Epidemiology

Theoretical guarantees for causal inference using propensity scores are partially based on the scores behaving like conditional probabilities. However, scores between zero and one do not necessarily behave like probabilities, especially when output by flexible statistical estimators. We perform a simulation study to assess the error in estimating the average treatment effect before and after applying a simple and well-established postprocessing method to calibrate the propensity scores. We observe that postcalibration reduces the error in effect estimation and that larger improvements in calibration result in larger improvements in effect estimation...

BACKGROUND: Evidence about which firearm policies work, to what extent, and for whom is hotly debated, perhaps partly because variation in research methodology has produced mixed and inconclusive effect estimates. We conducted a scoping review of firearm policy research in the health sciences in the United States, focusing on methodological considerations for causal inference. METHODS: We identified original, empirical articles indexed in PubMed from 1/1/2000-9/1/2021 that examined any of 18 pre-specified firearm policies...

BACKGROUND: Pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) are determinants of maternal and child health. However, many studies of these factors rely on error-prone self-reported measures. METHODS: Using data from Life-course Experiences And Pregnancy (LEAP), a US-based cohort, we assessed the validity of pre-pregnancy BMI and GWG recalled on average 8 years postpartum against medical record data treated as alloyed gold standard ("true") values...

BACKGROUND: Severe maternal morbidity is a composite measure of serious obstetric complications that is often identified in administrative data using International Classification of Diseases (ICD) diagnosis and procedure codes for a set of 21 indicators. Prior studies of screen-positive cases have demonstrated low predictive value for ICD codes relative to the medical record. To our knowledge, the validity of ICD-10 codes for identifying severe maternal morbidity has not been fully described...

BACKGROUND: African-born women have a lower risk of preterm birth and small for gestational age (SGA) birth compared to United States (US)-born Black women, however variation by country of origin is overlooked. Additionally, the extent that nativity disparities in adverse perinatal outcomes to Black women are explained by individual-level factors remains unclear. METHODS: We conducted a population-based study of non-anomalous singleton live births to US- and African-born Black women in California from 2011-2020 (n=194,320)...

BACKGROUND: Perceptions of the U.S. healthcare system can impact individuals' healthcare utilization, including vaccination intentions. This study examined the association between perceived racial-ethnic inequities in COVID-19 healthcare and willingness to receive the COVID-19 vaccine. METHODS: This study used data from the REACH-US study, a large, diverse study of U.S. adults (N=5,145 January-March 2021). Confirmatory factor and regression analyses examined a latent factor of perceived racial-ethnic inequities in COVID-19 healthcare, whether the factor was associated with willingness to receive the COVID-19 vaccine, and whether associations varied across racial-ethnic groups reported as probit estimates (B) and 95% confidence intervals (CIs)...

BACKGROUND: Artificial light at night, a well-recognized circadian clock disrupter, causes disturbances in endocrine homeostasis. However, the association of artificial light at night with polycystic ovary syndrome (PCOS) is still unknown. This study examines the effects of outdoor artificial light at night on sex hormones, glucose homeostasis markers, and PCOS prevalence in Anhui Province, China. METHODS: We recruited 20633 women of reproductive age from Anhui Medical University Reproductive Medicine Center...

BACKGROUND: Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk factor studies, but is not yet aligned with epidemiology's current focus on causal inference and intervention...

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One of the common errors in the calculation of the population attributable fraction (PAF) is the use of an adjusted risk ratio in the Levin formula. In this article, we discuss the errors visually using wireframes by varying the standardized mortality ratio (SMR) and associational risk ratio (aRR) when the prevalence of exposure is fixed. When SMR > 1 and SMR > aRR, the absolute bias is positive, and its magnitude increases as the difference between SMR and aRR increases. By contrast, when aRR > SMR > 1, the absolute bias is negative and its magnitude is relatively small...

BACKGROUND: Initiation of proprotein convertase subtilisin/kexin type 9 monoclonal antibody (PCSK9 mAb) for lipid-lowering following myocardial infarction (MI) is likely affected by patients' prognostic factors, potentially leading to bias when comparing real-world treatment effects. METHODS: Using target-trial emulation, we assessed potential confounding when comparing two treatment strategies post-MI: initiation of PCSK9 mAb within 1 year and no initiation of PCSK9 mAb...

Sometimes treatment effects are absent in a subgroup of the population. For example, penicillin has no effect on severe symptoms in individuals infected by resistant staphylococcus aureus, and codeine has no effect on pain in individuals with certain polymorphisms in the CYP2D6 enzyme. Subgroups where a treatment is ineffective are often called negative control populations or placebo groups. They are leveraged to detect bias in different disciplines. Here we present formal criteria that justify the use of negative control populations to rule out unmeasured confounding and mechanistic (direct) causal effects...

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge there are no pragmatic methods to address the bias introduced by performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation (MLE)-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm...

BACKGROUND: Several observational studies have described an inverse association between cancer diagnosis and subsequent dementia risk. Multiple biologic mechanisms and potential biases have been proposed in attempts to explain this association. One proposed explanation is the opposite expression of Pin1 in cancer and dementia, and we use this explanation and potential drug target to illustrate the required assumptions and potential sources of bias for inferring an effect of Pin1 on dementia risk from analyses measuring cancer diagnosis as a proxy for Pin1 expression...

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a non-parametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment...

Although many epidemiologic studies focus on point identification, it is also possible to partially identify causal effects under consistency and the data alone. However, the literature on the so-called "assumption-free" bounds has focused on settings with time-fixed exposures. We describe assumption-free bounds for the effects of both static and dynamic sustained interventions. To provide intuition for the width of the bounds, we also discuss a mathematical connection between assumption-free bounds and clone-censor-weight approaches to causal effect estimation...

BACKGROUND: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, SiCLOPS (Simulation of Community-Level Overdose Prevention Strategy), we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties...

BACKGROUND: We describe the use of Apisensr, a web-based application that can be used to implement quantitative bias analysis for misclassification, selection bias, and unmeasured confounding. We apply Apisensr using an example of exposure misclassification bias due to use of self-reported body mass index (BMI) to define obesity status in an analysis of the relationship between obesity and diabetes. METHODS: We used publicly available data from the National Health and Nutrition Examination Survey (NHANES)...

BACKGROUND: Mainland China experienced a major surge in SARS-CoV-2 infections in December 2022-January 2023, but its impact on mortality was unclear given the under-reporting of COVID-19 deaths. METHODS: Using obituary data from the Chinese Academy of Engineering (CAE), we estimated the excess death rate among senior CAE members by taking the difference between the observed rate of all-cause death in December 2022-January 2023 and the expected rate for the same months in 2017 to 2022 by age groups...