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.

Discrimination and Calibration of the Veterans Aging Cohort Study Index 2.0 for Predicting Mortality Among People With Human Immunodeficiency Virus in North America.

BACKGROUND: The updated Veterans Aging Cohort Study (VACS) Index 2.0 combines general and human immunodeficiency virus (HIV)-specific biomarkers to generate a continuous score that accurately discriminates risk of mortality in diverse cohorts of persons with HIV (PWH), but a score alone is difficult to interpret. Using data from the North American AIDS Cohort Collaboration (NA-ACCORD), we translate VACS Index 2.0 scores into validated probability estimates of mortality. METHODS: Because complete mortality ascertainment is essential for accurate calibration, we restricted analyses to cohorts with mortality from the National Death Index or equivalent sources. VACS Index 2.0 components were ascertained from October 1999 to April 2018. Mortality was observed up to March 2019. Calibration curves compared predicted (estimated by fitting a gamma model to the score) to observed mortality overall and within subgroups: cohort (VACS/NA-ACCORD subset), sex, age <50 or ≥50 years, race/ethnicity, HIV-1 RNA ≤500 or >500 copies/mL, CD4 count <350 or ≥350 cells/µL, and years 1999-2009 or 2010-2018. Because mortality rates have decreased over time, the final model was limited to 2010-2018. RESULTS: Among 37230 PWH in VACS and 8061 PWH in the NA-ACCORD subset, median age was 53 and 44 years; 3% and 19% were women; and 48% and 39% were black. Discrimination in NA-ACCORD (C-statistic = 0.842 [95% confidence interval {CI}, .830-.854]) was better than in VACS (C-statistic = 0.813 [95% CI, .809-.817]). Predicted and observed mortality largely overlapped in VACS and the NA-ACCORD subset, overall and within subgroups. CONCLUSIONS: Based on this validation, VACS Index 2.0 can reliably estimate probability of all-cause mortality, at various follow-up times, among PWH in North America.

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.

Change in Alcohol Use Based on Self-Report and a Quantitative Biomarker, Phosphatidylethanol, in People With HIV.

The timeline followback (TLFB) takes more resources to collect than the Alcohol Use Disorder Identification Test (AUDIT-C). We assessed agreement of TLFB and AUDIT-C with the biomarker phosphatidylethanol (PEth) and compared changes in TLFB and PEth among persons with HIV (PWH) using secondary data from randomized trials. We calculated operating characteristics and agreement between TLFB (> 1 and > 2 average drinks/day), AUDIT-C ≥ 4 and PEth ≥ 20 among 275 men with HIV. Median age was 57 years, 80% were African-American; and 17% white. Sixty-eight percent had PEth ≥ 20, 46% reported > 2 average drinks/day on TLFB, 61% reported > 1 average drinks/day on TLFB, and 72% had an AUDIT-C ≥ 4. Relative to PEth, sensitivity for AUDIT-C ≥ 4 was 84% (kappa = 0.36), and for TLFB > 1 average drink/day was 76% (kappa = 0.44). Change in alcohol use appeared greater using TLFB measures than PEth. Strategies to robustly assess alcohol use in PWH may require both self-report and biomarkers.

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.

Development and validation of case-finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data.

PURPOSE: To determine the positive predictive values (PPVs) of ICD-9, ICD-10, and current procedural terminology (CPT)-based diagnostic coding algorithms to identify prosthetic joint infection (PJI) following knee arthroplasty (TKA) within the United States Veterans Health Administration. METHODS: We identified patients with: (1) hospital discharge ICD-9 or ICD-10 diagnosis of PJI, (2) ICD-9, ICD-10, or CPT procedure code for TKA prior to PJI diagnosis, (3) CPT code for knee X-ray within ±90 days of the PJI diagnosis, and (4) at least 1 CPT code for arthrocentesis, arthrotomy, blood culture, or microbiologic procedure within ±90 days of the PJI diagnosis date. Separate samples of patients identified with the ICD-9 and ICD-10-based PJI diagnoses were obtained, stratified by TKA procedure volume at each medical center. Medical records of sampled patients were reviewed by infectious disease clinicians to adjudicate PJI events. The PPV (95% confidence interval [CI]) for the ICD-9 and ICD-10 PJI algorithms were calculated. RESULTS: Among a sample of 80 patients meeting the ICD-9 PJI algorithm, 60 (PPV 75.0%, [CI 64.1%-84.0%]) had confirmed PJI. Among 80 patients who met the ICD-10 PJI algorithm, 68 (PPV 85.0%, [CI 75.3%-92.0%]) had a confirmed diagnosis. CONCLUSIONS: An algorithm consisting of an ICD-9 or ICD-10 PJI diagnosis following a TKA code combined with CPT codes for a knee X-ray and either a relevant surgical procedure or microbiologic culture yielded a PPV of 75.0% (ICD-9) and 85.0% (ICD-10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.

Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study.

BACKGROUND: There is growing concern that racial and ethnic minority communities around the world are experiencing a disproportionate burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19). We investigated racial and ethnic disparities in patterns of COVID-19 testing (i.e., who received testing and who tested positive) and subsequent mortality in the largest integrated healthcare system in the United States. METHODS AND FINDINGS: This retrospective cohort study included 5,834,543 individuals receiving care in the US Department of Veterans Affairs; most (91%) were men, 74% were non-Hispanic White (White), 19% were non-Hispanic Black (Black), and 7% were Hispanic. We evaluated associations between race/ethnicity and receipt of COVID-19 testing, a positive test result, and 30-day mortality, with multivariable adjustment for a wide range of demographic and clinical characteristics including comorbid conditions, health behaviors, medication history, site of care, and urban versus rural residence. Between February 8 and July 22, 2020, 254,595 individuals were tested for COVID-19, of whom 16,317 tested positive and 1,057 died. Black individuals were more likely to be tested (rate per 1,000 individuals: 60.0, 95% CI 59.6-60.5) than Hispanic (52.7, 95% CI 52.1-53.4) and White individuals (38.6, 95% CI 38.4-38.7). While individuals from minority backgrounds were more likely to test positive (Black versus White: odds ratio [OR] 1.93, 95% CI 1.85-2.01, p < 0.001; Hispanic versus White: OR 1.84, 95% CI 1.74-1.94, p < 0.001), 30-day mortality did not differ by race/ethnicity (Black versus White: OR 0.97, 95% CI 0.80-1.17, p = 0.74; Hispanic versus White: OR 0.99, 95% CI 0.73-1.34, p = 0.94). The disparity between Black and White individuals in testing positive for COVID-19 was stronger in the Midwest (OR 2.66, 95% CI 2.41-2.95, p < 0.001) than the West (OR 1.24, 95% CI 1.11-1.39, p < 0.001). The disparity in testing positive for COVID-19 between Hispanic and White individuals was consistent across region, calendar time, and outbreak pattern. Study limitations include underrepresentation of women and a lack of detailed information on social determinants of health. CONCLUSIONS: In this nationwide study, we found that Black and Hispanic individuals are experiencing an excess burden of SARS-CoV-2 infection not entirely explained by underlying medical conditions or where they live or receive care. There is an urgent need to proactively tailor strategies to contain and prevent further outbreaks in racial and ethnic minority communities.

A map of the inorganic ternary metal nitrides.

Exploratory synthesis in new chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials synthesis is challenging. Nitrides represent one such space, where stringent synthesis constraints have limited the exploration of this important class of functional materials. Here, we employ a suite of computational materials discovery and informatics tools to construct a large stability map of the inorganic ternary metal nitrides. Our map clusters the ternary nitrides into chemical families with distinct stability and metastability, and highlights hundreds of promising new ternary nitride spaces for experimental investigation-from which we experimentally realized seven new Zn- and Mg-based ternary nitrides. By extracting the mixed metallicity, ionicity and covalency of solid-state bonding from the density functional theory (DFT)-computed electron density, we reveal the complex interplay between chemistry, composition and electronic structure in governing large-scale stability trends in ternary nitride materials.

Correction to: Excess Weight Gain After Cure of Hepatitis C Infection with Direct-Acting Antivirals.

JGIM published the article matched with the editorial in this issue in the July 2020 issue. Our apologies to the authors of the paper and the editorial.

