The Association of Post-COVID-19-Related Symptoms and Preceding Severe Acute Respiratory Syndrome Coronavirus 2 Infection Among Fully Vaccinated Paramedics in Canada.

This study investigated the association between previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and risk of symptoms associated with post-COVID conditions among fully vaccinated paramedics in Canada. We included vaccinated paramedics who provided blood sample and questionnaire data on the same date during the study period. We examined the presence of symptoms associated with post-COVID conditions and depression severity against prior SARS-CoV-2 infection categories. Compared to the "no previous SARS-CoV-2 infection" group, there was no detected association between known prior SARS-CoV-2 infection (odds ratio [OR], 1.42 [95% confidence interval {CI}, 0.96-2.09]), nor unknown prior SARS-CoV-2 infection (OR, 0.54 [95% CI, 0.29-1.00]), and the presence of symptoms associated with post-COVID conditions.

Comparative Analysis of Instrumental Variables on the Assignment of Buprenorphine/Naloxone or Methadone for the Treatment of Opioid Use Disorder.

BACKGROUND: Instrumental variable (IV) analysis provides an alternative set of identification assumptions in the presence of uncontrolled confounding when attempting to estimate causal effects. Our objective was to evaluate the suitability of measures of prescriber preference and calendar time as potential IVs to evaluate the comparative effectiveness of buprenorphine/naloxone versus methadone for treatment of opioid use disorder (OUD). METHODS: Using linked population-level health administrative data, we constructed five IVs: prescribing preference at the individual, facility, and region levels (continuous and categorical variables), calendar time, and a binary prescriber's preference IV in analyzing the treatment assignment-treatment discontinuation association using both incident-user and prevalent-new-user designs. Using published guidelines, we assessed and compared each IV according to the four assumptions for IVs, employing both empirical assessment and content expertise. We evaluated the robustness of results using sensitivity analyses. RESULTS: The study sample included 35,904 incident users (43.3% on buprenorphine/naloxone) initiated on opioid agonist treatment by 1585 prescribers during the study period. While all candidate IVs were strong (A1) according to conventional criteria, by expert opinion, we found no evidence against assumptions of exclusion (A2), independence (A3), monotonicity (A4a), and homogeneity (A4b) for prescribing preference-based IV. Some criteria were violated for the calendar time-based IV. We determined that preference in provider-level prescribing, measured on a continuous scale, was the most suitable IV for comparative effectiveness of buprenorphine/naloxone and methadone for the treatment of OUD. CONCLUSIONS: Our results suggest that prescriber's preference measures are suitable IVs in comparative effectiveness studies of treatment for OUD.

Can supervised deep learning architecture outperform autoencoders in building propensity score models for matching?

PURPOSE: Propensity score matching is vital in epidemiological studies using observational data, yet its estimates relies on correct model-specification. This study assesses supervised deep learning models and unsupervised autoencoders for propensity score estimation, comparing them with traditional methods for bias and variance accuracy in treatment effect estimations. METHODS: Utilizing a plasmode simulation based on the Right Heart Catheterization dataset, under a variety of settings, we evaluated (1) a supervised deep learning architecture and (2) an unsupervised autoencoder, alongside two traditional methods: logistic regression and a spline-based method in estimating propensity scores for matching. Performance metrics included bias, standard errors, and coverage probability. The analysis was also extended to real-world data, with estimates compared to those obtained via a double robust approach. RESULTS: The analysis revealed that supervised deep learning models outperformed unsupervised autoencoders in variance estimation while maintaining comparable levels of bias. These results were supported by analyses of real-world data, where the supervised model's estimates closely matched those derived from conventional methods. Additionally, deep learning models performed well compared to traditional methods in settings where exposure was rare. CONCLUSION: Supervised deep learning models hold promise in refining propensity score estimations in epidemiological research, offering nuanced confounder adjustment, especially in complex datasets. We endorse integrating supervised deep learning into epidemiological research and share reproducible codes for widespread use and methodological transparency.

Physical comorbidity is associated with overnight hospitalization in U.S. adults with asthma: an assessment of the 2005-2018 National Health and Nutrition Examination Surveys.

