A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.

Economic evaluation of the effect of needle and syringe programs on skin, soft tissue, and vascular infections in people who inject drugs: a microsimulation modelling approach.

BACKGROUND: Needle and syringe programs (NSP) are effective harm-reduction strategies against HIV and hepatitis C. Although skin, soft tissue, and vascular infections (SSTVI) are the most common morbidities in people who inject drugs (PWID), the extent to which NSP are clinically and cost-effective in relation to SSTVI in PWID remains unclear. The objective of this study was to model the clinical- and cost-effectiveness of NSP with respect to treatment of SSTVI in PWID. METHODS: We performed a model-based, economic evaluation comparing a scenario with NSP to a scenario without NSP. We developed a microsimulation model to generate two cohorts of 100,000 individuals corresponding to each NSP scenario and estimated quality-adjusted life-years (QALY) and cost (in 2022 Canadian dollars) over a 5-year time horizon (1.5% per annum for costs and outcomes). To assess the clinical effectiveness of NSP, we conducted survival analysis that accounted for the recurrent use of health care services for treating SSTVI and SSTVI mortality in the presence of competing risks. RESULTS: The incremental cost-effectiveness ratio associated with NSP was $70,278 per QALY, with incremental cost and QALY gains corresponding to $1207 and 0.017 QALY, respectively. Under the scenario with NSP, there were 788 fewer SSTVI deaths per 100,000 PWID, corresponding to 24% lower relative hazard of mortality from SSTVI (hazard ratio [HR] = 0.76; 95% confidence interval [CI] = 0.72-0.80). Health service utilization over the 5-year period remained lower under the scenario with NSP (outpatient: 66,511 vs. 86,879; emergency department: 9920 vs. 12,922; inpatient: 4282 vs. 5596). Relatedly, having NSP was associated with a modest reduction in the relative hazard of recurrent outpatient visits (HR = 0.96; 95% CI = 0.95-0.97) for purulent SSTVI as well as outpatient (HR = 0.88; 95% CI = 0.87-0.88) and emergency department visits (HR = 0.98; 95% CI = 0.97-0.99) for non-purulent SSTVI. CONCLUSIONS: Both the individuals and the healthcare system benefit from NSP through lower risk of SSTVI mortality and prevention of recurrent outpatient and emergency department visits to treat SSTVI. The microsimulation framework provides insights into clinical and economic implications of NSP, which can serve as valuable evidence that can aid decision-making in expansion of NSP services.

The evolution of SARS-CoV-2 seroprevalence in Canada: a time-series study, 2020-2023.

BACKGROUND: During the first year of the COVID-19 pandemic, the proportion of reported cases of COVID-19 among Canadians was under 6%. Although high vaccine coverage was achieved in Canada by fall 2021, the Omicron variant caused unprecedented numbers of infections, overwhelming testing capacity and making it difficult to quantify the trajectory of population immunity. METHODS: Using a time-series approach and data from more than 900 000 samples collected by 7 research studies collaborating with the COVID-19 Immunity Task Force (CITF), we estimated trends in SARS-CoV-2 seroprevalence owing to infection and vaccination for the Canadian population over 3 intervals: prevaccination (March to November 2020), vaccine roll-out (December 2020 to November 2021), and the arrival of the Omicron variant (December 2021 to March 2023). We also estimated seroprevalence by geographical region and age. RESULTS: By November 2021, 9.0% (95% credible interval [CrI] 7.3%-11%) of people in Canada had humoral immunity to SARS-CoV-2 from an infection. Seroprevalence increased rapidly after the arrival of the Omicron variant - by Mar. 15, 2023, 76% (95% CrI 74%-79%) of the population had detectable antibodies from infections. The rapid rise in infection-induced antibodies occurred across Canada and was most pronounced in younger age groups and in the Western provinces: Manitoba, Saskatchewan, Alberta and British Columbia. INTERPRETATION: Data up to March 2023 indicate that most people in Canada had acquired antibodies against SARS-CoV-2 through natural infection and vaccination. However, given variations in population seropositivity by age and geography, the potential for waning antibody levels, and new variants that may escape immunity, public health policy and clinical decisions should be tailored to local patterns of population immunity.

Survival Modelling For Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis using Electronic Health Records.

