Epidemiology [JOURNAL]
Sun
200 papers
Constructing G-computation Estimators: Two Case Studies in Selection Bias.
Zivich PN, Lu H
Epidemiology · 2026 Jan · PMID 41342792 · Full text
G-computation is a useful estimation method that can be adapted to address various biases in epidemiology. However, these adaptations may not be obvious for some complex causal structures. This challenge is an example of...
G-computation is a useful estimation method that can be adapted to address various biases in epidemiology. However, these adaptations may not be obvious for some complex causal structures. This challenge is an example of the much wider issue of translating a causal diagram into a novel estimation strategy. To highlight these challenges, we consider two recent cases from the selection bias literature: treatment-induced selection and co-occurrence of biases that lack a joint adjustment set. For each case study, we show how g-computation can be adapted, describe how to implement that adaptation, show some general statistical properties, and illustrate the estimator using simulation. To simplify both the theoretical study and practical application of our estimators, we express the proposed g-computation estimators as stacked estimating equations. These examples illustrate how epidemiologists can translate identification results into a g-computation estimator and study the theoretical and finite-sample properties of a novel estimator.
Bounds and E-values for Marginal Causal Effects.
Sjölander A, Ciocănea-Teodorescu I, Gabriel EE
Epidemiology · 2026 Jan · PMID 41342791 · Publisher ↗
Unmeasured confounding is an important obstacle when estimating causal effects from observational data. Ding and VanderWeele (EPIDEMIOLOGY 2016;27:368) derived bounds for causal effects, based on sensitivity parameters t...
Unmeasured confounding is an important obstacle when estimating causal effects from observational data. Ding and VanderWeele (EPIDEMIOLOGY 2016;27:368) derived bounds for causal effects, based on sensitivity parameters that quantify the maximal strength of unmeasured confounding. These bounds translate to the popular E-value metric, which quantifies the magnitude of unmeasured confounding required to "explain away" an observed association. While Ding and VanderWeele mainly focused on conditional (on measured confounders) causal effects, they also outlined how their method might be used for marginal causal effects. However, this requires specification of the sensitivity parameters at each level of the measured confounders, which is impractical in high-dimensional settings, and it yields overly conservative bounds that lack a natural E-value analog. In this article, we propose novel bounds for marginal causal effects based on Ding and VanderWeele's sensitivity parameters. The proposed bounds only require the analyst to specify the maximal values of the sensitivity parameters across all levels of the measured confounders, thus substantially reducing dimensionality. Furthermore, the proposed bounds are often narrower than Ding and VanderWeele's bounds, and they translate naturally into an E-value for marginal causation. We show how the proposed bounds can be estimated using standard regression techniques, and we illustrate through an application to publicly available data, with accompanying R code provided.
How Should We Study the Indirect Effects of Antimicrobial Treatment Strategies?: A Causal Perspective.
Gago J, Boyer C, Lipsitch M
Epidemiology · 2026 Jan · PMID 41176807 · Publisher ↗
Effective antimicrobial stewardship requires unbiased assessment of the benefits and harms of different treatment strategies, considering both immediate patient outcomes and patterns of antimicrobial resistance. In princ...
Effective antimicrobial stewardship requires unbiased assessment of the benefits and harms of different treatment strategies, considering both immediate patient outcomes and patterns of antimicrobial resistance. In principle, these benefits and harms can be expressed as causal contrasts between treatment strategies and, therefore, should be ideally suited for study under the potential outcomes framework. However, causal inference in this setting is complicated by interference between individuals (or units) due to the indirect effects of antibiotic treatment, including the spread of resistant bacteria to others. These indirect effects complicate the assessment of trade-offs as benefits are mostly due to the direct effects among those treated, while harms are more diffuse and, therefore, harder to measure. While causal frameworks and study designs that accommodate interference have previously been proposed, they have been applied predominantly to the study of vaccines, which differ from antimicrobial interventions in fundamental ways. In this article, we review these existing approaches and propose alternative adaptations tailored to the study of antimicrobial treatment strategies.
