Chatton A, Schomaker M, Luque-Fernandez MA
… +2 more, Platt RW, Schnitzer ME
Epidemiology
· 2025 Nov · PMID 40856329
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BACKGROUND: Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this assumption can be challenging because it...BACKGROUND: Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this assumption can be challenging because it usually relies on multiple parametric propensity score models, unlikely to all be correctly specified. Therefore, we propose a new algorithm, called sequential Positivity Regression Tree (sPoRT), to overcome this issue and identify the subgroups found to be violating this assumption, allowing for insights about the nature of the violations and potential solutions. METHODS: We present different versions of sPoRT based on either stratifying or pooling over time under static or dynamic treatment strategies. This methodologic development was motivated by a real-life application of the impact of the timing of initiation of HIV treatment with and without smoothing over time, which we also use to demonstrate the method. RESULTS: The illustration of sPoRT demonstrates its easy use and the interpretability of the results for applied epidemiologists. Furthermore, an R notebook showing how to use sPoRT in practice is available at github.com/ArthurChatton/sPoRT-notebook. CONCLUSIONS: The sPoRT algorithm provides interpretable subgroups violating the sequential positivity violation, allowing patterns and trends in the confounders to be easily identified. We finally provided practical implications and recommendations when positivity violations are identified.
Inose S, Williams NT, Hoffman KL
… +3 more, Perry A, Díaz I, Rudolph KE
Epidemiology
· 2025 Nov · PMID 40838610
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BACKGROUND: We estimated the extent to which the risk of developing opioid use disorder or overdose over 15 months of follow-up would be affected by applying prescription opioid dose and duration reductions to subsets of...BACKGROUND: We estimated the extent to which the risk of developing opioid use disorder or overdose over 15 months of follow-up would be affected by applying prescription opioid dose and duration reductions to subsets of newly diagnosed musculoskeletal pain patients, defined in terms of the "riskiness" level of their initial opioid prescription. METHODS: We studied a cohort of nonpregnant Medicaid patients, aged 19-63 years, without cancer nor on palliative care, who were opioid-naive, newly diagnosed with musculoskeletal pain, and were prescribed an opioid within 3 months from the diagnosis date (N = 324,389). We applied a novel statistical approach to estimate the effects of local modified treatment policies (a generalization of the average treatment effect on the treated). Specifically, we estimated the expected difference in risk of developing opioid use disorder or opioid overdose by sequential 3-month follow-ups among patients with different levels of opioid prescribing had those patients had their prescription opioid dose and/or duration decreased by 20% versus no hypothetical intervention, and had they remained uncensored. RESULTS: We estimated clinically modest effects on absolute opioid use disorder risk when universally reducing opioid prescription dose and duration by 20% across the cohort. In contrast, we estimated much larger, clinically relevant reductions in absolute risk of one percentage point or greater when assessing the localized effects of: (1) a 20% reduction in dose among individuals with doses ≥90 morphine milligram (mg) equivalents, (2) a 20% reduction in days supplied among individuals with >30 days supplied, and (3) 20% reductions in both dose and duration among those with ≥50 morphine mg equivalents and >7 days supplied. CONCLUSIONS: We estimate that reductions in opioid prescribing may have a limited impact on the risk of opioid use disorder when applied broadly but possibly meaningful reductions in risk when applied to those with riskier prescriptions.
Clancey E, Mietchen MS, McMichael C
… +1 more, Lofgren ET
Epidemiology
· 2025 Nov · PMID 40838608
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Institutions of higher education faced a number of challenges during the COVID-19 pandemic. Chief among them was whether or not to reopen during the second wave of COVID-19 in the fall of 2020, which was controversial be...Institutions of higher education faced a number of challenges during the COVID-19 pandemic. Chief among them was whether or not to reopen during the second wave of COVID-19 in the fall of 2020, which was controversial because incidence in young adults was on the rise. The migration of students back to campuses worried many that transmission within student populations would spread into surrounding communities. In light of this, many colleges and universities implemented mitigation strategies, with varied degrees of success. Washington State University, located in the city of Pullman in Whitman County, WA, is an example of this type of university-community co-location, where the role of students returning to the area for the fall 2020 semester was contentious. Using COVID-19 incidence in Pullman, WA, reported to the Whitman County Health Department, we retrospectively study the transmission dynamics that occurred between the student and community subpopulations in fall 2020. We develop a two-population ordinary differential equations mechanistic model to infer transmission rates within and across the university student and community subpopulations. We use results from Bayesian parameter estimation to determine if exponential transmission of COVID-19 occurred in Pullman, WA, and the magnitude of cross-transmission from students to community members. We find these results are consistent with the estimation of the time-varying reproductive number that outbreak potential was minimal and resolved quickly, and conclude that the students returning to Washington State University-Pullman did not place the surrounding community at disproportionate risk of COVID-19 during fall 2020 when mitigation efforts were in place.
