Epidemiology最新文献

Background: Drivers of persistent racial-ethnic inequities in severe maternal morbidity are poorly understood. This study examined how neighborhood-level structural racism measures shape risk of severe maternal morbidity among Black mothers.

Methods: Data are from live hospital births in California between 1997 and 2019 at ≥20 weeks' gestation (N = 555,511). We leveraged information from the U.S. Census Bureau and the American Community Survey to determine neighborhood (census tract) measures of structural racism across six domains (homeownership, unemployment, poverty, educational attainment, racialized economic deprivation, and racial residential segregation). We used (1) an additive composite index (quartile 1 [low]-quartile 4 [high]) and (2) latent class analysis to characterize four structural racism typologies. We examined associations across both measurement approaches using mixed-effects logistic regression models with neighborhood random intercepts, adjusting for maternal age, education, and hospital payer/insurance information.

Results: Black mothers living in neighborhoods scoring high (quartile 4) on the additive composite index had 13% higher risk of severe maternal morbidity than those in neighborhoods scoring low (quartile 1) (95% confidence interval = 1.04, 1.24). Models evaluating latent class typologies also revealed that Black mothers living in neighborhoods characterized by consistently high racial inequity in unemployment, racialized economic deprivation, and racial residential segregation across the study period had a 12% higher risk of severe maternal morbidity compared with those in neighborhoods consistently scoring low in the domains examined (95% confidence interval = 1.03, 1.23).

Conclusions: Our findings support the hypothesis that neighborhood-level measures of structural racism influence the risk of severe maternal morbidity among Black mothers.

Background: Between 1970 and 1991, when viral inhibition reduced the risk, people with a bleeding disorder in the United Kingdom had their missing clotting factors replaced with plasma products derived from donated plasma at risk of infection. We analyzed longer-term survival of people with bleeding disorders exposed to plasma products.

Methods: The National Haemophilia Database documents people with bleeding disorders registered, treated before, and alive on 1 January 1992. We estimated all-cause mortality proportional hazard ratios for exposure groups (Human immunodeficiency virus (HIV)/Hepatitis C virus (HCV) coinfected, HCV-diagnosed, and HCV-status unknown) versus HCV antibody negative, within distinct epochs: 1992-1999; 2000-2009; 2010-2019. We estimated years of life lost by epoch and exposure group versus UK general population lifetables or via parametric survival models compared with people with bleeding disorders negative or unknown for HCV antibodies. Models were adjusted for sex, age band at 1 January 1992, bleeding disorder, and severity.

Results: Of 6282 people with bleeding disorders who met inclusion criteria, 15% were HIV/HCV coinfected, 32% HCV antibody positive, and 28% HCV antibody negative. Compared with HCV-negative, those HIV/HCV coinfected had an all-cause mortality hazard ratio of 4.2 (95% confidence interval: 2.9, 6.0) and HCV+ of 2.2 (1.7, 2.8) in 2010 to 2019. Years of life lost for 2014 to 2019 were 740 (95% confidence interval: 440, 1030) for HCV+ persons and 270 (130, 400) for HIV/HCV coinfected persons, compared with HCV unknown or negative persons.

Conclusions: People with bleeding disorders in the United Kingdom infected before, but alive at, 1 January 1992, were still at increased risk of death 3 decades postimplementation of HCV screening of blood supplies.

Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in both types of study this can be done mathematically or, equivalently, by specifying an ideal trial: a hypothetical perfect randomized experiment (with representative sample, perfect adherence, etc). The target trial framework is increasingly used for causal inference in observational studies, but clarity is lacking in how a target trial should be specified and how it relates to an ideal trial. Here, we review the mathematical and ideal trial approaches to defining a causal estimand, highlighting their equivalence and the need to balance practical relevance and feasibility of estimation regardless of approach. We then consider the question of how a target trial should be specified, outlining the challenges of a recommended approach, commonly seen in applications, that puts the focus heavily on the feasibility of estimation: to specify the target trial such that it is closely aligned with the observational data (e.g., uses the same eligibility criteria). We argue that with this "aligned" approach, biases may remain relative to the estimand of ultimate practical interest, defined by the ideal trial, that mirror the often-overlooked biases of actual trials. We conclude that consideration of the ideal trial and of how the target trial and its emulation or the actual trial differ from it is necessary to identify and manage all bias sources in both settings. An example from respiratory epidemiology is used for illustration.