Excess Weight Gain After Cure of Hepatitis C Infection with Direct-Acting Antivirals.

BACKGROUND: Cure from chronic hepatitis C virus (HCV) infection is readily achievable with direct-acting antivirals (DAA), but little is known about optimal management after treatment. Weight gained after DAA treatment may mitigate benefits or increase risk for liver disease progression. As the single largest sample of HCV-infected individuals receiving DAA treatment in the United States, the Veterans Affairs (VA) Birth Cohort is an ideal setting to assess weight gain after DAA treatment. METHODS: We performed a prospective study of patients dispensed DAA therapy from January 2014 to June 2015. Weight change was calculated as the difference in weight from sustained virologic response (SVR) determination to 2 years later. Demographic, weight, height, prescription, laboratory, and diagnosis code data were used for covariate definitions. We used multiple logistic regression to assess the association between candidate predictors and excess weight gain (≥ 10 lbs) after 2 years. RESULTS: Among 11,469 patients, 78.0% of patients were already overweight or obese at treatment initiation. Overall, SVR was achieved in 97.0% of patients. After 2 years, 52.6% of patients gained weight and 19.8% gained excess weight. In those with SVR, weight gain was as high as 38.2 lbs, with the top 10% gaining ≥ 16.5 lbs. Only 1% of those with obesity at treatment initiation normalized their weight class after 2 years. Significant predictors of post-SVR weight gain were SVR achievement, lower age, high FIB-4 score, cirrhosis, and weight class at treatment initiation. CONCLUSION: Weight gain is common after DAA treatment, even among those who are overweight or obese prior to treatment. Major predictors include age, baseline weight, alcohol, cirrhosis, and SVR. Everyone receiving DAAs should be counseled against weight gain with a particular emphasis among those at higher risk.

Safety of Gabapentin Prescribed for Any Indication in a Large Clinical Cohort of 571,718 US Veterans with and without Alcohol Use Disorder.

BACKGROUND: Gabapentin is prescribed for seizures and pain and has efficacy for treating alcohol use disorder (AUD) starting at doses of 900 milligrams per day (mg/d). Recent evidence suggests safety concerns associated with gabapentin including adverse neurologic effects. Individuals with hepatitis C (HCV), HIV, or AUD may be at increased risk due to comorbidities and potential medication interactions. METHODS: We identified patients prescribed gabapentin for ≥ 60 days for any indication between 2002 and 2015. We propensity-score matched each gabapentin-exposed patient with up to 5 gabapentin-unexposed patients. We followed patients for 2 years or until diagnosed with (i) falls or fractures, or (ii) altered mental status using validated ICD-9 diagnostic codes. We used Poisson regression to estimate incidence rates and relative risk (RR) of these adverse events in association with gabapentin exposure overall and stratified by age, race/ethnicity, sex, HCV, HIV, AUD, and dose. RESULTS: Incidence of falls or fractures was 1.81 per 100 person-years (PY) among 140,310 gabapentin-exposed and 1.34/100 PY among 431,408 gabapentin-unexposed patients (RR 1.35, 95% confidence interval [CI] 1.28 to 1.44). Incidence of altered mental status was 1.08/100 PY among exposed and 0.97/100 PY among unexposed patients, RR of 1.12 (95% CI 1.04 to 1.20). Excess risk of falls or fractures associated with gabapentin exposure was observed in all subgroups except patients with HCV, HIV, or AUD; however, these groups had elevated incidence regardless of exposure. There was a clear dose-response relationship for falls or fractures with highest risk observed among those prescribed ≥ 2,400 mg/d (RR 1.90, 95% CI 1.50 to 2.40). Patients were at increased risk for altered mental status at doses 600 to 2,399 mg/d; however, low number of events in the highest dose category limited power to detect a statistically significant association ≥ 2,400 mg/d. CONCLUSIONS: Gabapentin is associated with falls or fractures and altered mental status. Clinicians should be monitoring gabapentin safety, especially at doses ≥ 600 mg/d, in patients with and without AUD.

Does Reducing Drinking in Patients with Unhealthy Alcohol Use Improve Pain Interference, Use of Other Substances, and Psychiatric Symptoms?