OBJECTIVE: Identifying the effects of comorbidity on healthcare utilization is critical for understanding the benefits of improved comorbidity management. Asthma is a common respiratory condition, associated with gastrointestinal, metabolic, psychiatric, and other respiratory conditions. Adults with asthma represent a key population in understanding comorbidity and its consequences. The objective was to explore the relationship between comorbidity and overnight hospitalizations in U.S. adults with asthma. STUDY DESIGN AND METHODS: A cross-sectional sample of 3,887 subjects aged 20-79 was aggregated from seven cycles (2005-2018) of the National Health and Nutrition Examination Survey (NHANES). The survey design was created using the full seven cycles, then a subpopulation was used for the analysis. Design-based modified Poisson regression with robust standard errors compared the prevalence of overnight hospitalizations in subjects with and without comorbidities. Comorbidity was defined as the presence of one or more additional chronic conditions. RESULTS: Over half (61.6%) of patients with asthma reported having comorbidities. The overnight hospitalization prevalence was higher in those with comorbidities (21.6%) than those without (7.4%). The adjusted prevalence ratio of overnight hospitalizations in those with comorbidities vs. those without was 2.02 (95% CI: 1.54-2.66). Conclusions from sensitivity analyses remained the same. CONCLUSIONS: Comorbidity in U.S. adult asthma patients is associated with increased overnight hospitalizations. Study results concur with examinations of other healthcare utilization outcomes, revealing how comorbidity influences healthcare utilization patterns in patients with asthma. The reduction of overnight hospitalizations should be a targeted goal when developing and evaluating interventions to manage comorbidities in patients with asthma.

Childhood poverty, social support, immigration background and adolescent health and life satisfaction: A population-based longitudinal study.

OBJECTIVES: This study examined whether poverty (neighborhood and household) was associated with future health or life satisfaction outcomes and whether the association operated through social support (adult support at home, adult support at school, peer belonging), or differed by the immigration background (nonimmigrant family or immigrant family) of the family. METHODS: This study utilized a retrospective, longitudinal, population-based cohort that included self-reported survey data from the Middle Years Development Instrument (MDI) completed by children at age 9 and age 12, linked to administrative records. Participants included 5906 children in British Columbia, Canada. Neighborhood and household poverty were observed at age 8. Social support from adults and peers was self-reported at age 9. Outcomes (overall health; life satisfaction) were self-reported at age 12. Adjusted multi-level multiple linear regression analyses and parallel mediation analyses were utilized. The interaction between poverty exposure and immigration background was also examined. RESULTS: Exposure to either poverty type was associated with lower levels of life satisfaction and overall health at age 12, though household poverty appeared to be associated with lower outcomes in comparison to neighborhood poverty. The indirect effects of poverty on outcomes appeared to operate primarily through adult support at home and peer belonging. Children in immigrant families had a larger negative association between neighborhood poverty and life satisfaction. CONCLUSIONS: Household poverty had a larger negative association to outcomes in comparison to neighborhood poverty. The association of poverty to outcomes differed by immigration background and operated partially through adult support at home and peer belonging.

The Relationship Between Anti-Spike SARS-CoV-2 Antibody Levels and Risk of Breakthrough COVID-19 Among Fully Vaccinated Adults.

The relationship between antibodies to wild-type severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigens and the risk of breakthrough infections is unclear, especially during circulation of the Omicron strain. We investigated the association of anti-spike and anti-receptor binding domain antibody levels and the risk of subsequent breakthrough coronavirus disease 2019 (COVID-19). We included adult paramedics from an observational cohort study who received 2 mRNA vaccines but did not have COVID-19 before the blood collection. Higher postvaccination antibody levels to wild-type SARS-CoV-2 antigens were associated with a reduced risk of COVID-19. Further research into clinical utility of antibody levels, to inform a threshold for protection and timing of boosters, should be prioritized.

Healthcare Utilization After Respiratory Tuberculosis: A Controlled Interrupted Time Series Analysis.

BACKGROUND: Despite data suggesting elevated morbidity and mortality among people who have survived tuberculosis disease, the impact of respiratory tuberculosis on healthcare utilization in the years following diagnosis and treatment remains unclear. METHODS: Using linked health administrative data from British Columbia, Canada, we identified foreign-born individuals treated for respiratory tuberculosis between 1990 and 2019. We matched each person with up to four people without a tuberculosis diagnosis from the same source cohort using propensity score matching. Then, using a controlled interrupted time series analysis, we measured outpatient physician encounters and inpatient hospital admissions in the 5 years following respiratory tuberculosis diagnosis and treatment. RESULTS: We matched 1216 individuals treated for respiratory tuberculosis to 4864 non-tuberculosis controls. Immediately following the tuberculosis diagnostic and treatment period, the monthly rate of outpatient encounters in the tuberculosis group was 34.0% (95% confidence interval [CI]: 30.7%, 37.2%) higher than expected, and this trend was sustained for the duration of the post-tuberculosis period. The excess utilization represented an additional 12.2 (95% CI: 10.6, 14.9) outpatient encounters per person over the post-tuberculosis period, with respiratory morbidity a large contributor to the excess healthcare utilization. Results were similar for hospital admissions, with an additional 0.4 (95% CI: .3, .5) hospital admissions per person over the post-tuberculosis period. CONCLUSIONS: Respiratory tuberculosis appears to have long-term impacts on healthcare utilization beyond treatment. These findings underscore the need for screening, assessment, and treatment of post-tuberculosis sequelae, as it may provide an opportunity to improve health and reduce resource use.

