United States community births increased by 20% from 2019 to 2020.

BACKGROUND: Anecdotal and emerging evidence suggested that the 2020 COVID-19 pandemic may have influenced women's attitudes toward community birth. Our purpose was to examine trends in community births from 2019 to 2020, and the risk profile of these births. METHODS: Recently released 2020 birth certificate data were compared with prior years' data to analyze trends in community births by socio-demographic and medical characteristics. RESULTS: In 2020, there were 71 870 community births in the United States, including 45 646 home births and 21 884 birth center births. Community births increased by 19.5% from 2019 to 2020. Planned home births increased by 23.3%, while birth center births increased by 13.2%. Increases occurred in every US state, and for all racial and ethnic groups, particularly non-Hispanic Black mothers (29.7%), although not all increases were statistically significant. In 2020, 1 of every 50 births in the United States was a community birth (2.0%). Women with planned home and birth center births were less likely than women with hospital births to have several characteristics associated with poor pregnancy outcomes, including teen births, smoking during pregnancy, obesity, and preterm, low birthweight, and multiple births. More than two-thirds of planned home births were self-paid, compared with one-third of birth center and just 3% of hospital births. CONCLUSIONS: It is to the great credit of United States midwives working in home and birth center settings that they were able to substantially expand their services during a worldwide pandemic without compromising standards in triaging women to optimal settings for safe birth.

Assessing knowledge, attitudes, and practices towards causal directed acyclic graphs: a qualitative research project.

Causal graphs provide a key tool for optimizing the validity of causal effect estimates. Although a large literature exists on the mathematical theory underlying the use of causal graphs, less literature exists to aid applied researchers in understanding how best to develop and use causal graphs in their research projects. We sought to understand why researchers do or do not regularly use DAGs by surveying practicing epidemiologists and medical researchers on their knowledge, level of interest, attitudes, and practices towards the use of causal graphs in applied epidemiology and health research. We used Twitter and the Society for Epidemiologic Research to disseminate the survey. Overall, a majority of participants reported being comfortable with using causal graphs and reported using them 'sometimes', 'often', or 'always' in their research. Having received training appeared to improve comprehension of the assumptions displayed in causal graphs. Many of the respondents who did not use causal graphs reported lack of knowledge as a barrier to using DAGs in their research. Causal graphs are of interest to epidemiologists and medical researchers, but there are several barriers to their uptake. Additional training and clearer guidance are needed. In addition, methodological developments regarding visualization of effect measure modification and interaction on causal graphs is needed.

Prenatal exposure to acetaminophen and adolescent assessment of behavior: Discrepancies by age and reporter.

Acetaminophen, which is one of the most commonly used medications during pregnancy, has been linked to adverse neurodevelopmental outcomes among offspring during childhood. Less is known about associations with outcomes occurring later in adolescence. Methods: We conducted a follow-up study of children born between 1996 and 2002. Data on illnesses and medications, including acetaminophen, during pregnancy were collected through a standardized interview after delivery. Behavioral assessments were conducted at two subsequent time points, childhood (ages 5-10) and adolescence (ages 11-17). Outcomes examined included internalizing, externalizing, and total behavior problems based on the parent-completed Child Behavior Checklist (CBCL), the teacher-completed Teacher Report Form (TRF), and the youth-completed Youth Self Report (YSR, adolescent follow-up only). Adjusted linear regression models were used to calculate mean differences (MD) and 95% confidence intervals (95% CI) in T-scores comparing those with prenatal acetaminophen exposure to those without. Stabilized inverse probability weights were used to account for attrition. Results: Among the 216 mother-child dyads with completed parent and teacher behavioral assessments at both childhood and adolescence, prenatal acetaminophen exposure was not associated with behavioral problems according to either parent or teacher assessments. Modest increases in externalizing and total behavior problems were observed according to youth report (MD: 1.9). Compared to associations observed during the childhood follow-up, associations at adolescence were attenuated according to parent-report. Conclusion: Reported associations between prenatal acetaminophen exposure and behavioral outcomes were not consistent over time nor between reporters.

A case study and proposal for publishing directed acyclic graphs: The effectiveness of the quadrivalent human papillomavirus vaccine in perinatally HIV Infected girls.

BACKGROUND: Developing a causal graph is an important step in etiologic research planning and can be used to highlight data flaws and irreparable bias and confounding. As a case study, we consider recent findings that suggest human papillomavirus (HPV) vaccine is less effective against HPV-associated disease among girls living with HIV compared to girls without HIV. OBJECTIVES: To understand the relationship between HIV status and HPV vaccine effectiveness, it is important to outline the key assumptions of the causal mechanisms before designing a study to investigate the effect of the HPV vaccine in girls living with HIV infection. METHODS: We present a causal graph to describe our assumptions and proposed approach to explore this relationship. We hope to obtain feedback on our assumptions before data analysis and exemplify the process for designing causal graphs to inform an etiologic study. CONCLUSION: The approach we lay out in this paper may be useful for other researchers who have an interest in using causal graphs to describe and assess assumptions in their own research before undergoing data collection and/or analysis.

Complex systems models for causal inference in social epidemiology.

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

A systematic review of quantitative bias analysis applied to epidemiological research.

BACKGROUND: Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006-19. METHODS: We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility. RESULTS: Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale. CONCLUSIONS: QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers.