Characterizing Fit-for-Purpose Real-World Data: An Assessment of a Mother-Infant Linkage in the Japan Medical Data Center Claims Database.

Purpose: Observational postapproval safety studies are needed to inform medication safety during pregnancy. Real-world databases can be valuable for supporting such research, but fitness for regulatory purpose must first be vetted. Here, we demonstrate a fit-for-purpose assessment of the Japan Medical Data Center (JMDC) claims database for pregnancy safety regulatory decision-making. Patients and Methods: The Duke-Margolis framework considers a database's fitness for regulatory purpose based on relevancy (capacity to answer the research question based on variable availability and a sufficiently sized, representative population) and quality (ability to validly answer the research question based on data completeness and accuracy). To assess these considerations, we examined descriptive characteristics of infants and pregnancies among females ages 12-55 years in the JMDC between January 2005 and March 2022. Results: For relevancy, we determined that critical data fields (maternal medications, infant major congenital malformations, covariates) are available. Family identification codes permitted linkage of 385,295 total mother-infant pairs, 57% of which were continuously enrolled during pregnancy. The prevalence of specific congenital malformation subcategories and maternal medical conditions were representative of the general population, but preterm births were below expectations (3.6% versus 5.6%) in this population. For quality, our methods are expected to accurately identify the complete set of mothers and infants with a shared health insurance plan. However, validity of gestational age information was limited given the high proportion (60%) of missing live birth delivery codes coupled with suppression of infant birth dates and inaccessibility of disease codes with gestational week information. Conclusion: The JMDC may be well suited for descriptive studies of pregnant people in Japan (eg, comorbidities, medication usage). More work is needed to identify a method to assign pregnancy onset and delivery dates so that in utero medication exposure windows can be defined more precisely as needed for many regulatory postapproval pregnancy safety studies.

Experiences of Racial Discrimination and Periconceptional Diet Quality.

BACKGROUND: Racism is a key determinant of perinatal health disparities. Poor diet may contribute to this effect, but research on racism and dietary patterns is limited. OBJECTIVE: We aimed to describe the relation between experiences of racial discrimination and adherence to the 2015‒2020 Dietary Guidelines for Americans. METHODS: We used data from a prospective pregnancy cohort study conducted at 8 United States medical centers (2010‒2013). At 6‒13 weeks of gestation, 10,038 nulliparous people with singleton pregnancies were enrolled. Participants completed a Block food frequency questionnaire, assessing usual diet in the 3 mo around conception, and the Krieger Experiences of Discrimination Scale, assessing the number of situational domains (e.g., at school and on the street) in which participants ever experienced racial discrimination. Alignment of dietary intake with the 2015-2020 Dietary Guidelines for Americans was assessed using the Healthy Eating Index (HEI)-2015. RESULTS: The study showed that 49%, 44%, 35%, and 17% of the Asian, Black, Hispanic, and White participants reported experiences of racial discrimination in any domain. Most participants experienced discrimination in 1 or 2 situational domains. There were no meaningful differences in HEI-2015 total or component scores in any racial or ethnic group according to count of self-reported domains in which individuals experienced discrimination. For example, mean total scores were 57‒59 among Black, 61‒66 among White, 61‒63 among Hispanic, and 66‒69 among Asian participants across the count of racial discrimination domains. CONCLUSIONS: This null association stresses the importance of going beyond interpersonal racial discrimination to consider the institutions, systems, and practices affecting racialized people to eliminate persistent inequalities in diet and perinatal health.

Longitudinal profiles of neighborhood socioeconomic vulnerability influence blood pressure changes across the female midlife period.

PURPOSE: To examine whether longitudinal exposure to neighborhood socioeconomic vulnerability influences blood pressure changes throughout midlife in a racially, ethnically, and geographically-diverse cohort of women transitioning through menopause. METHODS: We used longitudinal data on 2738 women (age 42-52 at baseline) living in six United States cities from The Study of Women's Health Across the Nation. Residential histories, systolic blood pressures (SBP), and diastolic blood pressures (DBP) were collected annually for ten years. We used longitudinal latent profile analysis to identify patterns of neighborhood socioeconomic vulnerability occurring from 1996 to 2007 in participant neighborhoods. We used linear mixed-effect models to determine if a woman's neighborhood profile throughout midlife was associated with blood pressure changes. RESULTS: We identified four unique profiles of neighborhood socioeconomic vulnerability - differentiated by residential socioeconomic status, population density, and vacant housing conditions - which remained stable across time. Women residing in the most socioeconomically vulnerable neighborhoods experienced the steepest increase in annual SBP growth by 0.93 mmHg/year (95% CI: 0.65-1.21) across ten-year follow-up. CONCLUSIONS: Neighborhood socioeconomic vulnerability was significantly associated with accelerated SBP increases throughout midlife among women.

