropriate medications (PIMs) are medications contra-indicated in particular circumstances. We sought to characterize PIMs by level of polypharmacy by age, sex, and race/ethnicity. METHODS: We performed a cross-sectional drug dispensing study using electronic health records available through the US Department of Veterans Affairs. We extracted pharmacy fill and refill records during fiscal year 2016 (i.e., October 1, 2015-September 30, 2016) for all patients aged 49-70 who accessed care in the preceding fiscal year. PIMs were defined by the combined Beers and Laroche (henceforth Beers Laroche) criteria used for older patients and the PROMPT criteria used for middle-aged. RESULTS: In the 1 499 586 patients aged 49-64, PIMs prevalence by PROMPT in patients with 0-4, 5-9, and ≥10 medications was 14.0%, 62.2%, and 86.1%, respectively, and by Beers Laroche was 14.3%, 63.4%, and 85.7%, respectively. In the 1 249 119 patients aged 65-70, PIMs prevalence by Beers Laroche was 14.8%, 59.9%, and 83.3%, and by PROMPT was 13.9%, 57.4%, and 82.0%, respectively. Meaningful differences in prevalence were shown by sex and race/ethnicity according to both set of criteria (e.g. PROMPT in patients with 5-9 medications: 66.1% women vs. 59.3% men; standardized-mean-differences [SMD] = 0.14; 61.7% of White vs. 54.5% of non-White; SMD = 0.15). The most common PIMs were digestive, analgesic, antidiabetic, and psychotropic medications. CONCLUSION: Prevalence of PIMs was high and increased with polypharmacy. Beers Laroche and PROMPT provided similar estimations inside and outside their target age, suggesting that PIMs are common among those with polypharmacy regardless of age.

Recording of 'COVID-19 vaccine declined': a cohort study on 57.9 million National Health Service patients' records in situ using OpenSAFELY, England, 8 December 2020 to 25 May 2021.

BackgroundPriority patients in England were offered COVID-19 vaccination by mid-April 2021. Codes in clinical record systems can denote the vaccine being declined.AimWe describe records of COVID-19 vaccines being declined, according to clinical and demographic factors.MethodsWith the approval of NHS England, we conducted a retrospective cohort study between 8 December 2020 and 25 May 2021 with primary care records for 57.9 million patients using OpenSAFELY, a secure health analytics platform. COVID-19 vaccination priority patients were those aged ≥ 50 years or ≥ 16 years clinically extremely vulnerable (CEV) or 'at risk'. We describe the proportion recorded as declining vaccination for each group and stratified by clinical and demographic subgroups, subsequent vaccination and distribution of clinical code usage across general practices.ResultsOf 24.5 million priority patients, 663,033 (2.7%) had a decline recorded, while 2,155,076 (8.8%) had neither a vaccine nor decline recorded. Those recorded as declining, who were subsequently vaccinated (n = 125,587; 18.9%) were overrepresented in the South Asian population (32.3% vs 22.8% for other ethnicities aged ≥ 65 years). The proportion of declining unvaccinated patients was highest in CEV (3.3%), varied strongly with ethnicity (black 15.3%, South Asian 5.6%, white 1.5% for ≥ 80 years) and correlated positively with increasing deprivation.ConclusionsClinical codes indicative of COVID-19 vaccinations being declined are commonly used in England, but substantially more common among black and South Asian people, and in more deprived areas. Qualitative research is needed to determine typical reasons for recorded declines, including to what extent they reflect patients actively declining.

Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans.

Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ among patients hospitalized with COVID-19. We examined the effects of HCQ alone and in combination with azithromycin in a hospitalized population of US veterans with COVID-19, using a propensity score-adjusted survival analysis with imputation of missing data. According to electronic health record data from the US Department of Veterans Affairs health care system, 64,055 US Veterans were tested for the virus that causes COVID-19 between March 1, 2020 and April 30, 2020. Of the 7,193 veterans who tested positive, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and there was an increased risk of intubation when HCQ was used in combination with azithromycin (hazard ratio = 1.55; 95% confidence interval: 1.07, 2.24). In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treatment of patients hospitalized with COVID-19, using a national sample of the US veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ.

Protease inhibitor-based direct-acting antivirals are associated with increased risk of aminotransferase elevations but not hepatic dysfunction or decompensation.