Comparative effectiveness of natalizumab on cognition in multiple sclerosis: A cohort study.

BACKGROUND: Cognitive impairment occurs in 40%-70% of persons with multiple sclerosis (MS). OBJECTIVE: To examine the effectiveness of natalizumab compared with other disease-modifying treatments (DMTs) on improving cognition as measured by the Symbol Digit Modalities Test (SDMT). METHODS: Data were collected as part of Swedish nationwide phase IV surveillance studies (2007-2020). An increase in SDMT score by ⩾10% of the difference between maximum score possible (110) and the baseline value was defined as cognitive improvement. The likelihood of improvement was compared between natalizumab-treated individuals and individuals treated with other DMTs using mixed effect logistic regression. Trend in odds of improvement was investigated using slope analyses. RESULTS: We included 2100 persons with relapsing-remitting MS treated with natalizumab and 2622 persons treated with other DMTs. At 6 months, 45% reached improvement. The natalizumab group showed largest odds of improvement during follow-up (odds ratio: 2.3, 95% confidence interval (CI): 1.5-3.5). The odds of improvement increased by 7% (95% CI: 6-7) per month of natalizumab treatment. The equivalent estimate was 4% (95% CI: 2-5) for other monoclonal antibodies and nonsignificant for oral or platform therapies. CONCLUSION: Treatment with natalizumab or other monoclonal antibodies is associated with a significantly faster likelihood of cognitive improvement than platform or oral DMTs.

The association between HIV self-test awareness and recent HIV testing uptake in the male population in Gambia: data analysis from 2019-2020 demographic and health survey.

BACKGROUND: The Gambian Ministry of Health is supportive of HIV self-testing (HIVST) and HIVST initiatives are being piloted as an additional strategy to increase HIV testing for individuals not currently reached by existing services, particularly men. This study aimed to determine awareness of HIVST among Gambian men, and whether prior awareness of HIVST is associated with recent HIV testing uptake. METHODS: We used men's cross-sectional data from the 2019-2020 Gambian Demographic and Health Survey. We employed design-adjusted multivariable logistic regression to examine the association between HIVST awareness and recent HIV testing. Propensity-score weighting was conducted as sensitivity analyses. RESULTS: Of 3,308 Gambian men included in the study, 11% (372) were aware of HIVST and 16% (450) received HIV testing in the last 12 months. In the design-adjusted multivariable analysis, men who were aware of HIVST had 1.76 times (95% confidence interval: 1.26-2.45) the odds of having an HIV test in the last 12 months, compared to those who were not aware of HIVST. Sensitivity analyses revealed similar findings. CONCLUSION: Awareness of HIVST may help increase the uptake of HIV testing among men in Gambia. This finding highlights HIVST awareness-raising activities to be an important intervention for nationwide HIVST program planning and implementation in Gambia.

Role of latent tuberculosis infection on elevated risk of cardiovascular disease: a population-based cohort study of immigrants in British Columbia, Canada, 1985-2019.

We investigated cardiovascular disease (CVD) risk associated with latent tuberculosis infection (LTBI) (Aim-1) and LTBI therapy (Aim-2) in British Columbia, a low-tuberculosis-incidence setting. 49,197 participants had valid LTBI test results. Cox proportional hazards model was fitted, adjusting for potential confounders. Compared with the participants who tested LTBI negative, LTBI positive was associated with an 8% higher CVD risk in complete case data (adjusted hazard ratio (HR): 1.08, 95% CI: 0.99-1.18), a statistically significant 11% higher risk when missing confounder values were imputed using multiple imputation (HR: 1.11, 95% CI: 1.02-1.20), and 10% higher risk when additional proxy variables supplementing known unmeasured confounders were incorporated in the highdimensional disease risk score technique to reduce residual confounding (HR: 1.10, 95% CI: 1.01-1.20). Also, compared with participants who tested negative, CVD risk was 27% higher among people who were LTBI positive but incomplete LTBI therapy (HR: 1.27, 95% CI: 1.04-1.55), whereas the risk was similar in people who completed LTBI therapy (HR: 1.04, 95% CI: 0.87-1.24). Findings were consistent in different sensitivity analyses. We concluded that LTBI is associated with an increased CVD risk in low-tuberculosis-incidence settings, with a higher risk associated with incomplete LTBI therapy and attenuated risk when therapy is completed.