Transporting Results from a Trial to an External Target Population When Trial Participation Impacts Adherence.
Ross RK, Díaz I, Pitts AJ … +2 more , Stuart EA, Rudolph KE
Epidemiology · 2026 Jan · PMID 41144860 · Full text
Randomized clinical trials are considered the gold standard for informing treatment guidelines, but results may not generalize to real-world populations. Generalizability is hindered by distributional differences in base...
Randomized clinical trials are considered the gold standard for informing treatment guidelines, but results may not generalize to real-world populations. Generalizability is hindered by distributional differences in baseline covariates and treatment-outcome mediators. Approaches to address differences in covariates are well established, but approaches to address differences in mediators are more limited. Here, we consider the setting where trial activities that differ from usual-care settings (e.g., monetary compensation and follow-up visits frequency) affect treatment adherence. When treatment and adherence data are unavailable for the real-world target population, we cannot identify the mean outcome under a specific treatment assignment (i.e., mean potential outcome) in the target population. Therefore, we propose a sensitivity analysis in which a parameter for the relative difference in adherence to a specific treatment between the trial and the target, possibly conditional on covariates, must be specified. We discuss options for specification of the sensitivity analysis parameter based on external knowledge, including setting a range or specifying a probability distribution from which to repeatedly draw parameter values (i.e., use Monte Carlo sampling). We introduce two estimators for the mean counterfactual outcome in the target, which incorporate this sensitivity parameter, a plug-in estimator, and a one-step estimator that is double robust and supports the use of machine learning for estimating nuisance models. Finally, we apply the proposed approach to the motivating application where we transport the risk of relapse under two different medications for the treatment of opioid use disorder from a trial to a real-world population.
Chapter Four.
Swanson SA
Epidemiology · 2026 Jan · PMID 41086383 · Publisher ↗
Abstract loading — click title to view on PubMed.
Causal Identification Conditions for the Effect of Treatment in the Treated: Illustration Using the Northwest Germany Stroke Registry.
Wiener C, Zivich PN, Kurth T … +4 more , Jonsson-Funk M, Breskin A, Berger K, Cole SR
Epidemiology · 2026 Jan · PMID 41086382 · Full text
BACKGROUND: A set of conditions sufficient to identify the average treatment effect (ATE) in observational data includes no measurement error, causal consistency, and conditional mean exchangeability with positivity. The...
BACKGROUND: A set of conditions sufficient to identify the average treatment effect (ATE) in observational data includes no measurement error, causal consistency, and conditional mean exchangeability with positivity. The average treatment effect in the treated (ATT) is identified under a subset of these conditions, specifically relaxing the symmetry of conditional exchangeability with positivity. METHODS: We reanalyzed data from the Northwest Germany Stroke Registry (2020-2021) to estimate the effect of tissue-type plasminogen activator (tPA) on inhospital mortality. We used inverse probability of treatment weighting for the ATE and standardized mortality ratio (SMR) weighting for the ATT. We also conducted 5000 simulations of 6000 patients, varying the prevalence of treatment indication. We generated homogeneous and heterogeneous treatment effects under two scenarios: (1) positivity holds for treated and untreated groups and (2) positivity only holds for the treated. RESULTS: Among 6000 patients, 20% received tPA, and 5% died. The inverse probability of treatment weighting risk ratio (ATE) was 1.70 (95% CI: 0.80, 3.64), while the SMR-weighted risk ratio (ATT) was 0.82 (95% CI: 0.59, 1.14). In simulations, ATT estimates of the risk ratio remained unbiased when we violated positivity for the untreated. However, ATE estimates showed increasing log-scale bias with increased nonpositivity, ranging from 0.2 to 1.1 for homogeneous effects and 0.2 to 0.9 for heterogeneous effects. CONCLUSIONS: While ATE estimates suggested harm from tPA, ATT estimates suggest a protective effect. Simulations show that when one-sided positivity violations exist, epidemiologists can leverage weaker identification conditions to consistently estimate the ATT, even when estimates of the ATE are biased.