Adverse Cardiovascular Events Following Severe Maternal Morbidity.

Severe maternal morbidity (SMM) affects 50,000 women annually in the United States, but its consequences are not well understood. We aimed to estimate the association between SMM and risk of adverse cardiovascular events during the 2 years postpartum. We analyzed 137,140 deliveries covered by the Pennsylvania Medicaid program (2016-2018), weighted with inverse probability of censoring weights to account for nonrandom loss to follow-up. SMM was defined as any diagnosis on the Centers for Disease Control and Prevention list of SMM diagnoses and procedures and/or intensive care unit admission occurring at any point from conception through 42 days postdelivery. Outcomes included heart failure, ischemic heart disease, and stroke/transient ischemic attack up to 2 years postpartum. We used marginal standardization to estimate average treatment effects. We found that SMM was associated with increased risk of each adverse cardiovascular event across the follow-up period. Per 1,000 deliveries, relative to no SMM, SMM was associated with 12.1 (95% confidence interval (CI): 6.2, 18.0) excess cases of heart failure, 6.4 (95% CI: 1.7, 11.2) excess cases of ischemic heart disease, and 8.2 (95% CI: 3.2, 13.1) excess cases of stroke/transient ischemic attack at 26 months of follow-up. These results suggest that SMM identifies a group of women who are at high risk of adverse cardiovascular events after delivery. Women who survive SMM may benefit from more comprehensive postpartum care linked to well-woman care.

Simulation as a Tool for Teaching and Learning Epidemiologic Methods.

In aspiring to be discerning epidemiologists, we must learn to think critically about the fundamental concepts in our field and be able to understand and apply many of the novel methods being developed today. We must also find effective ways to teach both basic and advanced topics in epidemiology to graduate students, in a manner that goes beyond simple provision of knowledge. Here, we argue that simulation is one critical tool that can be used to help meet these goals, by providing examples of how simulation can be used to address 2 common misconceptions in epidemiology. First, we show how simulation can be used to explore nondifferential exposure misclassification. Second, we show how an instructor could use simulation to provide greater clarity on the correct definition of the P value. Through these 2 examples, we highlight how simulation can be used to both clearly and concretely demonstrate theoretical concepts, as well as to test and experiment with ideas, theories, and methods in a controlled environment. Simulation is therefore useful not only in the classroom but also as a skill for independent self-learning.

Life-course impact of child maltreatment on midlife health-related quality of life in women: longitudinal mediation analysis for potential pathways.

PURPOSE: We examined (1) if child maltreatment (CM) is associated with lower health-related quality of life (HRQoL) and fewer quality-adjusted life years (QALY) over a 9-year follow-up of midlife women and (2) if adulthood psychosocial mediators could explain these associations. METHODS: Women (n = 342) completed the Childhood Trauma Questionnaire. Longitudinal HRQoL and QALY outcomes measured at five study visits include 36-item Short-Form Health Survey mental component score and physical component score and the Short Form-6 Dimension health index. Aims 1 and 2 were investigated by generalized estimating equations and sequential structural nested mean models, respectively. RESULTS: Twenty percent reported 2+ CM types. Compared with women without CM, women who experienced 2+ CM types reported 5- and 4-points lower scores in mental component score and physical component score, respectively, and 28 fewer healthy days per year in QALY. Low optimism, sleep problems, and low social support each explained greater than 10% of the relationship between 2+ CM and HRQoL and QALY over time. CONCLUSIONS: CM is a life-course social determinant of HRQoL and QALY throughout midlife, particularly in women who experienced 2+ CM types. Several mediators are modifiable and could be targets of interventions to mitigate the negative impact of CM on midlife HRQoL and QALY in women.

Teaching yourself about structural racism will improve your machine learning.

In this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for generalizability in almost all domains of health research.

Effect of preconception low dose aspirin on pregnancy and live birth according to socioeconomic status: A secondary analysis of a randomized clinical trial.

