Paediatric and perinatal epidemiology最新文献

Background: Much research has focused on identifying predictors of late antenatal care initiation. Many of these predictors (e.g., young age, migration background, socioeconomic position) are impossible to modify, illustrating the need to explore other interventions.

Objectives: This study aims to investigate inequalities in antenatal care initiation and assess whether early pregnancy recognition may reduce these inequalities.

Methods: Data from Generation R were used (N = 4196), a population-based birth cohort study in Rotterdam, The Netherlands. The association of gestational age at pregnancy recognition with the timing of antenatal care initiation and associations of individual and socioeconomic factors with the timing of antenatal care initiation were assessed using linear regression analyses. G-methods were used to estimate the reduction of the inequalities in antenatal care initiation if everyone would have recognised the pregnancy within 6 weeks.

Results: Participants who recognised their pregnancy within 6 weeks (81.7%) had their first antenatal care visit 1.3 weeks (95% confidence interval [CI] -1.6, -0.9) earlier than those who recognised their pregnancy after 6 weeks. All individual and socioeconomic factors were associated with the timing of antenatal care initiation. Modelling a scenario where pregnancy recognition occurred within 6 weeks reduced inequalities in antenatal care initiation timing across several groups: age < 20 versus 30-35 (-0.4 weeks, 95% CI -0.7, -0.2), first-generation migrants versus no migration (-0.1 weeks, 95% CI -0.2, 0.0), unplanned/ambivalent versus planned pregnancies (-0.4 weeks, 95% CI -0.6, -0.2), lower versus higher educational attainment (-0.1 weeks, 95% CI -0.2, 0.0), unemployed versus employed (-0.1 weeks, 95% CI -0.2, 0.0), low versus high household income (-0.1 weeks, 95% CI -0.3, 0.0), renters versus homeowners (-0.1 weeks, 95% CI -0.2, 0.0), and high versus low neighbourhood deprivation (-0.1 weeks, 95% CI -0.2, -0.1).

Conclusions: Early recognition of pregnancy may help reduce the impact of socio-economic inequalities in the timely initiation of antenatal care.

Background: Extreme ambient heat has been linked to maternal and neonatal mortality and morbidity. However, less is known regarding the impact of extreme ambient heat on mental health outcomes, particularly among socially vulnerable pregnant people.

Objectives: We aimed to estimate the effect of ambient heat exposure on psychiatric emergency services encounters among pregnant people served by a public program in the metropolitan Boston area. We hypothesised that days of extreme heat would have higher odds of an encounter with psychiatric emergency services when compared to normal temperature days.

Methods: Using electronic health records from the Boston Emergency Services Team program, we identified 861 geocodable encounters for pregnant people who utilised psychiatric emergency services between 2005-2009 and 2017-2021. Using a time-stratified case-crossover design, we matched each case (psychiatric emergency services encounter) with 3-5 control dates (matched by day-of-week and month). We obtained daily temperature (degrees Celsius) measures (mean, minimum, maximum) at an 800 m gridded resolution for cases/controls. Extreme heat exposure was defined at the 95th percentile, and exposure-response relationships were analysed using distributed lag nonlinear models with a 5-day lag period. We used conditional logistic regression models to estimate the association between ambient temperature exposure and odds of psychiatric emergency services encounters, controlling for U.S. federal holidays, relative humidity, and week of case/control date. Effect measure modification by time was examined (e.g., 2005-2009, 2017-2021).

Results: Among socially vulnerable pregnant people, we found no evidence of an association between days of extreme heat and the odds of psychiatric emergency services encounters, compared to normal temperature days.

Conclusions: This study does not support our a priori hypothesis. Future research is needed to confirm to what extent this association is truly null, as opposed to being obscured by selection bias into psychiatric emergency services, among socially vulnerable pregnant people.

Background: Studies of perinatal and paediatric health often analyse data from consecutive pregnancies. However, little is known about the factors associated with maternal residential changes between births or how maternal mobility may affect the validity of epidemiological findings, particularly those involving geographic-based measurements.

Objectives: Using a statewide sibling cohort, we studied the pattern of residential mobility between births and its association with several major sociodemographic and perinatal factors. We discussed how maternal moving between births may affect the validity of epidemiologic research, focusing on sibling comparison analyses.