Key considerations for choosing a statistical method to deal with incomplete treatment adherence in pragmatic trials.

Pragmatic trials offer practical means of obtaining real-world evidence to help improve decision-making in comparative effectiveness settings. Unfortunately, incomplete adherence is a common problem in pragmatic trials. The commonly used methods in randomized control trials often cannot handle the added complexity imposed by incomplete adherence, resulting in biased estimates. Several naive methods and advanced causal inference methods (e.g., inverse probability weighting and instrumental variable-based approaches) have been used in the literature to deal with incomplete adherence. Practitioners and applied researchers are often confused about which method to consider under a given setting. This current work is aimed to review commonly used statistical methods to deal with non-adherence along with their key assumptions, advantages, and limitations, with a particular focus on pragmatic trials. We have listed the applicable settings for these methods and provided a summary of available software. All methods were applied to two hypothetical datasets to demonstrate how these methods perform in a given scenario, along with the R codes. The key considerations include the type of intervention strategy (point treatment settings, where treatment is administered only once versus sustained treatment settings, where treatment has to be continued over time) and availability of data (e.g., the extent of measured or unmeasured covariates that are associated with adherence, dependent confounding impacted by past treatment, and potential violation of assumptions). This study will guide practitioners and applied researchers to use the appropriate statistical method to address incomplete adherence in pragmatic trial settings for both the point and sustained treatment strategies.

The relationship between mood disorder diagnosis and experiencing an unmet health-care need in Canada: findings from the 2014 Canadian Community Health Survey.

BACKGROUND: Despite Canada's universal health-care system, millions of Canadians experience unmet health-care needs (UHCN). People with mood disorders may be at higher risk of UHCN due to barriers such as stigma and gaps in health-care services. AIM: We aimed to examine the relationship between having a diagnosed mood disorder and experiencing UHCN using a recent, nationally representative survey. METHODS: Using the 2014 Canadian Community Health Survey, we used multivariate logistic regression to estimate the association between mood disorder and UHCN in the past 12 months, adjusting for sociodemographic variables and health status. RESULTS: Among 52,825 respondents, 11.8% reported UHCN. Respondents with a diagnosed mood disorder were more likely to report UHCN [adjusted odds ratio (OR) 1.61, 95% confidence interval (CI) 1.38, 1.89]. Among respondents with a regular doctor, people with mood disorders were still more likely to report UHCN (OR 1.63, 95% CI 1.38, 1.93). Sensitivity analyses using propensity score and missing data imputation approaches resulted in similar estimates. CONCLUSIONS: Adults diagnosed with a mood disorder are more likely to report UHCN in the past year, even those with a regular doctor. Our findings suggest that barriers beyond physician attachment may impact access to care for people with mood disorders.

Chronic physical conditions and suicidal ideation: a population-level analysis of Canadian school-attending young adults.

BACKGROUND: Students who identify with a chronic physical condition are a growing population and their conditions may be associated with poor mental well-being. AIM: To compare suicidal ideation prevalence between Canadian school-attending young adults with and without a chronic physical condition. We hypothesized that students living with a chronic condition have a higher likelihood of experiencing suicidal ideation. METHODS: A cross-sectional study was conducted using a nationally representative sample of 2297 Canadian school-attending young adults (ages 15-29 years) from the 2012-13 Canadian Community Health Survey-Mental Health (CCHS-MH). Survey-weighted logistic regression and sensitivity analyses were performed to estimate the likelihood of experiencing suicidal ideation between students with and without a chronic physical condition. RESULTS: Approximately 14.3% (n = 329) students experienced suicidal ideation at some point. Students living with a physical chronic condition demonstrated 1.65 (95% CI: 1.14, 2.39) times higher odds of experiencing suicidal ideation, compared to students not living with a chronic physical condition. CONCLUSIONS: Suicide prevention and health promotion are important considerations for campus health providers and administrators when planning services and accommodations for students living with chronic physical conditions.

