Misclassification Error-Adjusted Prevalence of Injection Drug Use Among Infective Endocarditis Hospitalizations in the United States: A Serial Cross-Sectional Analysis of the 2007-2016 National Inpatient Sample.

Administrative health databases have been used to monitor trends in infective endocarditis hospitalization related to nonprescription injection drug use (IDU) using International Classification of Diseases (ICD) code algorithms. Because no ICD code for IDU exists, drug dependence and hepatitis C virus (HCV) have been used as surrogate measures for IDU, making misclassification error (ME) a threat to the accuracy of existing estimates. In a serial cross-sectional analysis, we compared the unadjusted and ME-adjusted prevalences of IDU among 70,899 unweighted endocarditis hospitalizations in the 2007-2016 National Inpatient Sample. The unadjusted prevalence of IDU was estimated with a drug algorithm, an HCV algorithm, and a combination algorithm (drug and HCV). Bayesian latent class models were used to estimate the median IDU prevalence and 95% Bayesian credible intervals and ICD algorithm sensitivity and specificity. Sex- and age group-stratified IDU prevalences were also estimated. Compared with the misclassification-adjusted prevalence, unadjusted estimates were lower using the drug algorithm and higher using the combination algorithm. The median ME-adjusted IDU prevalence increased from 9.7% (95% Bayesian credible interval (BCI): 6.3, 14.8) in 2008 to 32.5% (95% BCI: 26.5, 38.2) in 2016. Among persons aged 18-34 years, IDU prevalence was higher in females than in males. ME adjustment in ICD-based studies of injection-related endocarditis is recommended.

Evaluation of the Cherokee Nation Hepatitis C Virus Elimination Program in the First 22 Months of Implementation.

Importance: In 2019, hepatitis C virus (HCV) infection contributed to more deaths in the US than 60 other notifiable infectious diseases combined. The incidence of and mortality associated with HCV infection are highest among American Indian and Alaska Native individuals. Objective: To evaluate the association of the Cherokee Nation (CN) HCV elimination program with each element of the cascade of care: HCV screening, linkage to care, treatment, and cure. Design, Setting, and Participants: This cohort study used data from the CN Health Services (CNHS), which serves approximately 132 000 American Indian and Alaska Native individuals residing in the 14-county CN reservation in rural northeastern Oklahoma. Data from the first 22 months of implementation (November 1, 2015, to August 31, 2017) of an HCV elimination program were compared with those from the pre-elimination program period (October 1, 2012, to October 31, 2015). The analysis included American Indian and Alaska Native individuals aged 20 to 69 years who accessed care through the CNHS between October 1, 2012, and August 31, 2017. Cure data were recorded through April 15, 2018. Exposure: The CN HCV elimination program. Main Outcomes and Measures: The main outcomes were the proportions of the population screened for HCV, diagnosed with current HCV infection, linked to care, treated, and cured during the initial 22 months of the elimination program period and the pre-elimination program period. Data from electronic health records and an HCV treatment database were analyzed. The cumulative incidence of HCV infection in this population was estimated using bayesian analyses. Results: Among the 74 039 eligible individuals accessing care during the elimination program period, the mean (SD) age was 36.0 (13.5) years and 55.9% were women. From the pre-elimination program period to the elimination program period, first-time HCV screening coverage increased from 20.9% to 38.2%, and identification of current HCV infection and treatment in newly screened individuals increased from a mean (SD) of 170 (40) per year to 244 (4) per year and a mean of 95 (133) per year to 215 (9) per year, respectively. During the implementation period, of the 793 individuals with current HCV infection accessing the CNHS, 664 were evaluated (83.7%), 394 (59.3%) initiated treatment, and 335 (85.0%) had documented cure. In less than 2 years, the 85% 3-year goal was reached for cure (85.0%), and the goal for linkage to care was nearly reached (83.7%), whereas screening (44.1%) and treatment initiation (59.3%) required more time and resources. Conclusions and Relevance: This cohort study found that after 22 months of implementation, the CNHS community-based HCV elimination program was associated with an improved cascade of care. The facilitators and lessons learned in this program may be useful to other organizations planning similar programs.

Validity of ICD-based algorithms to estimate the prevalence of injection drug use among infective endocarditis hospitalizations in the absence of a reference standard.