Background: Projections for major health outcomes are crucial for decision-making to enable early interventions. Pooling results from meta-analyses with estimates based on individual participant data can reduce uncertainty and improve generalizability. However, current methods for meta-meta-analyses of nonlinear functions have remained underdeveloped.

Methods: We proposed a novel meta-analysis method to pool pointwise nonlinear function literature estimates with parameter estimates, applied here to the association of dietary change scenarios with mortality and ischemic heart disease (IHD) based on Finnish individual participant data. We linked individual-level demographic and risk factor data from the Health 2000, FINRISK 2007, FINRISK 2012, and FinHealth 2017 surveys (n = 20,784) with follow-up data on outcomes obtained from national health registers. We applied a Poisson multistate model and microsimulation to project state probabilities.

Results: Pooling reduced uncertainty in the hazard ratio estimates and in the health projections. We estimated that a two-thirds reduction in red and processed meat consumption would decrease the prevalence of IHD by 2 (95% prediction intervals [PI] 1, 4) percentage points (%pt) in the 2017 cohort and deaths by 2%pt (95% PI 1, 4) by 2050. We estimated that a 100% increase in whole grain consumption would reduce IHD by 2%pt (95% PI 0, 3) and deaths by 2%pt (95% PI 0, 3).

Conclusions: Our flexible meta-analysis method allowed the pooling of nonlinear estimates reported in the literature without detailed technical information. Our estimates support the hypothesis that more plant-based diets reduce mortality and IHD prevalence. The most beneficial scenarios included reductions in red and processed meat and increments in whole grain consumption.

Test-negative designs (TNDs) are widely used for postmarket evaluation of vaccine effectiveness (VE), particularly in cases when randomized trials are not feasible. Unlike classical TNDs, which only include healthcare seekers with symptoms, recent TNDs have involved individuals with various reasons for testing, especially in an outbreak setting. While including these data can increase sample size and hence improve precision, concerns have been raised about whether they introduce bias into the current framework of TNDs, thereby demanding a formal statistical examination of this modified design. In this article, using statistical derivations, causal graphs, and numerical demonstrations, we show that the standard odds ratio estimator may be biased if various reasons for testing are not taken into account. To eliminate this bias, we identify three categories of reasons for testing, namely symptoms, mandatory screening, and case contact tracing, and characterize associated statistical properties and estimands. Based on our characterization, we show how to consistently estimate each estimand via stratification. Furthermore, we describe when these estimands correspond to the same VE parameter and, when appropriate, propose a stratified estimator that can incorporate multiple reasons for testing and improve precision. We demonstrate the performance of our proposed method through simulation studies and a real-data analysis.

Background: Randomized trials of major adverse cardiovascular events found no effect of dipeptidyl-peptidase-4 inhibitors (DPP-4i) medications compared with second-generation sulfonylureas, while nonrandomized studies estimated a benefit of DPP-4i. Socioeconomic residual confounding was thought to be implicated. We compared area-level prescribing density and physician prescribing preference as candidate instrumental variables for the effect of DPP-4i medications on major adverse cardiovascular events.

Methods: Using Medicare claims data, we built two cohorts emulating randomized trials of sitagliptin or saxagliptin starters, each compared with sulfonylurea starters. The proportion of DPP-4i prescribing in a ZIP Code tabulation area defined the area-level prescribing density instrumental variable at various cutoffs (0% vs. 100% to <50% vs. ≥50%). Patients' physician prescribing history using the same proportion cutoffs was the physician prescribing preference candidate instrumental variable. An instantaneous physician preference instrumental variable used a physician's most recent prescription. We adjusted two-stage instrumental variable regression models for propensity score quintiles.