Low socioeconomic status (SES) is associated with adverse pregnancy outcomes and infertility. Low-dose aspirin (LDA) was shown to improve livebirth rates in certain subsets of women, and therefore, may impact pregnancy rates differentially by SES status. Therefore, the aim of the current study was to examine whether daily preconception-initiated LDA affects rates of pregnancy, livebirth, and pregnancy loss differently across strata of socioeconomic status (SES). This is a secondary analysis of The Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial, a multisite, block- randomized, placebo-controlled trial conducted at four U.S. medical centers (n = 1,228, 2007-2012). Women attempting spontaneous conception with a history of pregnancy loss were randomly allocated preconception to 81mg of aspirin + 400mcg of folic acid (n = 615) or placebo + 400mcg of folic acid (n = 613). Study medication was administered for six menstrual cycles or until 36 weeks' gestation if pregnancy was achieved. For this analysis, women were stratified by SES, which included income (low, mid, high) and a combined grouping of education and income (low-low, low-high, high-low, high-high). Log binomial models with robust variance estimated risks of pregnancy, livebirth, and pregnancy loss for LDA versus placebo. LDA increased pregnancy and livebirth rates (RR 1.23, 95% CI: 1.03, 1.45) in the high-income, but not mid- or low-income groups. LDA increased pregnancy rates in both the low education-low income group (RR 1.22, 95% CI: 1.02, 1.46) and the high education-high income group (RR 1.23, 95%CI: 1.06, 1.42), with no effect observed in mid-SES groupings. LDA, a low-cost and widely available treatment, may be particularly beneficial to women at the highest and lowest ends of the socioeconomic spectrum, though underlying mechanisms of this disparity are unclear. Confirming these findings and identifying factors which may modulate the effectiveness of LDA will ultimately facilitate personalized clinical care and improvements in population-level reproductive health. Trial registration number: ClinicalTrials.gov, NCT00467363.

Differential impact of socioeconomic position across life on oral cancer risk in Kerala, India: An investigation of life-course models under a time-varying framework.

OBJECTIVES: The incidence of oral cancer has been rapidly increasing in India, calling for evidence contributing to a deeper understanding of its determinants. Although disadvantageous life-course socioeconomic position (SEP) is independently associated with the risk of these cancers, the explanatory mechanisms remain unclear. Possible pathways may be better understood by testing which life-course model most influences oral cancer risk. We estimated the association between life-course SEP and oral cancer risk under three life-course models: critical period, accumulation and social mobility. METHODS: We recruited incident oral cancer cases (N = 350) and controls (N = 371) frequency-matched by age and sex from two main referral hospitals in Kozhikode, Kerala, India, between 2008 and 2012. We collected information on childhood (0-16 years), early adulthood (17-30 years) and late adulthood (above 30 years) SEP and behavioural factors along the life span using interviews and a life-grid technique. Odds ratios (OR) and 95% confidence intervals (CI) were estimated for the association between life-course SEP and oral cancer risk using inverse probability weighted marginal structural models. RESULTS: Relative to an advantageous SEP in childhood and early adulthood, a disadvantageous SEP was associated with oral cancer risk [(OR = 2.76, 95% CI: 1.99, 3.81) and (OR = 1.84, 95% CI: 1.21, 2.79), respectively]. In addition, participants who were in a disadvantageous (vs advantageous) SEP during all three periods of life had an increased oral cancer risk (OR = 4.86, 95% CI: 2.61, 9.06). The childhood to early adulthood social mobility model and overall life-course trajectories indicated strong influence of exposure to disadvantageous SEP in childhood on the risk for oral cancer. CONCLUSIONS: Using novel approaches to existing methods, our study provides empirical evidence that disadvantageous childhood SEP is critical for oral cancer risk in this population from Kerala, India.

Optimal Timing of Physician Visits after Hospital Discharge to Reduce Readmission.

OBJECTIVE: To identify the optimal timing of in-person physician visit after hospital discharge to yield the largest reduction in readmission among elderly or chronically ill patients. DATA SOURCES/STUDY SETTING/EXTRACTION METHODS: We extracted insurance billing data on 620,656 admissions for any cause from 2002 to 2009 in Quebec, Canada. STUDY DESIGN: We used flexible survival models to estimate inverse probability weights for the precise timing (days) of in-person physician visit after discharge and weighted competing risk outcome models. PRINCIPAL FINDINGS: Readmission reduction associated with in-person physician visits (compared to none) was seen early after discharge, with 67.8 fewer readmissions per 1,000 discharges if physician visit occurred within 7 days (95 percent CI: 66.7-69.0), and 110.0 fewer readmissions within 21 days (95 percent CI: 108.2-111.7). The period of largest contribution to readmission reduction was seen in the first 10 days, while physician visits occurring later than 21 days after discharge did not further contribute to reducing hospital readmissions. Larger risk reductions were observed among patients in the highest morbidity level and for in-person follow-up with a primary care physician rather than a medical specialist. CONCLUSIONS: When provided promptly, postdischarge in-person physician visit can prevent many readmissions. The benefits appear optimal when such visit occurs within the first 10 days, or at least within the first 21 days of discharge.