Non-parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyze survival data that have been collected under different study designs. We review non-parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) To clarify the differences in the model assumptions, and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta analysis of survival data obtained from different types of study, and to the modern era of electronic health records.

Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study.

BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.

Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket.

BACKGROUND: Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Beverages (SSB). Sales of price discounted food items are known to increase during the period of discounting. However, the presence and extent of the lagged effect of discounting, a sustained level of sales after discounting ends, is previously unaccounted for. We investigated the presence of the lagged effect of discounting on the sales of five SSB categories, which are soda, fruit juice, sport and energy drink, sugar-sweetened coffee and tea, and sugar-sweetened drinkable yogurt. METHODS: We fitted distributed lag models to weekly volume-standardized sales and percent discounting generated by a supermarket in Montreal, Canada between January 2008 and December 2013, inclusive (n = 311 weeks). RESULTS: While the sales of SSB increased during the period of discounting, there was no evidence of a prominent lagged effect of discounting in four of the five SSB; the exception was sports and energy drinks, where a posterior mean of 28,459 servings (95% credible interval: 2661 to 67,253) of excess sales can be attributed to the lagged effect in the target store during the 6 years study period. CONCLUSION: Our results indicate that studies that do not account for the lagged effect of promotions may not fully capture the effect of price discounting for some food categories.

Opioid prescribing among new users for non-cancer pain in the USA, Canada, UK, and Taiwan: A population-based cohort study.

BACKGROUND: The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. METHODS AND FINDINGS: We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. CONCLUSIONS: In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic.

Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records.

Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous-time process potentially as a function of time-varying covariates. We develop a continuous-time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation-maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates.

Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada.

OBJECTIVE: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions. DESIGN: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transforms store-level sales into area-level purchasing, we developed small-area public health indicators of food purchasing for neighbourhood districts. We generated the area-level indicators measuring per-resident purchasing quantity for soda, diet soda, flavoured (sugar-added) yogurt and plain yogurt purchasing. We then provided an illustrative public health application of these indicators as covariates for an ecological spatial regression model to estimate spatially correlated small-area risk of type 2 diabetes mellitus (T2D) obtained from the public health administrative data. SETTING: Greater Montreal, Canada in 2012. PARTICIPANTS: Neighbourhood districts (n 193). RESULTS: The indicator of flavoured yogurt had a positive association with neighbourhood-level risk of T2D (1·08, 95 % credible interval (CI) 1·02, 1·14), while that of plain yogurt had a negative association (0·93, 95 % CI 0·89, 0·96). The indicator of soda had an inconclusive association, and that of diet soda was excluded due to collinearity with soda. The addition of the indicators also improved model fit of the T2D spatial regression (Watanabe-Akaike information criterion = 1765 with the indicators, 1772 without). CONCLUSION: Store-level grocery sales data can be used to reveal micro-scale geographic disparities and trends of food selections that would be masked by traditional survey-based estimation.

Fluoroquinolone Use and Seasonal Patterns of Ciprofloxacin Resistance in Community-Acquired Urinary Escherichia coli Infection in a Large Urban Center.

Urinary tract infections caused by the bacterium Escherichia coli are among the most frequently encountered infections and are a common reason for antimicrobial prescriptions. Resistance to fluoroquinolone antimicrobial agents, particularly ciprofloxacin, has increased in recent decades. It is intuitive that variation in fluoroquinolone resistance is driven by changes in antimicrobial use, but careful study of this association requires the use of time-series methods. Between April 2010 and December 2014, we studied seasonal variation in resistance to ciprofloxacin, trimethoprim-sulfamethoxazole, and ampicillin in community-acquired urinary E. coli isolates in Montreal, Quebec, Canada. Using dynamic linear models, we investigated whether seasonal variation in resistance could be explained by seasonal variation in community antimicrobial use. We found a positive association between total fluoroquinolone use lagged by 1 and 2 months and the proportion of isolates resistant to ciprofloxacin. Our results suggest that resistance to ciprofloxacin is responsive to short-term variation in antimicrobial use. Thus, antimicrobial stewardship campaigns to reduce fluoroquinolone use, particularly in the winter when use is highest, are likely to be a valuable tool in the struggle against antimicrobial resistance.

An Area-Level Indicator of Latent Soda Demand: Spatial Statistical Modeling of Grocery Store Transaction Data to Characterize the Nutritional Landscape in Montreal, Canada.