BACKGROUND: We aimed to investigate the impact of reducing drinking in patients with unhealthy alcohol use on improvement of chronic pain interference, substance use, and psychiatric symptoms. METHODS: We analyzed longitudinal data from 2003 to 2015 in the Veterans Aging Cohort Study, a prospective, multisite observational study of US veterans, by emulating a hypothetical randomized trial (a target trial). Alcohol use was assessed using the Alcohol Use Disorders Identification Test (AUDIT) questionnaire, and outcome conditions were assessed via validated survey items. Individuals were followed from the first time their AUDIT score was ≥ 8 (baseline), a threshold consistent with unhealthy alcohol use. We compared individuals who reduced drinking (AUDIT < 8) at the next follow-up visit with individuals who did not (AUDIT ≥ 8). We fit separate logistic regression models to estimate odds ratios for improvement of each condition 2 years postbaseline among individuals who had that condition at baseline: moderate or severe pain interference symptoms, tobacco smoking, cannabis use, cocaine use, depressive symptoms, and anxiety symptoms. Inverse probability weighting was used to account for potential selection bias and confounding. RESULTS: Adjusted 2-year odds ratios (95% confidence intervals) for associations between reducing drinking and improvement or resolution of each condition were as follows: 1.49 (0.91, 2.42) for pain interference symptoms, 1.57 (0.93, 2.63) for tobacco smoking, 1.65 (0.92, 2.95) for cannabis use, 1.83 (1.03, 3.27) for cocaine use, 1.11 (0.64, 1.92) for depressive symptoms, and 1.33 (0.80, 2.22) for anxiety symptoms. CONCLUSIONS: We found some evidence for improvement of pain interference symptoms and substance use after reducing drinking among US veterans with unhealthy alcohol use, but confidence intervals were wide.

Validating Self-Reported Unhealthy Alcohol Use With Phosphatidylethanol (PEth) Among Patients With HIV.

BACKGROUND: We sought to compare self-reported alcohol consumption using Timeline Followback (TLFB) to biomarker-based evidence of significant alcohol use (phosphatidylethanol [PEth] > 20 ng/ml). Using data from patients with HIV (PWH) entering a clinical trial, we asked whether TLFB could predict PEth > 20 ng/ml and assessed the magnitude of association between TLFB and PEth level. METHODS: We defined unhealthy alcohol use as any alcohol use in the presence of liver disease, at-risk drinking, or alcohol use disorder. Self-reported alcohol use obtained from TLFB interview was assessed as mean number of drinks/day and number of heavy drinking days over the past 21 days. Dried blood spot samples for PEth were collected at the interview. We used logistic regression to predict PEth > 20 ng/ml and Spearman correlation to quantify the association with PEth, both as a function of TLFB. RESULTS: Among 282 individuals (99% men) in the analytic sample, approximately two-thirds (69%) of individuals had PEth > 20 ng/ml. The proportion with PEth > 20 ng/ml increased with increasing levels of self-reported alcohol use; of the 190 patients with either at-risk drinking or alcohol use disorder based on self-report, 82% had PEth > 20 ng/ml. Discrimination was better with number of drinks per day than heavy drinking days (AUC: 0.80 [95% CI: 0.74 to 0.85] vs. 0.74 [95% CI: 0.68 to 0.80]). The number of drinks per day and PEth were significantly and positively correlated across all levels of alcohol use (Spearman's R ranged from 0.29 to 0.56, all p values < 0.01). CONCLUSIONS: In this sample of PWH entering a clinical trial, mean numbers of drinks per day discriminated individuals with evidence of significant alcohol use by PEth. PEth complements self-report to improve identification of self-reported unhealthy alcohol use among PWH.

Differentiating Types of Self-Reported Alcohol Abstinence.

We contrast three types of abstinence: quit after alcohol associated problems (Q-AP), quit for other reasons (Q-OR), and lifetime abstainer (LTA). We summarized the characteristics of people living with HIV (PLWH), and matched uninfected individuals, by levels of alcohol use and types of abstinence. We then identified factors that differentiate abstinence and determined whether the association with an alcohol biomarker or a genetic polymorphism is improved by differentiating abstinence. Among abstainers, 34% of PLWH and 38% of uninfected were Q-AP; 53% and 53% were Q-OR; and 12% and 10% were LTA. Logistic regression models found smoking, alcohol, cocaine, and hepatitis C increased odds of Q-AP, whereas smoking and marijuana decreased odds of LTA. Differentiating types of abstinence improved association. Q-APs and LTAs can be readily differentiated by an alcohol biomarker and genetic polymorphism. Differentiating type of abstinence may enhance understanding of alcohol health effects.