A Higher Antibody Response Is Generated With a 6- to 7-Week (vs Standard) Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccine Dosing Interval.

The optimal dosing interval for severe acute respiratory syndrome coronavirus 2 vaccines remains controversial. In this prospective study, we compared serology results of paramedics vaccinated with mRNA vaccines at the recommended short (17-28 days) vs long (42-49 days) interval. We found that a long dosing interval resulted in higher spike, receptor binding domain, and spike N terminal domain antibody concentrations.

The use and quality of reporting of propensity score methods in multiple sclerosis literature: A review.

BACKGROUND: Propensity score (PS) analyses are increasingly used in multiple sclerosis (MS) research, largely owing to the greater availability of large observational cohorts and registry databases. OBJECTIVE: To evaluate the use and quality of reporting of PS methods in the recent MS literature. METHODS: We searched the PubMed database for articles published between January 2013 and July 2019. We restricted the search to comparative effectiveness studies of two disease-modifying therapies. RESULTS: Thirty-nine studies were included in the review, with most studies (62%) published within the past 3 years. All studies reported the list of covariates used for the PS model, but only 21% of studies mentioned how those covariates were selected. Most studies used PS matching (72%), followed by PS adjustment (18%), weighting (15%), and stratification (3%), with some overlap. Most studies using matching or weighting reported checking post-PS covariate imbalance (91%), although about 45% of these studies relied on p values from various statistical tests. Only 25% of studies using matching reported calculating robust standard errors for the PS analyses. CONCLUSIONS: The quality of reporting of PS methods in the MS literature is sub-optimal in general, and in some cases, inappropriate methods are used.

Recommendations for the use of propensity score methods in multiple sclerosis research.

BACKGROUND: With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. OBJECTIVES: To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. METHODS: We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. RESULTS: Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. CONCLUSION: This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.

Analysis approaches to address treatment nonadherence in pragmatic trials with point-treatment settings: a simulation study.

BACKGROUND: Two-stage least square [2SLS] and two-stage residual inclusion [2SRI] are popularly used instrumental variable (IV) methods to address medication nonadherence in pragmatic trials with point treatment settings. These methods require assumptions, e.g., exclusion restriction, although they are known to handle unmeasured confounding. The newer IV-method, nonparametric causal bound [NPCB], showed promise in reducing uncertainty compared to usual IV-methods. The inverse probability-weighted per-protocol [IP-weighted PP] method is useful in the same setting but requires different assumptions, e.g., no unmeasured confounding. Although all of these methods are aimed to address the same nonadherence problem, comprehensive simulations to compare performances of them are absent in the literature. METHODS: We performed extensive simulations to compare the performances of the above methods in addressing nonadherence when: (1) exclusion restriction satisfied and no unmeasured confounding, (2) exclusion restriction is met but unmeasured confounding present, and (3) exclusion restriction is violated. Our simulations varied parameters such as, levels of adherence rates, unmeasured confounding, and exclusion restriction violations. Risk differences were estimated, and we compared performances in terms of bias, standard error (SE), mean squared error (MSE), and 95% confidence interval coverage probability. RESULTS: For setting (1), 2SLS and 2SRI have small bias and nominal coverage. IP-weighted PP outperforms these IV-methods in terms of smaller MSE but produces high MSE when nonadherence is very high. For setting (2), IP-weighted-PP generally performs poorly compared to 2SLS and 2SRI in term of bias, and both-stages adjusted IV-methods improve precision than naive IV-methods. For setting (3), IV-methods perform worst in all scenarios, and IP-weighted-PP produces unbiased estimates and small MSE when confounders are adjusted. NPCB produces larger uncertainty bound width in almost all scenarios. We also analyze a two-arm trial to estimate vitamin-A supplementation effect on childhood mortality after addressing nonadherence. CONCLUSIONS: Understanding finite sample characteristics of these methods will guide future researchers in determining suitable analysis strategies. Since assumptions are different and often untestable for IP-weighted PP and IV methods, we suggest analyzing data using both IP-weighted PP and IV approaches in search of a robust conclusion.

A Prospective Observational Cohort Comparison of SARS-CoV-2 Seroprevalence Between Paramedics and Matched Blood Donors in Canada During the COVID-19 Pandemic.