BACKGROUND & AIMS: Cases of acute liver injury (ALI) have been reported among chronic HCV-infected patients receiving protease inhibitor (PI)-based direct-acting antiviral (DAA) regimens, but no analyses have compared the risk of ALI in patients receiving PI- vs. non-PI-based DAAs. Thus, we compared the risk of 3 ALI outcomes between patients (by baseline Fibrosis-4 [FIB-4] group) receiving PI-based or non-PI-based DAAs. METHODS: We conducted a cohort study of 18,498 patients receiving PI-based DAA therapy (paritaprevir/ritonavir/ombitasvir±dasabuvir, elbasvir/grazoprevir, glecaprevir/pibrentasvir) matched 1:1 on propensity score to those receiving non-PI-based DAAs (sofosbuvir/ledipasvir, sofosbuvir/velpatasvir) in the 1945-1965 Veterans Birth Cohort (2014-2019). During exposure to DAA therapy, we determined development of: i) alanine aminotransferase (ALT) >200 U/L, ii) severe hepatic dysfunction (coagulopathy with hyperbilirubinemia), and iii) hepatic decompensation. We used Cox regression to determine hazard ratios (HRs) with 95% CIs for each ALI outcome within groups defined by baseline FIB-4 (≤3.25; >3.25). RESULTS: Among patients with baseline FIB-4 ≤3.25, those receiving PIs had a higher risk of ALT >200 U/L (HR 3.98; 95% CI 2.37-6.68), but not severe hepatic dysfunction (HR 0.67; 95% CI 0.19-2.39) or hepatic decompensation (HR 1.01; 95% CI 0.29-3.49), compared to those receiving non-PI-based regimens. For those with baseline FIB-4 >3.25, those receiving PIs had a higher risk of ALT >200 U/L (HR, 2.15; 95% CI 1.09-4.26), but not severe hepatic dysfunction (HR, 1.23 [0.64-2.38]) or hepatic decompensation (HR, 0.87; 95% CI 0.41-1.87), compared to those receiving non-PI-based regimens CONCLUSION: While risk of incident ALT elevations was increased in those receiving PI-based DAAs in both FIB-4 groups, the risk of severe hepatic dysfunction and hepatic decompensation did not differ between patients receiving PI- or non-PI-based DAAs in either FIB-4 group. LAY SUMMARY: Cases of liver injury have been reported among patients treated with protease inhibitor-based direct-acting antivirals for hepatitis C infection, but it is not clear if the risk of liver injury among people starting these drugs is increased compared to those starting non-protease inhibitor-based therapy. In this study, patients receiving protease inhibitor-based treatment had a higher risk of liver inflammation than those receiving a non-protease inhibitor-based treatment, regardless of the presence of pre-treatment advanced liver fibrosis/cirrhosis. However, the risk of severe liver dysfunction and decompensation were not higher for patients treated with protease inhibitor-based regimens.

Use of non-steroidal anti-inflammatory drugs and risk of death from COVID-19: an OpenSAFELY cohort analysis based on two cohorts.

OBJECTIVES: To assess the association between routinely prescribed non-steroidal anti-inflammatory drugs (NSAIDs) and deaths from COVID-19 using OpenSAFELY, a secure analytical platform. METHODS: We conducted two cohort studies from 1 March to 14 June 2020. Working on behalf of National Health Service England, we used routine clinical data in England linked to death data. In study 1, we identified people with an NSAID prescription in the last 3 years from the general population. In study 2, we identified people with rheumatoid arthritis/osteoarthritis. We defined exposure as current NSAID prescription within the 4 months before 1 March 2020. We used Cox regression to estimate HRs for COVID-19 related death in people currently prescribed NSAIDs, compared with those not currently prescribed NSAIDs, accounting for age, sex, comorbidities, other medications and geographical region. RESULTS: In study 1, we included 536 423 current NSAID users and 1 927 284 non-users in the general population. We observed no evidence of difference in risk of COVID-19 related death associated with current use (HR 0.96, 95% CI 0.80 to 1.14) in the multivariable-adjusted model. In study 2, we included 1 708 781 people with rheumatoid arthritis/osteoarthritis, of whom 175 495 (10%) were current NSAID users. In the multivariable-adjusted model, we observed a lower risk of COVID-19 related death (HR 0.78, 95% CI 0.64 to 0.94) associated with current use of NSAID versus non-use. CONCLUSIONS: We found no evidence of a harmful effect of routinely prescribed NSAIDs on COVID-19 related deaths. Risks of COVID-19 do not need to influence decisions about the routine therapeutic use of NSAIDs.