Regional and Rural-Urban Differences in the Use of Direct-acting Antiviral Agents for Hepatitis C Virus: The Veteran Birth Cohort.

BACKGROUND: Veterans with hepatitis C virus (HCV) infection may face geographic obstacles to obtaining treatment. OBJECTIVE: We studied the influence of region and rural versus urban residence on receipt of direct-acting antiretroviral (DAA) medications for HCV. SUBJECTS: Veterans receiving care within Veterans Affairs Healthcare System born between 1945 and 1965. RESEARCH DESIGN: This is a observational study using national electronic health record data. MEASURES: Receipt of DAAs was defined as ≥1 filled prescription from January 1, 2014 to December 31, 2016. Region (South, Northeast, Midwest, and West) and residence (urban, rural-micropolitan, small rural towns, and isolated rural towns) variables were created using residential zone improvement plan codes and rural-urban commuting area (RUCA) codes. Multivariable models were adjusted for age, race, sex, severity of liver disease, comorbidities, and prior treatment experience. RESULTS: Among 166,353 eligible patients 64,854 received, DAAs. Variation by rural-urban residence depended on region. In unadjusted analyses, receipt varied by rural-urban designations within Midwest, and West regions (P<0.05) but did not vary within the South (P=0.12). Southern rural small town had the lowest incidence of DAA receipt (40.1%), whereas the incidence was 52.9% in Midwestern isolated rural towns. In adjusted logistic analyses, compared with southern urban residents (the largest single group), southern rural small town residents had the lowest odds ratio, 0.85 (95% confidence interval, 0.75-0.93), and Midwestern residents from isolated and small rural towns had the highest odds (odds ratio, both 1.27) to receive treatment. CONCLUSIONS: Substantial geographic variation exists in receipt of curative HCV treatment. Efforts are needed to provide more equitable access to DAAs.

Association Between Gabapentin Receipt for Any Indication and Alcohol Use Disorders Identification Test-Consumption Scores Among Clinical Subpopulations With and Without Alcohol Use Disorder.

BACKGROUND: Current medications for alcohol use disorder (AUD) have limited efficacy and utilization. Some clinical trials have shown efficacy for gabapentin among treatment-seeking individuals. The impact of gabapentin on alcohol consumption in a more general sample remains unknown. METHODS: We identified patients prescribed gabapentin for ≥180 consecutive days for any clinical indication other than substance use treatment between 2009 and 2015 in the Veterans Aging Cohort Study. We propensity-score matched each gabapentin-exposed patient with up to 5 unexposed patients. Multivariable difference-in-difference (DiD) linear regression models estimated the differential change in Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores during follow-up between exposed and unexposed patients, by baseline level of alcohol consumption and daily gabapentin dose. Analyses were stratified by AUD history. Clinically meaningful changes were a priori considered a DiD ≥1 point. RESULTS: Among patients with AUD, AUDIT-C scores decreased 0.39 points (95% confidence interval [CI] 0.05, 0.73) more among exposed than unexposed patients (p < 0.03). Potentially clinically meaningful differences were observed among those with AUD and exposed to ≥1,500 mg/d (DiD 0.77, 95% CI 0.15, 1.38, p < 0.02). No statistically significant effects were found among patients with AUD at doses lower than 1,500 mg/d or baseline AUDIT-C ≥4. Among patients without AUD, we found no overall difference in changes in AUDIT-C scores, nor in analyses stratified by baseline level of alcohol consumption. CONCLUSIONS: Patients exposed to doses of gabapentin consistent with those used in clinical trials, particularly those with AUD, experienced a greater decrease in AUDIT-C scores than matched unexposed patients.

Longitudinal Drinking Patterns and Their Clinical Correlates in Million Veteran Program Participants.