STUDY OBJECTIVE: SARS-CoV-2 represents an occupational risk to paramedics, who work in uncontrolled environments. We sought to identify the occupation-specific risk to paramedics by comparing their seroprevalence of SARS-CoV-2 infection-specific antibodies to that of blood donors in Canada. METHODS: In this prospective cohort study, we performed serology testing (Elecsys Anti-SARS-CoV-2 nucleocapsid assay) on samples from paramedics and blood donors (January to July 2021) in Canada. Paramedic samples were compared to blood donor samples through 1:1-matched (based on age, sex, location, date of blood collection, and vaccination status) and raking weighted comparisons. We compared the seroprevalence with a risk difference (and 95% confidence interval [CI]) and performed secondary analyses within subgroups defined by vaccination status. RESULTS: The 1:1 match included 1,627 cases per group; in both groups, 723 (44%) were women, with a median age of 38. The raking weighted comparison included 1,713 paramedic samples and 19,515 blood donor samples, with similar characteristics. In the 1:1 match, the seroprevalence was similar (difference 1.2; 95% CI -0.20 to 2.7) between paramedics (5.2%) and blood donors (3.9%). The raking weighted comparison was consistent (difference 0.97; 95% CI -0.10 to 2.0). The unvaccinated paramedic samples, in comparison to the blood donor samples, demonstrated a higher seroprevalence in the 1:1 (difference 5.9; 95% CI 1.8 to 10) and weighted (difference 6.5; 95% CI 1.8 to 10) comparisons. Among vaccinated cases, the between-group seroprevalence was similar. CONCLUSION: Overall, paramedics demonstrated similar evidence of prior SARS-CoV-2 infection to that of blood donors. However, among unvaccinated individuals, evidence of prior infection was higher among paramedics compared to blood donors.

Accurate classification of carotid endarterectomy indication using physician claims and hospital discharge data.

BACKGROUND AND PURPOSE: Studies of carotid endarterectomy (CEA) require stratification by symptomatic vs asymptomatic status because of marked differences in benefits and harms. In administrative datasets, this classification has been done using hospital discharge diagnosis codes of uncertain accuracy. This study aims to develop and evaluate algorithms for classifying symptomatic status using hospital discharge and physician claims data. METHODS: A single center's administrative database was used to assemble a retrospective cohort of participants with CEA. Symptomatic status was ascertained by chart review prior to linkage with physician claims and hospital discharge data. Accuracy of rule-based classification by discharge diagnosis codes was measured by sensitivity and specificity. Elastic net logistic regression and random forest models combining physician claims and discharge data were generated from the training set and assessed in a test set of final year participants. Models were compared to rule-based classification using sensitivity at fixed specificity. RESULTS: We identified 971 participants undergoing CEA at the Vancouver General Hospital (Vancouver, Canada) between January 1, 2008 and December 31, 2016. Of these, 729 met inclusion/exclusion criteria (n = 615 training, n = 114 test). Classification of symptomatic status using hospital discharge diagnosis codes was 32.8% (95% CI 29-37%) sensitive and 98.6% specific (96-100%). At matched 98.6% specificity, models that incorporated physician claims data were significantly more sensitive: elastic net 69.4% (59-82%) and random forest 78.8% (69-88%). CONCLUSION: Discharge diagnoses were specific but insensitive for the classification of CEA symptomatic status. Elastic net and random forest machine learning algorithms that included physician claims data were sensitive and specific, and are likely an improvement over current state of classification by discharge diagnosis alone.

Dealing With Treatment-Confounder Feedback and Sparse Follow-up in Longitudinal Studies: Application of a Marginal Structural Model in a Multiple Sclerosis Cohort.

The beta-interferons are widely prescribed platform therapies for patients with multiple sclerosis (MS). We accessed a cohort of patients with relapsing-onset MS from British Columbia, Canada (1995-2013), to examine the potential survival advantage associated with beta-interferon exposure using a marginal structural model. Accounting for potential treatment-confounder feedback between comorbidity, MS disease progression, and beta-interferon exposure, we found an association between beta-interferon exposure of at least 6 contiguous months and improved survival (hazard ratio (HR) = 0.63, 95% confidence interval 0.47, 0.86). We also assessed potential effect modifications by sex, baseline age, or baseline disease duration, and found these factors to be important effect modifiers. Sparse follow-up due to variability in patient contact with the health system is one of the biggest challenges in longitudinal analyses. We considered several single-level and multilevel multiple imputation approaches to deal with sparse follow-up and disease progression information; both types of approach produced similar estimates. Compared to ad hoc imputation approaches, such as linear interpolation (HR = 0.63), and last observation carried forward (HR = 0.65), all multiple imputation approaches produced a smaller hazard ratio (HR = 0.53), although the direction of effect and conclusions drawn concerning the survival advantage remained the same.