If You Want to Know the End, Look at the End.
Lash TL
Epidemiology · 2026 Jan · PMID 41086372 · Publisher ↗
Abstract loading — click title to view on PubMed.
Unbiased Estimates Using Temporally Aggregated Outcome Data in Time Series Analysis: Generalization to Different Outcomes, Exposures, and Types of Aggregation.
Basagaña X, Ballester J
Epidemiology · 2026 Jan · PMID 41036805 · Full text
BACKGROUND: A new method for time series analysis was recently formulated and implemented that uses temporally aggregated outcome data to generate unbiased estimates of the underlying association between temporally disag...
BACKGROUND: A new method for time series analysis was recently formulated and implemented that uses temporally aggregated outcome data to generate unbiased estimates of the underlying association between temporally disaggregated outcome and covariate data. However, the performance of the method was only tested in the context of the delayed nonlinear relation between temperature and mortality, and only in the case of the aggregation of sets of consecutive days. METHODS: We conduct a simulation analysis to test the performance of the method using (1) mortality and hospital admissions as health outcomes, (2) temperature and nitrogen dioxide as exposures, and (3) the three aggregation schemes most widely used in open-access health data, including aggregations of sets of nonconsecutive days. RESULTS: With sufficient data for analysis, the method can recover the underlying association for all combinations of outcomes, exposures, and aggregation schemes. The bias and variability of the estimates increase with the degree of aggregation of the outcome data, and they decrease with increasing sample size (length of dataset, number of cases). Remarkably, estimates are also unbiased even in extreme cases with weekly outcome data in an association confounded by the day of the week, such as those of air pollution models. CONCLUSIONS: With sufficient data, the method is able to flexibly generate unbiased estimates, generalizing previous results to other outcomes, exposures, and types and degrees of aggregation. Such results can boost the use of available temporally aggregated health data for research, translation, and policymaking, especially in low-resource and rural areas.
Causal Mediation Analysis with Mediator-outcome Confounders Affected by Exposure: On Definition and Identification of Generalized Natural Indirect Effect.
Chen YL, Yu T, Lin SH
Epidemiology · 2026 Jan · PMID 41021314 · Publisher ↗
Causal mediation analysis aims to disentangle the pathways through which an exposure influences an outcome. In the presence of mediator-outcome confounders affected by exposure (intermediate confounders), the natural ind...
Causal mediation analysis aims to disentangle the pathways through which an exposure influences an outcome. In the presence of mediator-outcome confounders affected by exposure (intermediate confounders), the natural indirect effect (NIE) is not identifiable under nonparametric structural equation models (SEMs) with independent errors. To address this challenge, we focus on the indirect pathway and introduce a novel class of indirect effect measures, referred to as generalized NIEs, of which the NIE is a special case. In particular, we introduce a case of generalized NIE defined through a randomized intervention, which, under the nonparametric SEM with independent errors, coincides with the interventional indirect effect (IIE)-even though identifying the IIE generally does not rely on the cross-world assumptions implied by nonparametric SEM with independent errors. Furthermore, when an additional no-heterogeneity assumption is imposed, the NIE becomes equal to this generalized NIE and hence is identifiable. Unlike prior approaches, we propose new indirect effect measures criteria that ensure valid mediation interpretation even in the presence of intermediate confounders. Under traditional identification assumptions alone, the IIE fails to satisfy these criteria. In contrast, all proposed generalized NIEs meet them, providing a wide range of options beyond the existing measures. Our findings highlight the generalized NIEs as a more pragmatic and reasonable alternative in settings where intermediate confounders are inevitable.
Does Delayed Response Due to Busy Ambulances Impact Risk of Death and Hospital Service Use?: A Cohort Study of 240,000 Medical Emergencies.
Asheim A, Næss LE, Krüger A … +8 more , Uleberg O, Dale J, Haugland H, Ulvin OE, Nilsen SM, Waaler Bjørnelv GM, Wattø JO, Bjørngaard JH
Epidemiology · 2025 Nov · PMID 40996067 · Full text
OBJECTIVES: When ground ambulances are busy with any task, delays are likely for concurrent emergencies. Whereas time-critical conditions are affected by delays, general impacts remain unclear. We aimed to assess how del...
OBJECTIVES: When ground ambulances are busy with any task, delays are likely for concurrent emergencies. Whereas time-critical conditions are affected by delays, general impacts remain unclear. We aimed to assess how delayed ambulance response due to busy ambulances affects risk of death and use of hospital services. METHODS: We studied individuals with out-of-hospital emergencies that precipitated a call to the medical emergency number in Central Norway from 2013 to 2022. Emergency service and hospital data were linked to assess subsequent death and hospitalizations. We addressed potential bias by multivariable adjustment and a natural experiment: For emergencies that occurred in the same area at similar times, we compared outcomes for patients with differences in busy ambulances to analyze delays in response that were arguably unrelated to prioritization due to the patient severity. RESULTS: Among 239,320 acute emergencies, 4.1% of patients died within 7 days. An interquartile range of variation in the probability a busy ambulance was associated with a 2.9-minute delay (95% confidence interval [CI] = 2.8, 3.0). Overall, a 5-minute delay was associated with a risk difference of 0.10 percentage points in the risk of death (95% CI = -0.17, 0.36) and 1.24 for hospitalization (95% CI = 0.59, 1.94). The cost of hospital treatment within 1 year increased by 616 euros (95% CI = 183, 1069). CONCLUSION: While we found no substantial increase in the overall risk of death associated with delayed ambulance response, the observed rise in hospital costs suggests a potential increase in morbidity.
Illustrating an Adaptive Prespecification Framework for Observational Research: Target Trial Emulations Comparing Immunomodulator Treatments for COVID-19.
Weckstein AR, Frajzyngier V, Vititoe SE … +11 more , Baglivo A, Beebe E, Govil P, Bradley MC, Perez-Vilar S, Liu W, Rivera DR, Lasky T, Chakravarty A, Garry EM, Gatto NM
Epidemiology · 2025 Nov · PMID 40996066 · Full text
Rigid prespecification can be impractical for noninterventional studies using secondary datasets, where data-driven flexibility is often required. Using target trial emulations comparing immunomodulator treatments for CO...
Rigid prespecification can be impractical for noninterventional studies using secondary datasets, where data-driven flexibility is often required. Using target trial emulations comparing immunomodulator treatments for COVID-19, we piloted an adaptive strategy that accommodates warranted mid-course refinements within a prespecified framework. Our preregistered protocol outlined an initial study plan along with predetermined diagnostic thresholds and contingencies. Implementation proceeded through sequential phases, allowing researcher decisions to be guided by prespecified criteria under varying degrees of blinding to results. The adaptive approach led to alterations in the underlying target trial and to the analysis plan used for emulation, strengthening the plausibility of causal assumptions and improving the relevance of findings. During the initial baseline phase, indicated contingencies included sample restrictions, redefining treatments from class-level to product-specific comparisons, a revised propensity score model, and weight truncation. In the subsequent postbaseline phase, diagnostic checks triggered a modified causal contrast, inverse probability of censor weighting to address noncompliance, cause-specific hazard estimation to contextualize competing events, and additional reporting of hazard ratios for progressively truncated follow-up periods. For a secondary study objective, the adaptive framework allowed for some iterative attempts to improve validity while providing a clear stopping point. Similar approaches could lend transparent structure to the process of learning what causal questions the data are equipped to support. Beyond guarding against researcher bias, prespecification of adaptive protocols may promote more robust designs by encouraging investigators to be explicit about their assumptions, strategies for interrogating those assumptions, and specific criteria for determining when and how deviations may be required.