Measurement of neighborhood dietary patterns at high spatial resolution allows public health agencies to identify and monitor communities with an elevated risk of nutrition-related chronic diseases. Currently, data on diet are obtained primarily through nutrition surveys, which produce measurements at low spatial resolutions. The availability of store-level grocery transaction data provides an opportunity to refine the measurement of neighborhood dietary patterns. We used these data to develop an indicator of area-level latent demand for soda in the Census Metropolitan Area of Montreal in 2012 by applying a hierarchical Bayesian spatial model to data on soda sales from 1,097 chain retail food outlets. The utility of the indicator of latent soda demand was evaluated by assessing its association with the neighborhood relative risk of prevalent type 2 diabetes mellitus. The indicator improved the fit of the disease-mapping model (deviance information criterion: 2,140 with the indicator and 2,148 without) and enables a novel approach to nutrition surveillance.

Joint Modeling of Resistance to Six Antimicrobials in Urinary Escherichia coli Isolates in Quebec, Canada.

Empirical treatment of urinary tract infections should be based on susceptibility profiles specific to the locale and patient population. Additionally, these susceptibility profiles should account for correlations between resistance to different types of antimicrobials. We used hierarchical logistic regression models to investigate geographic, temporal, and demographic trends in resistance to six antimicrobials in community-acquired and nosocomial urinary E. coli isolates from three communities in the province of Quebec, Canada, procured between April 2010 and December 2017. A total of 74,986 community-acquired (patient age, ≥18 years) and 4,384 nosocomial isolates (patient age, ≥65 years) were analyzed. In both community-acquired and nosocomial isolates, we found geographic variation in the prevalence of resistance. Male sex (community-acquired hierarchical mean odds ratio [OR], 1.24; 95% credible interval [CI], 1.02 to 1.50; nosocomial hierarchical mean OR, 1.16, 95% CI, 0.92 to 1.41) and recent hospitalization (community-acquired hierarchical mean OR, 1.49; 95% CI, 1.33 to 1.66; nosocomial hierarchical mean OR, 1.31; 95% CI, 0.99 to 1.78) were associated with a higher risk of resistance to most types of antimicrobials. We found distinct seasonal trends in both community-acquired and nosocomial isolates, but only community-acquired isolates showed a consistent annual pattern. Ciprofloxacin resistance increased sharply with patient age. We found clinically relevant differences in antimicrobial resistance in urinary E. coli isolates between locales and patient populations in the province of Quebec. These results could help inform empirical treatment decisions for urinary tract infections. In the future, similar models integrating local, provincial, and national resistance data could be incorporated into decision support systems for clinicians.

Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.

BACKGROUND: Super learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversely affect the performance of the super learner. METHODS: In this case study, we used data from a Canadian electronic prescribing system to predict when primary care physicians prescribed antidepressants for indications other than depression. The analysis included 73,576 antidepressant prescriptions and 373 candidate predictors. We derived two super learners: one using tuned hyperparameter values for each machine learning algorithm identified through an iterative grid search procedure and the other using the default values. We compared the performance of the tuned super learner to that of the super learner using default values ("untuned") and a carefully constructed logistic regression model from a previous analysis. RESULTS: The tuned super learner had a scaled Brier score (R) of 0.322 (95% [confidence interval] CI = 0.267, 0.362). In comparison, the untuned super learner had a scaled Brier score of 0.309 (95% CI = 0.256, 0.353), corresponding to an efficiency loss of 4% (relative efficiency 0.96; 95% CI = 0.93, 0.99). The previously-derived logistic regression model had a scaled Brier score of 0.307 (95% CI = 0.245, 0.360), corresponding to an efficiency loss of 5% relative to the tuned super learner (relative efficiency 0.95; 95% CI = 0.88, 1.01). CONCLUSIONS: In this case study, hyperparameter tuning produced a super learner that performed slightly better than an untuned super learner. Tuning the hyperparameters of individual algorithms in a super learner may help optimize performance.

Defining 'actionable' high- costhealth care use: results using the Canadian Institute for Health Information population grouping methodology.