BACKGROUND: A variety of measures have been developed to screen for hazardous or harmful drinking. The Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) is one of the screening measures recommended by the U.S. Preventive Services Task Force. Annual administration of the AUDIT-C to all primary care patients is required by the U.S. Veterans Affairs Health System. The availability of data from the repeated administration of this instrument over time in a large patient population provides an opportunity to evaluate the utility of the AUDIT-C for identifying distinct drinking groups. METHODS: Using data from the Million Veteran Program cohort, we modeled group-based drinking trajectories using 2,833,189 AUDIT-C scores from 495,178 Veterans across an average 6-year time period. We also calculated patients' age-adjusted mean AUDIT-C scores to compare to the drinking trajectories. Finally, we extracted data on selected clinical diagnoses from the electronic health record and assessed their associations with the drinking trajectories. RESULTS: Of the trajectory models, the 4-group model demonstrated the best fit to the data. AUDIT-C trajectories were highly correlated with the age-adjusted mean AUDIT-C scores (rs = 0.94). Those with an alcohol use disorder diagnosis had 10 times the odds of being in the highest trajectory group (consistently hazardous/harmful) compared to the lowest drinking trajectory group (infrequent). Those with hepatitis C, posttraumatic stress disorder, liver cirrhosis, and delirium had 10, 7, 21, and 34%, respectively, higher odds of being classified in the highest drinking trajectory group versus the lowest drinking trajectory group. CONCLUSIONS: Trajectories and age-adjusted mean scores are potentially useful approaches to optimize the information provided by the AUDIT-C. In contrast to trajectories, age-adjusted mean AUDIT-C scores also have clinical relevance for real-time identification of individuals for whom an intervention may be warranted.

Emulating a trial of joint dynamic strategies: An application to monitoring and treatment of HIV-positive individuals.

Decisions about when to start or switch a therapy often depend on the frequency with which individuals are monitored or tested. For example, the optimal time to switch antiretroviral therapy depends on the frequency with which HIV-positive individuals have HIV RNA measured. This paper describes an approach to use observational data for the comparison of joint monitoring and treatment strategies and applies the method to a clinically relevant question in HIV research: when can monitoring frequency be decreased and when should individuals switch from a first-line treatment regimen to a new regimen? We outline the target trial that would compare the dynamic strategies of interest and then describe how to emulate it using data from HIV-positive individuals included in the HIV-CAUSAL Collaboration and the Centers for AIDS Research Network of Integrated Clinical Systems. When, as in our example, few individuals follow the dynamic strategies of interest over long periods of follow-up, we describe how to leverage an additional assumption: no direct effect of monitoring on the outcome of interest. We compare our results with and without the "no direct effect" assumption. We found little differences on survival and AIDS-free survival between strategies where monitoring frequency was decreased at a CD4 threshold of 350 cells/µl compared with 500 cells/µl and where treatment was switched at an HIV-RNA threshold of 1000 copies/ml compared with 200 copies/ml. The "no direct effect" assumption resulted in efficiency improvements for the risk difference estimates ranging from an 7- to 53-fold increase in the effective sample size.

Patterns and Correlates of Prescription Opioid Receipt Among US Veterans: A National, 18-Year Observational Cohort Study.

A better understanding of predisposition to transition to high-dose, long-term opioid therapy after initial opioid receipt could facilitate efforts to prevent opioid use disorder (OUD). We extracted data on 69,268 patients in the Veterans Aging Cohort Study who received any opioid prescription between 1998 and 2015. Using latent growth mixture modelling, we identified four distinguishable dose trajectories: low (53%), moderate (29%), escalating (13%), and rapidly escalating (5%). Compared to low dose trajectory, those in the rapidly escalating dose trajectory were proportionately more European-American (59% rapidly escalating vs. 38% low); had a higher prevalence of HIV (31% vs. 29%) and hepatitis C (18% vs. 12%); and during follow-up, had a higher incidence of OUD diagnoses (13% vs. 3%); were hospitalised more often [18.1/100 person-years (PYs) vs. 12.5/100 PY]; and had higher all-cause mortality (4.7/100 PY vs. 1.8/100 PY, all p < 0.0001). These measures can potentially be used in future prevention research, including genetic discovery.