BACKGROUND: International Classification of Diseases (ICD) code algorithms are routinely used to estimate the frequency of illicit injection drug use (IDU)-associated hospitalizations in administrative health datasets despite a lack of evidence regarding their validity. We aimed to measure the sensitivity and specificity of ICD code algorithms used to estimate the prevalence of current/recent IDU among infective endocarditis (IE) hospitalizations without a reference standard. METHODS: We reviewed medical records of 321 patients aged 18-64 years old from an urban academic hospital with an IE diagnosis between 2007 and 2017. Diagnostic tests for IDU included self-reported IDU in medical records; a drug use, abuse and dependence (UAD) ICD algorithm; a Hepatitis C Virus (HCV) ICD algorithm; and a combination drug UAD/HCV ICD algorithm. Sensitivity, specificity and the misclassification error (ME)-adjusted IDU prevalence were estimated using Bayesian latent class models. RESULTS: The combination algorithm had the highest sensitivity and lowest specificity. Sensitivity increased for the drug UAD algorithm in the ICD-10 period compared to the ICD-9 period. The ME-adjusted current/recent IDU prevalence estimated using the drug UAD and HCV algorithms was 23 % (95 % Bayesian credible interval: 16 %, 31 %). The unadjusted prevalence estimate from the drug UAD algorithm underestimated the ME-adjusted prevalence, while the combination algorithm overestimated it. CONCLUSION: The validity of ICD code algorithms for IDU among IE hospitalizations is imperfect and differs between ICD-9 and ICD-10. Commonly used ICD-based algorithms could lead to substantially biased prevalence estimates in IDU-associated hospitalizations when using administrative health data.

Validity of International Classification of Diseases codes in identifying illicit drug use target conditions using medical record data as a reference standard: A systematic review.

BACKGROUND: The twenty-first century opioid crisis has spurred interest in using International Classification of Diseases (ICD) code algorithms to identify patients using illicit drugs from administrative healthcare data. We conducted a systematic review of studies that validated ICD code algorithms for illicit drug use against a reference standard of medical record data. METHODS: Systematic searches of MEDLINE, EMBASE, PsycINFO, and Web of Science were conducted for studies published between 1980 and 2018 in English, French, Italian, or Spanish. We included validation studies of ICD-9 or ICD-10 code algorithms for an illicit drug use target condition (e.g., illicit drug use, abuse, or dependence (UAD), illicit drug use-related complications) given the sensitivity or specificity was reported or could be calculated. Bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies Version 2 (QUADAS-2) tool. RESULTS: Six of the 1210 articles identified met the inclusion criteria. For validation studies of broad UAD (n = 4), the specificity was nearly perfect, but the sensitivity ranged from 47% to 83%, with higher sensitivities tending to occur in higher prevalence populations. For validation studies of injection drug use (IDU)-associated infective endocarditis (n = 2), sensitivity and specificity were poor due to the lack of an ICD code for IDU. For all six studies, the risk of bias for the QUADAS-2 "reference standard" and "flow/timing domains" was scored as "unclear" due to insufficient reporting. CONCLUSIONS: Few studies have validated ICD code algorithms for illicit drug use target conditions, and available evidence is challenging to interpret due to inadequate reporting. PROSPERO Registration: CRD42019118401.

Effect Modification of the Association Between Race and Stage at Colorectal Cancer Diagnosis by Socioeconomic Status.

OBJECTIVES: To compare risks of distant-stage colorectal cancer (CRC) diagnosis between whites and American Indian/Alaska Natives (AI/ANs) and to explore effect modification by area-based socioeconomic status (SES). DESIGN: Retrospective cohort study using data from the Oklahoma Central Cancer Registry. SETTING: Oklahoma. PARTICIPANTS: White and AI/AN cases of CRC diagnosed in Oklahoma between 2001 and 2008 (N = 8 438). A subanalysis was performed on the cohort of those aged 50 years and older (N = 7 728). MAIN OUTCOME MEASURE: Risk of distant-stage CRC diagnosis stratified by SES score. RESULTS: Race and SES were independently associated with distant-stage diagnosis. In SES-stratified analyses, AI/ANs in the 2 lowest SES groups experienced increased risks in the overall cohort and among those aged 50 years and older. In multivariable models, risks remained significant among those aged 50 years and older in the lowest SES groups (Adjusted risk ratio SES score of 2: 1.31, 95% confidence interval: 1.06-1.63 and adjusted risk ratio SES score of 1: 1.21, 95% confidence interval: 1.01-1.44). CONCLUSION: Socioeconomic status is an effect modifier in the association between race/ethnicity and stage at CRC diagnosis. Disparities in stage at CRC diagnosis exist between AI/ANs and whites with lower estimated SES. Efforts are needed to increase CRC screening among lower SES AI/ANs.