Results: Unadjusted analyses for sitagliptin and saxagliptin, each compared with sulfonylurea, estimated a reduced risk of major adverse cardiovascular events [sitagliptin hazard ratio (HR) = 0.86; 95% confidence interval = 0.83, 0.88]; saxagliptin (HR = 0.68; 0.64, 0.73). All instrumental variables were strong and reduced covariate imbalance. Analyses of area-level prescribing density found no meaningful difference for sitagliptin (0% vs. 100% HR = 1.1; 0.79, 1.6). Analyses of physician prescribing preference estimated reduced risk for sitagliptin (<50% vs. ≥50% HR = 0.69; 0.48, 0.98). Instantaneous physician prescribing preference analyses showed little to no difference for sitagliptin (HR = 0.86; 0.60, 1.1) and saxagliptin (HR = 0.98; 0.56, 1.7).

Conclusions: Candidate instrumental variables focusing on short-term prescribing preference hold promise over area-based variables but remain inefficient.

Background: Despite being the leading cause of death, the global tuberculosis (TB) burden is ill-defined. Existing methods to estimate incidence are time and/or resource-intensive and often inaccurate. Back-calculation was developed to estimate HIV incidence by considering reported cases to be a convolution of the disease duration and the incidence of new cases. New estimates of TB natural history parameters allow us to develop Bayesian back-calculation methods for TB to assign case notification data to the time point of onset of disease.

Methods: Recorded counts of TB cases are underestimates of the true burden of disease, so we include a multiplier derived from prevalence to notification ratios to account for underreporting. We assume a Poisson distribution for notifications and incidence and use a penalized-likelihood before smooth estimates. We estimate sex-stratified TB incidence for Vietnam, Cambodia, and the Philippines via Markov chain Monte Carlo.

Results: Annual estimated TB incidence was, on average 19% greater than recorded notifications. TB incidence among males was on average 3.8% higher than females in Vietnam, 1.3% in Cambodia, and 2.5% higher in the Philippines.

Conclusions: These estimates account for the delay between bacteriologically positive subclinical disease and notification and, as such, may be more temporally accurate than existing methods.

Epidemiologic studies are often concerned with estimating causal effects of a sequence of treatment decisions on survival outcomes. In many settings, treatment decisions do not occur at fixed, prespecified follow-up times. Rather, timing varies across subjects in ways that may be informative of subsequent treatment decisions and potential outcomes. Awareness of the issue and potential solutions is lacking in the literature, which motivates this work. Here, we formalize the issue of informative timing, problems associated with ignoring it, and show how g-methods can be used to analyze sequential treatments that are informatively timed. As we describe, in such settings, the waiting times between successive treatment decisions may be properly viewed as time-varying confounders. Using synthetic examples, we illustrate how g-methods that do not adjust for these waiting times may be biased and how adjustment can be done in scenarios where patients may die or be censored in between treatments. Finally, we provide implementation guidance and examples using publicly available software. Our concluding message is that (1) considering timing is important for valid inference and (2) correcting for informative timing can be done with g-methods that adjust for waiting times between treatments as time-varying confounders.

Background: Structural racism can manifest in contemporary neighborhoods via historical policies or programs. For example, the Home Owners' Loan Corporation, a government-backed program from the 1930s, systematically diverted wealth away from Black neighborhoods. The reproductive health consequences of this racist program remain understudied. We evaluated associations between residence in a historically redlined neighborhood and fecundability, the per-cycle probability of conception.

Methods: We used data from the Black Women's Health Study, a US cohort of Black women who were aged 21-69 years in 1995 and were followed up with biannual questionnaires. Experiences of infertility (i.e., tried for ≥12 months to conceive without success) were captured on several questionnaires. A 2011 supplemental module collected pregnancy histories between 1995 and 2011, including planning status and time to conception. We linked geocoded addresses to historical Home Owners' Loan Corporation grades (A ["best"] to D ["hazardous," i.e., redlined]). Using proportional probabilities regression models with generalized estimating equations, we estimated fecundability ratios and 95% confidence intervals (CIs).

Results: Our analysis included 818 planned pregnancy attempts from 674 participants (mean age = 33.9 years). Relative to participants residing in neighborhoods with the highest grades (A or B), adjusted models showed reduced fecundability among participants who resided in lower graded neighborhoods (C: 0.91, 95% CI: 0.77, 1.09; D: 0.82, 95% CI: 0.68, 0.99).

Conclusions: In this cohort of US Black women, contemporary residence in a historically redlined neighborhood was associated with reduced fecundability. Our findings highlight the importance of exploring how historical neighborhood disinvestment affects reproductive health.