Epidemiology
· 2025 Nov · PMID 40815066
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BACKGROUND: The Super Learner is an ensemble learning method that has been widely used with doubly robust causal effect estimators. It is recommended to deploy the Super Learner with a diverse library of algorithms. To o...BACKGROUND: The Super Learner is an ensemble learning method that has been widely used with doubly robust causal effect estimators. It is recommended to deploy the Super Learner with a diverse library of algorithms. To our knowledge, however, the magnitude of the improvements gained by including many algorithms has not yet been systematically evaluated in common epidemiologic research settings. METHODS: We applied Super Learning with two doubly robust estimators, augmented inverse probability weighting (AIPW) and targeted minimum loss-based estimation (TMLE), to estimate the average treatment effect (ATE) of high periconceptional dietary fruit and vegetable density on the risk of preeclampsia among 7,923 women from the nuMoM2b study. Using a reference ensemble with a diverse library of algorithms, we compared estimates under different sets of algorithms included in the Super Learner to evaluate whether ATE estimates were sensitive to library choices. RESULTS: The doubly robust estimators fitted with the reference Super Learner ensemble suggested ≥2.5 cups/1,000 kcal of total fruit and vegetable density was associated with a lower risk of preeclampsia. ATE estimated on the risk difference scale by AIPW was -0.019 (95% confidence interval = -0.036, -0.003) and by TMLE was -0.023 (95% confidence interval = -0.039, -0.007). Excluding any individual algorithm from the reference ensemble had little impact on estimates from either AIPW or TMLE. However, relying on a single algorithm (e.g., extreme gradient boosting) yielded results that were much more variable. CONCLUSION: Our empirical findings support recommendations to build ensemble learners for doubly robust estimators using a diverse array of flexible machine learning algorithms.
Plana-Ripoll O, Momen NC, Gallego-Alabanda D
… +5 more, Chen D, Hansen SN, Melis GG, Pedersen CB, Agerbo E
Epidemiology
· 2025 Nov · PMID 40815060
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BACKGROUND: Incidence rates and cumulative incidences estimated using registers (e.g., electronic healthcare records) might be biased by including cases diagnosed before the inception of the register as being at risk. Wa...BACKGROUND: Incidence rates and cumulative incidences estimated using registers (e.g., electronic healthcare records) might be biased by including cases diagnosed before the inception of the register as being at risk. Washout periods can identify and exclude prevalent cases from analyses, but the impact of washout duration on estimates is unknown. We estimated risks of mental disorders according to different washout period durations. METHODS: This population-based cohort included all 6,478,162 individuals aged 1-80 years living in Denmark in 2010-2021. Using hospital contacts in 2010-2021, we estimated incidence rates and cumulative incidence of mental disorders according to different washout period durations (0, 1, 2, 5, 15, and 41 years) based on hospital contacts prior to 2010. RESULTS: Without a washout period, the lifetime cumulative incidence of any mental disorder was 49.4% (95% confidence interval [CI]: 49.2%, 49.5%) for females and 45.1% (95% CI: 45.0%, 45.2%) for males. Estimates decreased when we increased the washout, reaching a lifetime incidence of 40.3% (95% CI: 40.1%, 40.4%) for females and 36.6% (95% CI: 36.5%, 36.8%) for males when using all available data (41 years of washout). Without a washout period, estimates for specific mental disorder types were up to 60% higher than those obtained with the maximum washout period, but the bias in absolute terms depended on the underlying risks. CONCLUSIONS: While including all cases identifiable in a register may decrease uncertainty, the inclusion of prevalent cases as being at risk may lead to substantially overestimated measures. We highlight the need for caution when using administrative registers and electronic healthcare databases.