Effect of Population-Specific Birthweight Curves on Disparities in Perinatal Mortality in Small-for-Gestational Age Pregnancies.

OBJECTIVE: To determine the effect of adopting sex or race/ethnicity-specific birthweight curves on small-for-gestational age (SGA)-associated mortality rates for specific populations. MATERIALS AND METHODS: Analyzing 20,095,735 singleton pregnancies, we compared rates of perinatal death associated with SGA in distinct sex and racial/ethnic groups when SGA was defined using nonspecific, sex-specific, and race/ethnicity-specific birthweight curves. RESULTS: With use of a nonspecific birthweight curve, the rate of perinatal death was higher for SGA males (20.4/1,000 [95% confidence interval (CI), 20.1, 20.7]) than SGA females [14.6/1,000 (95% CI, 14.4, 14.8)]. With a sex-specific curve, this disparity was reduced, measuring 17.7/1,000 (95% CI, 17.4, 17.9) for SGA males and 16.3/1,000 (95% CI, 16.1, 16.6) for females. Using a nonspecific birthweight curve, perinatal death rates were higher for non-Hispanic blacks (20.4/1,000 [95% CI, 20.0, 20.8]) than for all other racial/ethnic groups (15.9/1,000 [95% CI, 15.7, 16.1]). This difference increased with use of a race-specific birthweight curve: perinatal mortality was 29.7/1,000 (95% CI, 29.0, 30.3) for SGA blacks and 14.7/1,000 (95% CI, 14.6, 14.9) for all other racial/ethnic groups. CONCLUSION: Population-based differences in SGA-associated mortality are reduced with adoption of a sex-specific birthweight curve, but widen with use of a race/ethnicity-specific curve. These findings highlight the importance of outcomes analysis in the selection of diagnostic criteria for SGA.

Team-based versus traditional primary care models and short-term outcomes after hospital discharge.

BACKGROUND: Strategies to reduce hospital readmission have been studied mainly at the local level. We assessed associations between population-wide policies supporting team-based primary care delivery models and short-term outcomes after hospital discharge. METHODS: We extracted claims data on hospital admissions for any cause from 2002 to 2009 in the province of Quebec. We included older or chronically ill patients enrolled in team-based or traditional primary care practices. Outcomes were rates of readmission, emergency department visits and mortality in the 90 days following hospital discharge. We used inverse probability weighting to balance exposure groups on covariates and used marginal structural survival models to estimate rate differences and hazard ratios. RESULTS: We included 620 656 index admissions involving 312 377 patients. Readmission rates at any point in the 90-day post-discharge period were similar between primary care models. Patients enrolled in team-based primary care practices had lower 30-day rates of emergency department visits not associated with readmission (adjusted difference 7.5 per 1000 discharges, 95% confidence interval [CI] 4.2 to 10.8) and lower 30-day mortality (adjusted difference 3.8 deaths per 1000 discharges, 95% CI 1.7 to 5.9). The 30-day difference for mortality differed according to morbidity level (moderate morbidity: 1.0 fewer deaths per 1000 discharges in team-based practices, 95% CI 0.3 more to 2.3 fewer deaths; very high morbidity: 4.2 fewer deaths per 1000 discharges, 95% CI 3.0 to 5.3; p < 0.001). INTERPRETATION: Our study showed that enrolment in the newer team-based primary care practices was associated with lower rates of postdischarge emergency department visits and death. We did not observe differences in readmission rates, which suggests that more targeted or intensive efforts may be needed to affect this outcome.

Prepregnancy obesity and the racial disparity in infant mortality.

OBJECTIVE: To estimate the extent to which prepregnancy obesity explains the Black-White disparity in stillbirth and infant mortality. METHODS: A population-based study of linked Pennsylvania birth-infant death certificates (2003-2011; n = 1,055,359 births) and fetal death certificates (2006-2011; n = 3,102 stillbirths) for all singleton pregnancies in non-Hispanic (NH) White and NH Black women was conducted. Inverse probability weighted regression was used to estimate the role of prepregnancy obesity in explaining the race-infant/fetal death association. RESULTS: Compared with NH White women, NH Black women were more likely to have obesity (≥30 kg/m2 ) and experienced a higher rate of stillbirth (8.3 vs. 3.6 stillbirths per 1,000 live-born and stillborn infants) and infant death (8.5 vs. 3.0 infant deaths per 1,000 live births). When the contribution of prepregnancy obesity was removed, the difference in risk between NH Blacks and NH Whites decreased from 6.2 (95% CI: 5.6-6.7) to 5.5 (95% CI: 4.9-6.2) excess stillbirths per 1,000 and 5.8 (95% CI: 5.3-6.3) to 5.2 (95% CI: 4.7-5.7) excess infant deaths per 1,000. CONCLUSIONS: For every 10,000 live births in Pennsylvania (2003-2011), 6 of the 61 excess infant deaths in NH Black women and 5 of the 44 excess stillbirths (2006-2011) were attributable to prepregnancy obesity.