OpenSAFELY: Effectiveness of COVID-19 Vaccination in Children and Adolescents.
Andrews CD, Parker EPK, Horne E … +20 more , Walker V, Palmer T, Schaffer AL, Green ACA, Curtis HJ, Walker AJ, Bridges L, Wood C, Speed V, Bates C, Cockburn J, Parry J, Mehrkar A, MacKenna B, Bacon SCJ, Goldacre B, Hernan MA, Sterne JAC, Hulme WJ, OpenSAFELY Collaborative
Epidemiology · 2026 Jan · PMID 40985520 · Full text
BACKGROUND: We assessed the safety and effectiveness of the first- and second-dose BNT162b2 COVID-19 vaccination, offered as part of the national COVID-19 vaccine roll-out from September 2021, in children and adolescents...
BACKGROUND: We assessed the safety and effectiveness of the first- and second-dose BNT162b2 COVID-19 vaccination, offered as part of the national COVID-19 vaccine roll-out from September 2021, in children and adolescents in England. METHODS: Our observational study using OpenSAFELY-TPP, included adolescents aged 12-15 years and children aged 5-11 years. It compared individuals receiving (1) the first vaccination to unvaccinated controls and (2) the second vaccination to single-vaccinated controls. We matched vaccinated individuals with controls on age, sex, and other important characteristics. Outcomes were positive SARS-CoV-2 test (adolescents only), COVID-19 accident and emergency (A&E) attendance, COVID-19 hospitalization, COVID-19 critical care admission, and COVID-19 death; with safety outcomes, A&E attendance, unplanned hospitalization, pericarditis, and myocarditis. RESULTS: Among 820,926 previously unvaccinated adolescents, 20-week incidence rate ratios (IRRs) comparing vaccination with no vaccination were 0.74 for positive SARS-CoV-2 test, 0.60 for COVID-19 A&E attendance, and 0.58 for COVID-19 hospitalization. Among 441,858 adolescents who had received the first vaccination, IRRs comparing second dose with single-vaccination were 0.67 for positive SARS-CoV-2 test, 1.00 for COVID-19 A&E attendance, and 0.60 for COVID-19 hospitalization. In both children groups, COVID-19-related outcomes were too rare to allow IRRs to be estimated precisely. Across all analyses, there were no COVID-19-related deaths, and fewer than seven COVID-19-related critical care admissions. Myocarditis and pericarditis were documented only in the vaccinated groups, with rates of 27 and 10 cases/million after the first and second doses, respectively. CONCLUSIONS: BNT162b2 vaccination in adolescents reduced COVID-19 A&E attendance and hospitalization, although these outcomes were rare. Protection against positive SARS-CoV-2 tests was transient.
Pregnancy Length Measurement Error: A Comparison of Last Menstrual Period and Ultrasonography with Ovulation-based Estimation.
Doss GL, Daniels JL, Mumford SL … +6 more , Poole C, Steiner AZ, Schisterman EF, Silver RM, Klawans MR, Jukic AMZ
Epidemiology · 2026 Jan · PMID 40981999 · Full text
BACKGROUND: Last menstrual period (LMP) and ultrasound are commonly used to estimate pregnancy length. Ovulation, which precedes fertilization by ≤24 hours, should give a more accurate estimate. METHODS: The Effects of A...
BACKGROUND: Last menstrual period (LMP) and ultrasound are commonly used to estimate pregnancy length. Ovulation, which precedes fertilization by ≤24 hours, should give a more accurate estimate. METHODS: The Effects of Aspirin in Gestation and Reproduction (EAGeR) trial preconceptionally enrolled participants from four US medical centers from 2006 to 2012. Participants in our analyses delivered a singleton live birth, had prospectively recorded LMP, ovulation detected by a fertility monitor, and early first-trimester crown-rump length measurements. We estimated pregnancy length, preterm birth (<37 weeks) prevalence, and sex-specific size for gestational age by LMP, ultrasound, and ovulation. We report the sensitivity and specificity of LMP and ultrasound for detecting preterm birth compared with our gold standard, ovulation. RESULTS: In our analytic sample (n = 392), pregnancies were longest, preterm birth was least common (prevalence = 0.07, 95% confidence interval [CI]: 0.04, 0.10), and small for gestational age was most common when measured by LMP. Pregnancies were shortest, preterm birth was most common (prevalence = 0.10, 95% CI: 0.07, 0.13), and small for gestational age was least common when measured by ultrasound. The prevalence of preterm birth was 0.08 (95% CI: 0.06, 0.12) by ovulation. Using ovulation as the gold standard measure, LMP was less sensitive in detecting preterm birth (0.76, 95% CI: 0.61, 0.90) than ultrasound (0.94, 95% CI: 0.86, 1.00). The specificity of LMP was 1.00 (95% CI: 0.99, 1.00), and the specificity of ultrasound was 0.97 (95% CI: 0.96, 0.99). CONCLUSION: While this study's pregnancy length information is the best-case scenario, we observed misclassification of outcomes that may inform future bias analyses.
Erratum: Definition and Interpretation of Separable Path-specific Effects With Multiple Ordered Mediators.
Chen YL, Lin SH
Epidemiology · 2026 Jan · PMID 40961918 · Publisher ↗
Abstract loading — click title to view on PubMed.
Using Measurement Error Parameters From Validation Data.
Ross RK, Fox MP, Lesko CR … +3 more , Rudolph JE, Zalla LC, Edwards JK
Epidemiology · 2026 Jan · PMID 40952412 · Publisher ↗
Measurement error is ubiquitous in the data used for epidemiologic research and can lead to meaningful information bias. Analytic approaches to address measurement error and quantitative bias analyses examining the poten...
Measurement error is ubiquitous in the data used for epidemiologic research and can lead to meaningful information bias. Analytic approaches to address measurement error and quantitative bias analyses examining the potential impact of measurement error on study results often leverage validation data that provides information about the relationship between the true measure and the available imperfect measure, quantified by measurement error parameters such as sensitivity and specificity in the binary case. Leveraging validation data often requires transporting these measurement error parameters from the validation data to the target sample of interest (that may or may not include individuals from the validation data). In this paper, we examine the independence assumptions required to transport measurement error parameters from the validation data to the target sample, highlighting how the required assumption differs depending on the form of the measurement error parameters (i.e., whether it is the true measure conditional on the imperfect measure or vice versa). We then illustrate how diagrams can clarify the conditions under which the required assumptions hold and thus what measurement error parameters can be validly transported. This work provides practical tools for epidemiologists to address measurement error using validation data in applied research.
Use of Routinely Collected Data to Classify Planned Mode of Delivery Among Pregnancies With a Previous Cesarean Delivery: A Validation Study.
Brown MM, Yu YH, Hutcheon JA … +5 more , Woolcott CG, Allen VM, Fahey J, Gagnon I, Mehrabadi A
Epidemiology · 2026 Jan · PMID 40951986 · Full text
BACKGROUND: Counseling on the harms and benefits of a planned vaginal versus a planned repeat cesarean delivery often relies on observational studies using routinely collected (or administrative) data. However, the accur...