BACKGROUND: A small proportion of the population consumes the majority of health care resources. High-cost health care users are a heterogeneous group. We aim to segment a provincial population into relevant homogenous sub-groups to provide actionable information on risk factors associated with high-cost health care use within sub-populations. METHODS: The Canadian Institute for Health Information (CIHI) Population Grouping methodology was used to define mutually exclusive and clinically relevant health profile sub-groups. High-cost users (> = 90th percentile of health care spending) were defined within each sub-group. Univariate analyses explored demographic, socio-economic status, health status and health care utilization variables associated with high-cost use. Multivariable logistic regression models were constructed for the costliest health profile groups. RESULTS: From 2015 to 2017, 1,175,147 individuals were identified for study. High-cost users consumed 41% of total health care resources. Average annual health care spending for individuals not high-cost were $642; high-cost users were $16,316. The costliest health profile groups were 'long-term care', 'palliative', 'major acute', 'major chronic', 'major cancer', 'major newborn', 'major mental health' and 'moderate chronic'. Both 'major acute' and 'major cancer' health profile groups were largely explained by measures of health care utilization and multi-morbidity. In the remaining costliest health profile groups modelled, 'major chronic', 'moderate chronic', 'major newborn' and 'other mental health', a measure of socio-economic status, low neighbourhood income, was statistically significantly associated with high-cost use. INTERPRETATION: Model results point to specific, actionable information within clinically meaningful subgroups to reduce high-cost health care use. Health equity, specifically low socio-economic status, was statistically significantly associated with high-cost use in the majority of health profile sub-groups. Population segmentation methods, and more specifically, the CIHI Population Grouping Methodology, provide specificity to high-cost health care use; informing interventions aimed at reducing health care costs and improving population health.

A systematic review of aberration detection algorithms used in public health surveillance.

The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.

A population health perspective on artificial intelligence.

The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public's health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and fields within AI and illustrate through examples how they can contribute to informed decisions, with a focus on population health applications. We first introduce core concepts needed to understand modern uses of AI and then describe its sub-fields. Finally, we examine four sub-fields of AI most relevant to population health along with examples of available tools and frameworks. Artificial intelligence is a broad and complex field, but the tools that enable the use of AI techniques are becoming more accessible, less expensive, and easier to use than ever before. Applications of AI have the potential to assist clinicians, health system managers, policy-makers, and public health practitioners in making more precise, and potentially more effective, decisions.

Evaluation of the Impact of a Rotavirus Vaccine Program on Pediatric Acute Gastroenteritis Hospitalizations: Estimating the Overall Effect Attributable to the Program as a Whole and as a Per-Unit Change in Rotavirus Vaccine Coverage.

Estimation of the overall effect of a vaccine program is essential, but the effect is typically estimated for a whole program. We estimated the overall effect of the Quebec rotavirus vaccine program, launched in November 2011, and the effect for each 10% increase in rotavirus vaccine coverage on pediatric hospitalizations for all-cause acute gastroenteritis. We implemented negative binomial regressions adjusted for seasonality, long-term trends, and infection dynamics, to estimate the effect of the vaccine program as: 1) a dichotomous variable, representing program presence/absence, and linear term to account for changes in trend in the period after the program began; and 2) a continuous variable, representing rotavirus vaccine coverage. Using exposure 1, the vaccine program was associated with a 51.2% (95% confidence interval (CI): 28.5, 66.7) relative decline in adjusted weekly hospitalization rates for all-cause acute gastroenteritis as of December 28, 2014. Using exposure 2, a 10% increase in rotavirus ≥1-dose coverage was associated with a 7.1% (95% CI: 3.5, 10.5) relative decline in adjusted weekly rates, with maximum coverage of 87.0% associated with a 47.2% (95% CI: 26.9, 61.9) relative decline. Estimation of the overall effect attributable to a change in vaccine coverage might be a useful addition to standard measurement of the overall effect.

Accuracy of Administrative Health Data for Surveillance of Traumatic Brain Injury: A Bayesian Latent Class Analysis.