Mediation Analysis for Health Disparities Research.

Social epidemiologists often seek to determine the mechanisms that underlie health disparities. This work is typically based on mediation procedures that may not be justified with exposures of common interest in social epidemiology. In this analysis, we explored the consequences of using standard approaches, referred to as the difference and generalized product methods, when mediator-outcome confounders are associated with the exposure. We compared these with inverse probability-weighted marginal structural models, the structural transformation method, doubly robust g-estimation of a structural nested model, and doubly robust targeted minimum loss-based estimation. We used data on 900,726 births from 2003 to 2007 in the Penn Moms study, conducted in Pennsylvania, to assess the extent to which breastfeeding prior to hospital discharge explained the racial disparity in infant mortality. Overall, for every 1,000 births, 3.36 more infant deaths occurred among non-Hispanic black women relative to all other women (95% confidence interval: 2.78, 3.93). Using the difference and generalized product methods to assess the disparity that would remain if everyone breastfed prior to discharge suggested a complete elimination of the disparity (risk difference = -0.87 per 1,000 births; 95% confidence interval: -1.39, -0.35). In contrast, doubly robust methods suggested a reduction in the disparity to 2.45 (95% confidence interval: 2.20, 2.71) more infant deaths per 1,000 births among non-Hispanic black women. Standard approaches for mediation analysis in health disparities research can yield misleading results.

The parametric g-formula for time-to-event data: intuition and a worked example.

BACKGROUND: The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied. METHODS: We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding. RESULTS: Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula. CONCLUSIONS: The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.

Constructing inverse probability weights for continuous exposures: a comparison of methods.

Inverse probability-weighted marginal structural models with binary exposures are common in epidemiology. Constructing inverse probability weights for a continuous exposure can be complicated by the presence of outliers, and the need to identify a parametric form for the exposure and account for nonconstant exposure variance. We explored the performance of various methods to construct inverse probability weights for continuous exposures using Monte Carlo simulation. We generated two continuous exposures and binary outcomes using data sampled from a large empirical cohort. The first exposure followed a normal distribution with homoscedastic variance. The second exposure followed a contaminated Poisson distribution, with heteroscedastic variance equal to the conditional mean. We assessed six methods to construct inverse probability weights using: a normal distribution, a normal distribution with heteroscedastic variance, a truncated normal distribution with heteroscedastic variance, a gamma distribution, a t distribution (1, 3, and 5 degrees of freedom), and a quantile binning approach (based on 10, 15, and 20 exposure categories). We estimated the marginal odds ratio for a single-unit increase in each simulated exposure in a regression model weighted by the inverse probability weights constructed using each approach, and then computed the bias and mean squared error for each method. For the homoscedastic exposure, the standard normal, gamma, and quantile binning approaches performed best. For the heteroscedastic exposure, the quantile binning, gamma, and heteroscedastic normal approaches performed best. Our results suggest that the quantile binning approach is a simple and versatile way to construct inverse probability weights for continuous exposures.

A comparison of methods to estimate the hazard ratio under conditions of time-varying confounding and nonpositivity.

In occupational epidemiologic studies, the healthy worker survivor effect refers to a process that leads to bias in the estimates of an association between cumulative exposure and a health outcome. In these settings, work status acts both as an intermediate and confounding variable and may violate the positivity assumption (the presence of exposed and unexposed observations in all strata of the confounder). Using Monte Carlo simulation, we assessed the degree to which crude, work-status adjusted, and weighted (marginal structural) Cox proportional hazards models are biased in the presence of time-varying confounding and nonpositivity. We simulated the data representing time-varying occupational exposure, work status, and mortality. Bias, coverage, and root mean squared error (MSE) were calculated relative to the true marginal exposure effect in a range of scenarios. For a base-case scenario, using crude, adjusted, and weighted Cox models, respectively, the hazard ratio was biased downward 19%, 9%, and 6%; 95% confidence interval coverage was 48%, 85%, and 91%; and root MSE was 0.20, 0.13, and 0.11. Although marginal structural models were less biased in most scenarios studied, neither standard nor marginal structural Cox proportional hazards models fully resolve the bias encountered under conditions of time-varying confounding and nonpositivity.