Epidemiology
· 2025 Nov · PMID 40747909
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BACKGROUND: The effects of life course financial hardship on fertility have not been well studied. METHODS: We examined the association between life course financial hardship and fecundability in Pregnancy Study Online (...BACKGROUND: The effects of life course financial hardship on fertility have not been well studied. METHODS: We examined the association between life course financial hardship and fecundability in Pregnancy Study Online (PRESTO), a preconception cohort study of US and Canadian pregnancy planners aged 21-45 years who identified as female (2013-2023; N = 6,377). We followed participants up to 12 months or until pregnancy. Participants reported financial hardship in childhood (≤11 years), adolescence (12-17 years), and adulthood (≥18 years) via three questions: not having enough money for living expenses; needing to borrow money for medical expenses; or receiving public assistance. We used inverse probability-weighted proportional probabilities models to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs), accounting for time-dependent confounding and selection bias. RESULTS: Compared with no financial hardship, financial hardship during any life stage was associated with slightly reduced fecundability (FR = 0.93, 95% CI: 0.86, 1.0). Associations were similar for financial hardship during childhood and adolescence; however, those experiencing financial hardship during adulthood had lower fecundability (FR = 0.83, 95% CI: 0.77, 0.90). The association between adolescent financial hardship and fecundability was similar among those with and without childhood financial hardship. However, the association of adult financial hardship with fecundability was stronger among those who experienced hardship earlier in life (i.e., adult financial hardship among those with child/adolescent financial hardship: FR = 0.77; 95% CI: 0.64, 0.93). CONCLUSION: Adulthood is a sensitive period for the effects of financial hardship on fecundability. Moreover, cumulative financial hardship across the life course was associated with greater reductions in fecundability.
Ahmed AM, Musty A, Rigdon J
… +1 more, Hutcheon JA
Epidemiology
· 2025 Nov · PMID 40747905
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BACKGROUND: Some studies examining associations between maternal injuries and preterm birth reported null or counterintuitive protective effects, especially for 3rd-trimester injuries, likely due to time-related biases....BACKGROUND: Some studies examining associations between maternal injuries and preterm birth reported null or counterintuitive protective effects, especially for 3rd-trimester injuries, likely due to time-related biases. METHODS: This retrospective cohort study comprised all births occurring at the Atrium Health Wake Forest Baptist health system between 2018 and 2024. We ascertained maternal injuries using validated diagnostic codes and defined preterm birth as gestational age at delivery <37 weeks. We estimated associations between maternal injuries and preterm birth with two approaches. We used logistic regression for time-fixed analysis (injury at any point in pregnancy yes/no and preterm birth yes/no) and Cox proportional hazards models for time-varying analysis (i.e., time-varying injury definition, restricted follow-up to periods when pregnancies were at risk of preterm birth). RESULTS: Among 58,897 births, 1,801 women (3.1%) experienced maternal injuries during pregnancy. With the time-varying approach, maternal injuries were associated with increased risk of preterm birth (adjusted hazard ratio [HR]: 1.16; 95% confidence interval [CI] = 1.01, 1.32). Trimester-specific analyses showed positive associations for all trimesters, with higher effect estimates observed for 2nd and 3rd trimester injuries (adjusted HRs: 1.17; 95% CI = 0.97, 1.42) and 1.22 (95% CI = 0.92, 1.61), respectively. With time-fixed analyses, associations for any injury were underestimated, compared with time-varying analyses, and results for 3rd trimester injuries showed counterintuitive negative associations (adjusted odds ratio: 0.73 [0.54, 0.98]). CONCLUSIONS: Time-related biases typically underestimate associations between maternal injuries and preterm birth, particularly for 3rd - trimester injuries. Rigorous study design and analytical methods that account for time-related biases are crucial in studies investigating adverse outcomes after maternal injuries.
Epidemiology
· 2025 Nov · PMID 40658050
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Epidemiologic analyses that aim to quantify exposure effects on disease progression are not uncommon. Understanding the implications of these studies, however, is complicated, in part because different causal estimands c...Epidemiologic analyses that aim to quantify exposure effects on disease progression are not uncommon. Understanding the implications of these studies, however, is complicated, in part because different causal estimands could, at least in theory, be the target of such analyses. Here, to facilitate interpretation of these studies, we describe different settings in which causal questions related to disease progression can be asked, and consider possible estimands. For clarity, our discussion is structured around settings defined based on two factors: whether the disease occurrence is manipulable or not, and the type of outcome. We describe relevant causal structures and sets of response types, which consist of joint potential outcomes of disease occurrence and disease progression, and argue that settings where interventions to manipulate disease occurrence are not plausible are more common, and that, in this case, principal stratification might be an appropriate framework to conceptualize the analysis. Further, we suggest that the precise definition of the outcome of interest, in particular of what constitutes its permissible levels, might determine whether potential outcomes linked to disease progression are definable in different strata of the population. Our hope is that this paper will encourage additional methodological work on causal analysis of disease progression, as well as serve as a resource for future applied studies.