BACKGROUND: Traumatic brain injury surveillance provides information for allocating resources to prevention efforts. Administrative data are widely available and inexpensive but may underestimate traumatic brain injury burden by misclassifying cases. Moreover, previous studies evaluating the accuracy of administrative data surveillance case definitions were at risk of bias by using imperfect diagnostic definitions as reference standards. We assessed the accuracy (sensitivity/specificity) of traumatic brain injury surveillance case definitions in administrative data, without using a reference standard, to estimate incidence accurately. METHODS: We used administrative data from a 25% random sample of Montreal residents from 2000 to 2014. We used hierarchical Bayesian latent class models to estimate the accuracy of widely used traumatic brain injury case definitions based on the International Classification of Diseases, or on head radiologic examinations, covering the full injury spectrum in children, adults, and the elderly. We estimated measurement error-adjusted age- and severity-specific incidence. RESULTS: The adjusted traumatic brain injury incidence was 76 (95% CrI = 68, 85) per 10,000 person-years (underestimated as 54 [95% CrI = 54, 55] per 10,000 without adjustment). The most sensitive case definitions were radiologic examination claims in adults/elderly (0.48; 95% CrI = 0.43, 0.55 and 0.66; 95% CrI = 0.54, 0.79) and emergency department claims in children (0.45; 95% CrI = 0.39, 0.52). The most specific case definitions were inpatient claims and discharge abstracts (0.99; 95% CrI = 0.99, 1.00). We noted strong secular trends in case definition accuracy. CONCLUSIONS: Administrative data remain a useful tool for conducting traumatic brain injury surveillance and epidemiologic research when measurement error is adjusted for.

The Relationship Between 2009 Pandemic H1N1 Influenza During Pregnancy and Preterm Birth: A Population-based Cohort Study.

BACKGROUND: Previous studies of maternal influenza illness and preterm birth have yielded inconsistent results. Our objective was to assess the association between 2009 pandemic H1N1 (pH1N1) influenza during pregnancy and preterm birth in a large obstetrical population. METHODS: We linked a province-wide birth registry with health administrative databases to identify influenza-coded hospitalizations, emergency department visits, or physician visits among pregnant women during the 2009 H1N1 pandemic (our proxy for clinical pH1N1 influenza illness). Using Cox regression, we estimated adjusted hazard ratios (aHR) for preterm birth and spontaneous preterm birth treating influenza as a time-varying exposure. RESULTS: Among 192,082 women with a singleton live birth, 2,925 (1.5%) had an influenza-coded health care encounter during the 2009 H1N1 pandemic. Compared with unexposed pregnancy time, there was no association between exposure to the pandemic, with or without clinical influenza illness, and preterm birth (no pH1N1 diagnosis: aHR = 1.0; 95% confidence interval [CI] = 0.98, 1.1; pH1N1 diagnosis: aHR = 1.0; 95% CI = 0.88, 1.2). Among women with preexisting medical conditions, influenza was associated with increased preterm birth (aHR = 1.5; 95% CI = 1.1, 2.2) and spontaneous preterm birth (aHR = 1.7; 95% CI = 1.1, 2.6), and these associations were strongest in the third trimester and when data were analyzed to allow for a transient acute effect of influenza. CONCLUSIONS: In the general obstetrical population, there was no association between pH1N1 influenza illness and preterm birth, but women with preexisting medical conditions known to increase the risk of influenza-associated morbidity were at elevated risk.

Assessing the accuracy of using diagnostic codes from administrative data to infer antidepressant treatment indications: a validation study.

PURPOSE: To assess the accuracy of using diagnostic codes from administrative data to infer treatment indications for antidepressants prescribed in primary care. METHODS: Validation study of administrative diagnostic codes for 13 plausible indications for antidepressants compared with physician-documented treatment indications from an indication-based electronic prescribing system in Quebec, Canada. The analysis included all antidepressant prescriptions written by primary care physicians between January 1, 2003 and December 31, 2012 using the electronic prescribing system. Patient prescribed antidepressants were linked to physician claims and hospitalization data to obtain all diagnoses recorded in the past year. RESULTS: Diagnostic codes had poor sensitivity for all treatment indications, ranging from a high of only 31.2% (95% CI, 26.8%-35.9%) for anxiety/stress disorders to as low as 1.3% (95% CI, 0.0%-5.2%) for sexual dysfunction. Sensitivity was notably worse among older patients and patients with more chronic comorbidities. Physician claims data were a better source of diagnostic codes for antidepressant treatment indications than hospitalization data. CONCLUSIONS: Administrative diagnostic codes are poor proxies for antidepressant treatment indications. Future work should determine whether the use of other variables in administrative data besides diagnostic codes can improve the ability to predict antidepressant treatment indications.