Living in a low socioeconomic status neighbourhood is associated with lower cumulative ongoing pregnancy rate after IVF treatment.

RESEARCH QUESTION: Does an association exist between neighbourhood socioeconomic status (SES) and the cumulative rate of ongoing pregnancies after 2.5 years of IVF treatment? DESIGN: A retrospective observational study involving 2669 couples who underwent IVF or IVF and intracytoplasmic sperm injection treatment between 2006 and 2020. Neighbourhood SES for each couple was determined based on their residential postal code. Subsequently, SES was categorized into low (p80). Multivariable binary logistic regression analyses were conducted, with the cumulative ongoing pregnancy within 2.5 years as the outcome variable. The SES category (reference category: high), female age (reference category: 32-36 years), body mass index (reference category: 23-25 kg/m2), smoking status (yes/no), number of oocytes after the first ovarian stimulation, embryos usable for transfer or cryopreservation after the first cycle, duration of subfertility before treatment and insemination type were used as covariates. RESULTS: A variation in ongoing pregnancy rates was observed among SES groups after the first fresh embryo transfer. No difference was found in the median number of IVF treatment cycles carried out. The cumulative ongoing pregnancy rates differed significantly between SES groups (low: 44%; medium: 51%; high: 56%; P < 0.001). Low neighbourhood SES was associated with significantly lower odds for achieving an ongoing pregnancy within 2.5 years (OR 0.66, 95% CI 0.52 to 0.84, P < 0.001). CONCLUSION: Low neighbourhood SES compared with high neighbourhood SES is associated with reducing odds of achieving an ongoing pregnancy within 2.5 years of IVF treatment.

Association of neighbourhood socioeconomic trajectories with preterm birth and small-for-gestational-age in the Netherlands: a nationwide population-based study.

BACKGROUND: Adverse birth outcomes have serious health consequences, not only during infancy but throughout the entire life course. Most evidence linking neighbourhood socioeconomic status (SES) to birth outcomes is based on cross-sectional SES measures, which do not reflect neighbourhoods' dynamic nature. We investigated the association between neighbourhood SES trajectories and adverse birth outcomes, i.e. preterm birth and being small-for-gestational-age (SGA), for births occurring in the Netherlands between 2003 and 2017. METHODS: We linked individual-level data from the Dutch perinatal registry to the Netherlands Institute for Social Research neighbourhood SES scores. Based on changes in their SES across four-year periods, neighbourhoods were categorised into seven trajectories. To investigate the association between neighbourhood SES trajectories and birth outcomes we used adjusted multilevel logistic regression models. FINDINGS: Data on 2 334 036 singleton births were available for analysis. Women living in stable low-SES neighbourhoods had higher odds of preterm birth (OR[95%CI]= 1·12[1·07-1·17]) and SGA (OR[95%CI]= 1·19[1·15-1·23]), compared to those in high SES areas. Higher odds of preterm birth (OR[95%CI]= 1·12[1·05-1·20]) and SGA (OR[95%CI]=1·12[1·06-1·18]) were also observed for those living in areas declining to low SES. Women living in a neighbourhood where SES improved from low to medium showed higher odds of preterm birth (OR[95%CI]= 1·09[1·02-1·18]), but not of SGA (OR[95%CI]= 1·04[0.98-1·10]). The odds of preterm or SGA birth in other areas were comparable to those seen in high SES areas. INTERPRETATION: In the Netherlands, disadvantaged neighbourhood SES trajectories were associated with higher odds of adverse birth outcomes. Longitudinal neighbourhood SES measures should also be taken into account when selecting a target population for public health interventions. FUNDING: Erasmus Initiative Smarter Choices for Better Health.

Efficient selective screening for heart failure in elderly men and women from the community: A diagnostic individual participant data meta-analysis.

Background Prevalence of undetected heart failure in older individuals is high in the community, with patients being at increased risk of morbidity and mortality due to the chronic and progressive nature of this complex syndrome. An essential, yet currently unavailable, strategy to pre-select candidates eligible for echocardiography to confirm or exclude heart failure would identify patients earlier, enable targeted interventions and prevent disease progression. The aim of this study was therefore to develop and validate such a model that can be implemented clinically. Methods and results Individual patient data from four primary care screening studies were analysed. From 1941 participants >60 years old, 462 were diagnosed with heart failure, according to criteria of the European Society of Cardiology heart failure guidelines. Prediction models were developed in each cohort followed by cross-validation, omitting each of the four cohorts in turn. The model consisted of five independent predictors; age, history of ischaemic heart disease, exercise-related shortness of breath, body mass index and a laterally displaced/broadened apex beat, with no significant interaction with sex. The c-statistic ranged from 0.70 (95% confidence interval (CI) 0.64-0.76) to 0.82 (95% CI 0.78-0.87) at cross-validation and the calibration was reasonable with Observed/Expected ratios ranging from 0.86 to 1.15. The clinical model improved with the addition of N-terminal pro B-type natriuretic peptide with the c-statistic increasing from 0.76 (95% CI 0.70-0.81) to 0.89 (95% CI 0.86-0.92) at cross-validation. Conclusion Easily obtainable patient characteristics can select older men and women from the community who are candidates for echocardiography to confirm or refute heart failure.

A generic nomogram for multinomial prediction models: theory and guidance for construction.

BACKGROUND: The use of multinomial logistic regression models is advocated for modeling the associations of covariates with three or more mutually exclusive outcome categories. As compared to a binary logistic regression analysis, the simultaneous modeling of multiple outcome categories using a multinomial model often better resembles the clinical setting, where a physician typically must distinguish between more than two possible diagnoses or outcome events for an individual patient (e.g., the differential diagnosis). A disadvantage of the multinomial logistic model is that the interpretation of its results is often complex. In particular, the calculation of predicted probabilities for the various outcomes requires a series of careful calculations. Nomograms are widely used in studies reporting binary logistic regression models to facilitate the interpretation of the results and allow the calculation of the predicted probability for individuals. METHODS AND RESULTS: In this paper we outline an approach for deriving a generic nomogram for multinomial logistic regression models and an accompanying scoring chart that can further simplify the calculation of predicted multinomial probabilities. We illustrate the use of the nomogram and scoring chart and their interpretation using a clinical example. CONCLUSIONS: The generic multinomial nomogram and scoring chart can be used irrespective of the number of outcome categories that are present.