Bergenfeld I, Richardson RA, Hadd AR
… +5 more, Clark CJ, Haardörfer R, Wiltshire C, Lash TL, Bengtson AM
Epidemiology
· 2025 Nov · PMID 40658047
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BACKGROUND: Intimate partner violence (IPV) is an important global health issue for which measurement error limits public health action. Although most national IPV prevalence estimates come from general health surveys li...BACKGROUND: Intimate partner violence (IPV) is an important global health issue for which measurement error limits public health action. Although most national IPV prevalence estimates come from general health surveys like the Demographic and Health Surveys (DHS), such data probably underestimate prevalence compared with violence-focused surveys. METHODS: Using violence-focused surveys conducted in the same country and year (±1) as validation data, we explored two methods of bias adjustment to address measurement error in DHS prevalence estimates. In multidimensional bias analysis, we directly adjusted summary prevalence estimates, using a range of possible sensitivities (10%-100%) and specificities (95%-100%) to elucidate their reasonable bounds. In multiple overimputation, we reestimated all IPV observations, incorporating prior information on measurement error, and averaged prevalence estimates over 50 iterations. RESULTS: Multidimensional bias analysis revealed that an assumption of 95% specificity resulted in negative prevalence estimates in some cases, confirming that false positives are likely negligible. Reasonable sensitivities varied considerably across countries and IPV types, likely due to differences in the number of items used to assess IPV. Multiple overimputation-adjusted estimates were similar to survey estimates, except when unadjusted DHS estimates were <5% and highly discrepant. Past-year estimates were less discrepant than lifetime estimates, suggesting that recall bias may be a factor in underreporting. CONCLUSION: This study examines measurement error due to IPV underreporting in specific contexts where external information exists, highlighting the need for more accurate IPV assessment using multiple items per domain and for internal validation studies to be incorporated into large-scale surveys.
Psaras C, Arah OA, Chew KW
… +4 more, Lee SJ, Javanbakht M, Nianogo RA, Seamans MJ
Epidemiology
· 2025 Nov · PMID 40658042
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BACKGROUND: Hepatitis C virus (HCV) infection is a public health concern, with people living with opioid use disorder having a higher risk of infection. Despite the cooccurrence of HCV and opioid use disorder, little is...BACKGROUND: Hepatitis C virus (HCV) infection is a public health concern, with people living with opioid use disorder having a higher risk of infection. Despite the cooccurrence of HCV and opioid use disorder, little is known about the treatment patterns for the disorder in this population. This study characterized opioid agonist therapy adherence trajectories over 15 months following opioid agonist therapy initiation among people living with HCV and opioid use disorder and described the baseline characteristics of the patients within distinct opioid agonist therapy adherence trajectories. METHODS: We used Merative MarketScan healthcare claims data from 2015 to 2019 to identify distinct medication treatment adherence trajectories via growth mixture modeling among 5,495 people who initiated opioid agonist therapy for opioid use disorder and were living with HCV. RESULTS: Our models identified three distinct opioid agonist therapy adherence trajectories over the 15 months of follow-up. We named these trajectories rapidly declining opioid agonist therapy adherence (class 1; N = 1,904; 35%), steadily declining opioid agonist therapy adherence (class 2; N = 2,150; 39%), and consistently high opioid agonist therapy adherence (N = 1,441; 26%). People in the consistently high adherence group were older, more likely to be women (vs. men), White (vs. Black), had HCV direct-acting antiviral treatment during the baseline period, and had the lowest prevalence of nonopioid substance use diagnoses. CONCLUSIONS: These results may inform support for populations with elevated baseline risk of low opioid agonist therapy adherence during follow-up.
Strassle PD, Minc SD, Kalbaugh CA
… +3 more, Donneyong MM, Ko JS, McGinigle KL
Epidemiology
· 2025 Nov · PMID 40631724
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BACKGROUND: Major lower limb amputation is a devastating but preventable complication of peripheral artery disease. It is unclear whether racial and ethnic and rural differences in amputation rates are due to clinical, h...BACKGROUND: Major lower limb amputation is a devastating but preventable complication of peripheral artery disease. It is unclear whether racial and ethnic and rural differences in amputation rates are due to clinical, hospital, or structural factors. METHODS: We included all peripheral artery disease hospitalizations of patients ≥40 years old between 2017 and 2019 in Florida, Georgia, Maryland, Mississippi, or New York (HCUP State Inpatient Databases). We estimated the expected number of amputations using three models: (1) unadjusted, (2) adjusted for clinical factors, and (3) adjusted for clinical factors, hospital factors, and social determinants of health using least absolute shrinkage and selection operator (LASSO). We calculated and compared observed-to-expected ratios and quantified the role of these factors in amputation rates. RESULTS: Overall, 1,577,061 hospitalizations (990,152 unique patients) and 21,233 major lower limb amputations (1.4%) were included. After accounting for clinical differences, we observed amputation disparities among rural Black, Hispanic, Native American, and White patients and nonrural Black and Native American patients. After accounting for hospital factors and social determinants of health, disparities were no longer present among rural White adults (0.93, 95% confidence interval [CI]: 0.77, 1.09); however, disparities persisted among rural Black (1.26, 95% CI: 1.01, 1.51), Hispanic (1.50, 95% CI: 0.89, 2.12), and Native American patients (1.13, 95% CI: 0.68, 1.58) and nonrural Black (1.12, 95% CI: 1.09, 1.15) and Native American (1.15, 95% CI: 0.86, 1.44) patients. CONCLUSION: Clinical factors did not fully explain differences in amputation rates, and hospital factors and social determinants of health did not fully explain disparities. These findings provide additional evidence that implicit bias is associated with amputation disparities.
Epidemiology
· 2025 Sep · PMID 40525720
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Here, we posit that studies comparing outcomes of patients hospitalized with COVID-19 by vaccination status are important descriptive epidemiologic studies, but they contrast two groups that are not comparable with regar...Here, we posit that studies comparing outcomes of patients hospitalized with COVID-19 by vaccination status are important descriptive epidemiologic studies, but they contrast two groups that are not comparable with regard to causal analyses. We use the principal stratification framework to show that these studies can estimate a causal vaccine effect only for the subgroup of individuals who would be hospitalized with or without vaccination. Further, we describe the methodology for, and present sensitivity analyses of, this effect. Using this approach can change the interpretation of studies only reporting the standard analyses that condition on observed hospital admission status-that is, analyses comparing outcomes for all hospitalized COVID-19 patients by vaccination status.
Epidemiology
· 2025 Sep · PMID 40513075
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Social contact studies are used in infectious disease epidemiology to infer a contact matrix , having the mean number of contacts between individuals of different age groups as elements. However, does not capture the (of...Social contact studies are used in infectious disease epidemiology to infer a contact matrix , having the mean number of contacts between individuals of different age groups as elements. However, does not capture the (often large) variation in the number of contacts within each age group, information is also available in social contact studies. Here, we include such variation by separating each age group into two halves: the socially active (having many contacts) and the socially less active (having fewer contacts). The extended contact matrix and its associated epidemic model show that acknowledging variation in social activity within age groups has a substantial impact on the basic reproduction number, , and the final fraction getting infected if the epidemic takes off, . In fact, variation in social activity is more important for data fitting than allowing for different age groups. A difficulty with variation in social activity, however, is that social contact studies typically lack information on whether mixing with respect to social activity is assortative (when socially active mainly have contact with other socially active individuals) or not. Our analysis shows that accounting for variation in social activity improves model predictability, yielding more accurate expressions for and irrespective of whether such mixing is assortative, but different assumptions on assortativity give rather different outputs. Future social contact studies should, therefore, also try to infer the degree of assortativity (with respect to social activity) between peers and their contacts.
Unmeasured confounding is a major concern in obtaining credible inferences about causal effects from observational data. Proximal causal inference is an emerging methodological framework to detect and potentially account...Unmeasured confounding is a major concern in obtaining credible inferences about causal effects from observational data. Proximal causal inference is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a pair of negative control exposure and outcome variables, also known as treatment and outcome confounding proxies. Although regression-based proximal causal inference is well-developed for binary and continuous outcomes, analogous proximal causal inference regression methods for right-censored time-to-event outcomes are currently lacking. In this paper, we propose a novel two-stage regression proximal causal inference approach for right-censored survival data under an additive hazard structural model. We provide theoretical justification for the proposed approach tailored to different types of negative control outcomes, including continuous, count, and right-censored time-to-event variables. We illustrate the approach with an evaluation of the effectiveness of right heart catheterization among critically ill patients using data from the SUPPORT study. Our method is implemented in the open-access R package "pci2s."