BACKGROUND: Counseling on the harms and benefits of a planned vaginal versus a planned repeat cesarean delivery often relies on observational studies using routinely collected (or administrative) data. However, the accuracy of planned (rather than actual) mode of delivery classifications in such data remains unknown. This study aimed to evaluate the validity of an administrative data-based algorithm to identify planned vaginal and planned cesarean deliveries among individuals with a previous cesarean. METHODS: An algorithm based on diagnostic and procedural codes was applied to records from the Nova Scotia Atlee Perinatal Database. Included were individuals with a previous cesarean eligible for a trial of labor between 2017 and 2019. We compared the classification of planned mode of delivery using the algorithm with that determined through review of a random sample of 200 medical charts. We estimated sensitivity, specificity, and predictive values with 95% confidence intervals (CIs). RESULTS: Based on the chart review, 80 deliveries (40%) were planned vaginal deliveries. The algorithm had an estimated sensitivity of 99% (95% CI: 93%, 100%), specificity of 96% (95% CI: 91%, 99%), positive predictive value of 94% (95% CI: 87%, 98%), and negative predictive value of 99% (95% CI: 95%, 100%) for identifying planned vaginal deliveries. CONCLUSIONS: An algorithm based on routinely collected data accurately classified planned vaginal and planned cesarean deliveries among individuals with a previous cesarean. These findings suggest that studies using similar algorithms to inform counseling on planned mode of delivery in this population are minimally impacted by misclassification of this data.
Comparative Risks of Opioid Overdose in Patients on Oxycodone Initiating Selective Serotonin Reuptake Inhibitors.
Bykov K, Basham CA, Khan NF … +6 more , Glynn RJ, Belitkar S, Vine SM, Bea S, Bateman BT, Huybrechts KF
Epidemiology · 2026 Jan · PMID 40951958 · Full text
BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) are often co-prescribed with oxycodone, yet may potentiate respiratory depression. We aimed to assess the comparative effects of SSRIs on opioid overdose when a...
BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs) are often co-prescribed with oxycodone, yet may potentiate respiratory depression. We aimed to assess the comparative effects of SSRIs on opioid overdose when added to oxycodone. METHODS: Using US commercial and public health insurance claims data (2004 - 2020), we conducted a cohort study in adults who initiated SSRI while on oxycodone. We assigned patients to one of five exposures (sertraline, citalopram, escitalopram, fluoxetine, and paroxetine) and followed them for opioid overdose (hospitalization or emergency room visit) for 365 days and while they stayed on both oxycodone and index SSRI. We used propensity score matching weights to adjust for potential confounders and weighted Cox proportional hazard models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: Among 753,263 eligible individuals (mean age 46 years [SD 16]; 527,340 females [70%]), 221,792 initiated sertraline, 173,352 citalopram, 153,968 escitalopram, 126,954 fluoxetine, and 77,197 paroxetine. Overall, 1250 opioid overdose events occurred, with incidence rates ranging from 10.8 to 15.2 per 1,000 person-years across individual SSRIs. Weighted HRs, relative to sertraline, were 1.24 (95% CI = 1.04, 1.50) for citalopram, 1.22 (95% CI = 1.01, 1.47) for escitalopram, 1.26 (95% CI = 1.04, 1.53) for fluoxetine, and 1.26 (95% CI = 1.01, 1.57) for paroxetine. No differences were observed across SSRIs other than sertraline. CONCLUSIONS: In this study of individuals who added an SSRI to oxycodone, the incidence of opioid overdose was low. Patients who initiated sertraline experienced overdose at a slightly lower rate than patients who initiated other SSRIs.
Spatial Difference-in-Differences with Bayesian Disease Mapping Models.
Bonander C, Blangiardo M, Strömberg U
Epidemiology · 2026 Jan · PMID 40928072 · Full text
Bayesian disease-mapping models are widely used in small-area epidemiology to account for spatial correlation and stabilize estimates through spatial smoothing. In contrast, difference-in-differences (DID) methods-common...