Methods: We geocoded maternal residential addresses extracted from the livebirth records of sibling pairs from 2007 to 2015 in California. We described moving patterns between births, and we estimated the risk ratio (RR) of maternal moving according to maternal sociodemographic, pregnancy, and neonatal factors of the preceding birth. We studied maternal social mobility by examining the changes in the neighbourhood Social Vulnerability Indexes (SVI) among the movers.

Results: Our sample included 995,342 sibling pairs. Overall, 49.8% of mothers moved between births; 34.9% to another ZIP code, and 9.3% to another county. Younger maternal age, smoking during pregnancy, self-reported non-Hispanic Black race, lower educational attainment, nulliparous status, no prenatal care or having used government insurance for prenatal care, and non-adverse birth outcomes at the precedent birth were associated with maternal moving between births. Maternal social mobility between births in the cohort also varied by age, race/ethnicity, and education levels.

Conclusions: Maternal residential mobility between births was more common within certain sociodemographic subgroups in California. We recommend measuring residential changes in sibling studies, and assessing the potential impacts on statistical power, time-varying confounding, carry-over effect, and generalisability of findings. Further research is warranted to better understand how maternal mobility influences various types of perinatal epidemiological studies.

Background: Survey data are essential in paediatric epidemiology, providing valuable insights into child health outcomes. The potential outcomes framework has advanced causal inference using observational data. However, traditional design-based adjustments, especially sample weights, are often overlooked. This omission limits the ability to generalise findings to the broader population.

Objective: This study demonstrates three approaches for estimating the population average treatment effect (PATE) in a practical example, examining the impact of household second-hand smoke (SHS) exposure on blood pressure in school-aged children.

Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020, we assessed the effect of household SHS exposure, a non-randomised treatment, on blood pressure in school-aged children. We applied estimators based on Inverse Probability of Treatment Weighting (IPTW), G-computation, Targeted Maximum Likelihood Estimation (TMLE), and regression adjustment. Models without adjustments were run for comparison. We examined point estimates and the efficiency of the estimates obtained from these methods.

Results: The largest differences were observed between the unadjusted regression models and the fully adjusted methods (IPTW, G-computation, and TMLE), which account for both confounding and survey weights. While the inclusion of the sample weights leads to wider confidence intervals for all methods, G-computation and TMLE showed comparatively narrower confidence intervals. Confidence intervals for the models not adjusted for sample weights were likely underestimated.

Conclusions: This study highlights the important role of sample weights in causal inference. Generalisability of the average treatment effect as estimated on data sampled using common survey designs to a defined population requires the use of sample weights. The estimators described provide a framework for incorporating sample weights, and their use in health research is recommended.

Background: Pregnancy loss is recognised as a competing event in studies of prenatal medication use. A healthy live birth also precludes subsequent pregnancy outcomes, yet is often censored in time-to-event analyses.

Objectives: Using a Monte Carlo simulation, we examined bias resulting from censoring versus accounting for healthy live birth as a competing event in estimates of the total effect of prenatal medication use on pregnancy outcomes.

Methods: We simulated 2000 cohorts of 7500 conceptions with chronic hypertension under 12 treatment profiles. Ongoing pregnancies were indexed into the trial and randomly assigned to initiate or not initiate antihypertensives. Using time-to-event methods, we estimated absolute risks, risk differences (RD) per 100 pregnancies, and risk ratios (RR) for two outcomes, mirroring a prior trial: (i) composite fetal death or severe prenatal preeclampsia and (ii) small for gestational age (SGA) live birth. For the composite outcome, we conducted analyses where non-preeclamptic live birth was: (1) a censoring event and (2) a competing event. For SGA live birth, we conducted analyses where fetal death and non-SGA live birth were: (1) censoring events; (2) a competing event and censoring event, respectively; and (3) competing events.

Results: For the composite outcome, censoring non-preeclamptic live births overestimated the absolute risk by 42.3 to 49.1 percentage points; RD and RR estimates were biased (e.g., RD bias range -6.18 to 0.46). For SGA live birth, analyses censoring non-SGA live births (with or without fetal death as a competing event) overestimated absolute risk by 30.0 to 37.7 and 40.9 to 52.4 percentage points on average; RD and RR estimates were biased (e.g., RD bias range -7.45 to 0.79 and -9.62 to 1.81, respectively). Analyses in which healthy live births were modelled as competing events produced unbiased risks, RDs and RRs.