Bayesian disease-mapping models are widely used in small-area epidemiology to account for spatial correlation and stabilize estimates through spatial smoothing. In contrast, difference-in-differences (DID) methods-commonly used to estimate treatment effects from observational panel data-typically ignore spatial dependence. This paper integrates disease-mapping models into an imputation-based DID framework to address spatially structured residual variation and improve precision in small-area evaluations. The approach builds on recent advances in causal panel data methods, including two-way Mundlak estimation, to enable causal identification equivalent to fixed effects DID while incorporating spatiotemporal random effects. We implement the method using Integrated Nested Laplace Approximation, which supports flexible spatial and temporal structures and efficient Bayesian computation. Simulations show that, when the spatiotemporal structure is correctly specified, the approach improves precision and interval coverage compared with standard DID methods. We illustrate the method by evaluating local ice cleat distribution programs in Swedish municipalities.
Sensitivity of Cancer Registry Linkage with Missing or Incomplete Social Security Number and Implications for Cancer Cohorts.
McCullough LE, Deka A, Newton C … +5 more , Briggs P, Gardner E, Ward KC, Teras LR, Patel AV
Epidemiology · 2026 Jan · PMID 40923632 · Publisher ↗
BACKGROUND: Linking cancer cohort participants to state cancer registries typically relies on personally identifiable information, including social security numbers (SSN), which uniquely identify individuals. However, co...
BACKGROUND: Linking cancer cohort participants to state cancer registries typically relies on personally identifiable information, including social security numbers (SSN), which uniquely identify individuals. However, complete SSN collection can be limited due to privacy concerns. This study evaluates the sensitivity of cancer registry linkage using partial or missing SSN and examines differences by demographic characteristics. METHODS: Using data from 284,361 participants in the Cancer Prevention Study-3, we conducted probabilistic linkages with cancer registries in Georgia, Ohio, and Texas using Match*Pro software. Participants were linked using combinations of personally identifiable information: complete SSN, partial SSN (last four digits), and missing SSN. We compared the sensitivity of linkages before and after manual review and stratified by sex, age, and race-ethnicity. RESULTS: Before manual review, the sensitivity for missing and partial SSNs was 92.5%. Sensitivity improved to 98.6% for missing SSN and 98.8% for partial SSN after manual review. We observed no notable heterogeneity by sex, age, or race-ethnicity, with sensitivity exceeding 87% across all subgroups. Manual review substantially reduced uncertain matches, contributing to high linkage accuracy. CONCLUSION: This study demonstrates that high sensitivity in cancer registry linkage can be achieved without a complete SSN, provided other personally identifiable information (e.g., name, date of birth, longitudinal address) is available. These findings support the feasibility of accurate cancer case identification in cohorts with limited SSN data, particularly for historically marginalized populations, and underscore the importance of designing inclusive population-based cancer studies.
Doubly Robust Control Outcome Calibration Approach Estimation of Conditional Effects with Uncontrolled Confounding.
Loh WW
Epidemiology · 2026 Jan · PMID 40919757 · Publisher ↗
Drawing causal conclusions about nonrandomized exposures rests on assuming no uncontrolled confounding, but it is rarely justifiable to rule out all putative violations of this routinely made yet empirically untestable a...
Drawing causal conclusions about nonrandomized exposures rests on assuming no uncontrolled confounding, but it is rarely justifiable to rule out all putative violations of this routinely made yet empirically untestable assumption. Alternatively, this assumption can be avoided by leveraging negative control outcomes using the control outcome calibration approach (COCA). The existing COCA estimator of the average causal effect relies on correctly specifying the mean negative control outcome model, with a closed-form solution for the main exposure effect. In this article, we propose a doubly robust COCA estimator of the average causal effect that relaxes this modeling requirement and permits effect modification through covariate-exposure interaction terms. The doubly robust COCA estimator uses correctly specified exposure and focal outcome models to protect against biases from an incorrectly specified negative control outcome model. The ability to obtain unbiased point estimates and inferences is empirically evaluated using a simulation study. We demonstrate doubly robust COCA using a publicly available dataset to evaluate the effect of volunteering on mental health. This method is practical and easy to implement and permits unbiased estimation of causal effects even amid uncontrolled confounding.
About
- Frequency
- Sun
- Papers found
- 200
- RSS feed
- Subscribe