Conclusions: Censoring healthy live births resulted in overestimated risks as well as biased and imprecise total treatment effect estimates. Such inaccuracies about risks undermine informed patient-provider decision-making.

Background: Uterine fibroids, a common gynaecologic condition, are often underdiagnosed, potentially biasing results in epidemiologic studies due to measurement error.

Objectives: To examine how varying sensitivity in detecting uterine fibroids impacts effect estimates, using the association with hypertension onset as an example.

Methods: Three simulation studies were conducted (N = 100,000), considering true population prevalences of uterine fibroids of 5%, 20% and 60%. The first study varied detection sensitivity between 0% and 100%. The second examined differential sensitivity by symptom status (asymptomatic vs. symptomatic). The third assessed differential sensitivity by racialised groups. Specificity remained fixed at 90%, and true risk ratios (RRs) for the association with hypertension were set at 1.3 and 1.8.

Results: Decreasing sensitivity biased results towards the null, with low-sensitivity methods (e.g., self-report) showing the largest bias and high-sensitivity methods (e.g., transvaginal ultrasonography) the least bias. At low fibroid prevalence (5%), even gold-standard ascertainment introduced bias due to imperfect specificity, whereas this concern diminished at higher prevalence. Assuming a dose-response relationship between fibroids and hypertension based on symptom status, results remained biased towards the null unless sensitivity was 100% and prevalence was high (60%); bias was most pronounced at low prevalence. When only symptomatic fibroids were associated with hypertension, increasing sensitivity biased results away from the null by capturing more asymptomatic cases. Studies using low-sensitivity methods may fail to identify a true effect among Black females while identifying it among White females, potentially exacerbating disparities. Detection bias, where those with fibroids are more likely to have hypertension detected, could result in bias away from the null.

Conclusions: Underdiagnosis of uterine fibroids can bias results towards the null, particularly with self-report or modest effect estimates, potentially obscuring true effects. When only symptomatic fibroids were associated with the outcome, the bias was away from the null. Results varied by symptom status and race, highlighting the need to prioritise sensitive ascertainment methods, employ sensitivity analyses and improve reliability across diverse gynecologic conditions and health disparities.

Background: People who recently gave birth are strongly advised to wait 6 months before attempting pregnancy. Interpregnancy intervals (IPI) of ≥ 18 months are considered optimal. Current guidance is not tailored based on maternal characteristics (e.g., age).

Objectives: We evaluated whether maternal age modifies IPI-preterm birth (PTB) associations.

Methods: From a US retrospective cohort of multiparae (1997-2011), we categorised IPI: < 6, 6-11, 12-17, 18-23 (reference), 24-59 or ≥ 60 months. PTB occurred before 37 0/7 weeks' gestation. We estimated risk ratios (RR) between IPI and PTB using modified Poisson regression, adjusted for potential confounders and stratified by age at prior delivery: < 25 (n = 2484), 25-29 (n = 1626) or ≥ 30 (n = 1209) years. We conducted quantitative bias analysis to adjust for volunteer bias and dependent misclassification between IPI and gestational length (since both are calculated using the estimated start of pregnancy). We computed E-values when RR lower bounds of the 95% simulation intervals were > 1.00.

Results: Estimates were imprecise due to small numbers. However, in terms of general patterns, PTB risk was highest with < 6 months IPI in all age groups (covariate-adjusted RR point estimates ≥ 1.30). The strongest associations were observed among 25-29 years. For ≥ 30 years, PTB risk was lowest with 6-17 months IPI. After multiple bias adjustments, estimates tended to move downward, but similar patterns remained. For 25-29 years, the lower bound of the 95% simulation interval for < 6 versus 18-23 months IPI was > 1.00, with an E-value of 3.82, suggesting unmeasured confounding would need to be very strong to explain the association.

Conclusions: Estimates were imprecise. However, our study adds to growing evidence that IPI associations may be weaker among older individuals. Older individuals with shorter IPI may have lower PTB risk than those with currently recommended IPI, but more research is needed.